diff --git a/.gitignore b/.gitignore
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--- a/.gitignore
+++ b/.gitignore
@@ -4,3 +4,6 @@ PROJECT.org*
.Rhistory
**/*.Rds
results/
+.Rproj.user
+main.R
+manuscript1.Rmd
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diff --git a/bibliography.bib b/bibliography.bib
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--- a/bibliography.bib
+++ b/bibliography.bib
@@ -1,9 +1,2965 @@
-@article{exampleKey9999,
- author = {{This is an example of a BibTex entry}},
- title = {You can get the BibTex entry of any article using its doi from for example https://www.bibtex.com/c/doi-to-bibtex-converter/},
- journal = {You can remove this entry once you start putting your own entries here},
- year = 9999,
- volume = 1,
- number = 1,
- pages = {1-2},
- month = {Aug}}
\ No newline at end of file
+
+\usepackage{url}
+
+@article{1,
+ author = "WHO",
+ title = "Injuries and violence",
+ url={https://www.who.int/news-room/fact-sheets/detail/injuries-and-violence},
+ language={en},
+ year = 2019,
+ month = mar
+ }
+
+
+@ARTICLE{2,
+ title = "Global, regional, and national age-sex-specific mortality for
+ 282 causes of death in 195 countries and territories, 1980-2017:
+ a systematic analysis for the Global Burden of Disease Study
+ 2017",
+ author = "{GBD 2017 Causes of Death Collaborators}",
+ abstract = "BACKGROUND: Global development goals increasingly rely on
+ country-specific estimates for benchmarking a nation's progress.
+ To meet this need, the Global Burden of Diseases, Injuries, and
+ Risk Factors Study (GBD) 2016 estimated global, regional,
+ national, and, for selected locations, subnational
+ cause-specific mortality beginning in the year 1980. Here we
+ report an update to that study, making use of newly available
+ data and improved methods. GBD 2017 provides a comprehensive
+ assessment of cause-specific mortality for 282 causes in 195
+ countries and territories from 1980 to 2017. METHODS: The causes
+ of death database is composed of vital registration (VR), verbal
+ autopsy (VA), registry, survey, police, and surveillance data.
+ GBD 2017 added ten VA studies, 127 country-years of VR data, 502
+ cancer-registry country-years, and an additional surveillance
+ country-year. Expansions of the GBD cause of death hierarchy
+ resulted in 18 additional causes estimated for GBD 2017. Newly
+ available data led to subnational estimates for five additional
+ countries-Ethiopia, Iran, New Zealand, Norway, and Russia.
+ Deaths assigned International Classification of Diseases (ICD)
+ codes for non-specific, implausible, or intermediate causes of
+ death were reassigned to underlying causes by redistribution
+ algorithms that were incorporated into uncertainty estimation.
+ We used statistical modelling tools developed for GBD, including
+ the Cause of Death Ensemble model (CODEm), to generate cause
+ fractions and cause-specific death rates for each location,
+ year, age, and sex. Instead of using UN estimates as in previous
+ versions, GBD 2017 independently estimated population size and
+ fertility rate for all locations. Years of life lost (YLLs) were
+ then calculated as the sum of each death multiplied by the
+ standard life expectancy at each age. All rates reported here
+ are age-standardised. FINDINGS: At the broadest grouping of
+ causes of death (Level 1), non-communicable diseases (NCDs)
+ comprised the greatest fraction of deaths, contributing to
+ 73·4\% (95\% uncertainty interval [UI] 72·5-74·1) of total
+ deaths in 2017, while communicable, maternal, neonatal, and
+ nutritional (CMNN) causes accounted for 18·6\% (17·9-19·6), and
+ injuries 8·0\% (7·7-8·2). Total numbers of deaths from NCD
+ causes increased from 2007 to 2017 by 22·7\% (21·5-23·9),
+ representing an additional 7·61 million (7·20-8·01) deaths
+ estimated in 2017 versus 2007. The death rate from NCDs
+ decreased globally by 7·9\% (7·0-8·8). The number of deaths for
+ CMNN causes decreased by 22·2\% (20·0-24·0) and the death rate
+ by 31·8\% (30·1-33·3). Total deaths from injuries increased by
+ 2·3\% (0·5-4·0) between 2007 and 2017, and the death rate from
+ injuries decreased by 13·7\% (12·2-15·1) to 57·9 deaths
+ (55·9-59·2) per 100 000 in 2017. Deaths from substance use
+ disorders also increased, rising from 284 000 deaths (268
+ 000-289 000) globally in 2007 to 352 000 (334 000-363 000) in
+ 2017. Between 2007 and 2017, total deaths from conflict and
+ terrorism increased by 118·0\% (88·8-148·6). A greater reduction
+ in total deaths and death rates was observed for some CMNN
+ causes among children younger than 5 years than for older
+ adults, such as a 36·4\% (32·2-40·6) reduction in deaths from
+ lower respiratory infections for children younger than 5 years
+ compared with a 33·6\% (31·2-36·1) increase in adults older than
+ 70 years. Globally, the number of deaths was greater for men
+ than for women at most ages in 2017, except at ages older than
+ 85 years. Trends in global YLLs reflect an epidemiological
+ transition, with decreases in total YLLs from enteric
+ infections, respiratory infections and tuberculosis, and
+ maternal and neonatal disorders between 1990 and 2017; these
+ were generally greater in magnitude at the lowest levels of the
+ Socio-demographic Index (SDI). At the same time, there were
+ large increases in YLLs from neoplasms and cardiovascular
+ diseases. YLL rates decreased across the five leading Level 2
+ causes in all SDI quintiles. The leading causes of YLLs in
+ 1990-neonatal disorders, lower respiratory infections, and
+ diarrhoeal diseases-were ranked second, fourth, and fifth, in
+ 2017. Meanwhile, estimated YLLs increased for ischaemic heart
+ disease (ranked first in 2017) and stroke (ranked third), even
+ though YLL rates decreased. Population growth contributed to
+ increased total deaths across the 20 leading Level 2 causes of
+ mortality between 2007 and 2017. Decreases in the cause-specific
+ mortality rate reduced the effect of population growth for all
+ but three causes: substance use disorders, neurological
+ disorders, and skin and subcutaneous diseases. INTERPRETATION:
+ Improvements in global health have been unevenly distributed
+ among populations. Deaths due to injuries, substance use
+ disorders, armed conflict and terrorism, neoplasms, and
+ cardiovascular disease are expanding threats to global health.
+ For causes of death such as lower respiratory and enteric
+ infections, more rapid progress occurred for children than for
+ the oldest adults, and there is continuing disparity in
+ mortality rates by sex across age groups. Reductions in the
+ death rate of some common diseases are themselves slowing or
+ have ceased, primarily for NCDs, and the death rate for selected
+ causes has increased in the past decade. FUNDING: Bill \&
+ Melinda Gates Foundation.",
+ journal = "Lancet",
+ publisher = "Elsevier BV",
+ volume = 392,
+ number = 10159,
+ pages = "1736--1788",
+ month = nov,
+ year = 2018,
+ copyright = "http://creativecommons.org/licenses/by/4.0/"
+}
+
+
+@article{3,
+ author = "SweTrau",
+ title = "Svenska Traumaregistret Årsrapport 2022",
+ year = 2022,
+ url={https://rcsyd.se/swetrau/wp-content/uploads/sites/10/2023/05/Årsrapport-SweTrau-2022.pdf}
+
+}
+
+"https://rcsyd.se/swetrau/wp-content/uploads/sites/10/2023/05/Årsrapport-SweTrau-2022.pdf"
+
+@ARTICLE{4,
+ title = "Measuring post-discharge socioeconomic and quality of life
+ outcomes in trauma patients: a scoping review",
+ author = "David, Siddarth Daniels and Roy, Nobhojit and Solomon, Harris
+ and Lundborg, Cecilia St{\aa}lsby and W{\"a}rnberg, Martin
+ Gerdin",
+ abstract = "PURPOSE: Trauma is a global public health challenge. Measuring
+ post-discharge socioeconomic and quality-of-life outcomes can
+ help better understand and reduce the consequences of trauma.
+ METHODS: We performed a scoping review to map the existing
+ research on post-discharge outcomes for trauma patients,
+ irrespective of the country or setting in which the study was
+ performed. The scoping review was conducted by searching six
+ databases - MEDLINE, EMBASE, the Cochrane Library, Global Index
+ Medicus, BASE, and Web of Science - to identify all articles
+ that report post-discharge socioeconomic or quality of life
+ outcomes in trauma patients from 2009 to 2018. RESULTS: Seven
+ hundred fifty-eight articles were included in this study,
+ extracting 958 outcomes. Most studies (82\%) were from
+ high-income countries (HICs). More studies from low- and
+ middle-income countries (LMICs) were cross-sectional (71\%)
+ compared with HIC settings (46\%). There was a wide variety of
+ different definitions, interpretations, and measurements used by
+ various articles for similar outcomes. Quality of life, return
+ to work, social support, cost, and participation were the main
+ outcomes studied in post-discharge trauma patients. CONCLUSIONS:
+ The wide range of outcomes and outcome measures reported across
+ different types of injuries and settings. This variability can
+ be a barrier when comparing across different types of injuries
+ and settings. Post-discharge trauma studies should move towards
+ building evidence based on standardized measurement of outcomes.",
+ journal = "J. Patient Rep. Outcomes",
+ publisher = "Springer Science and Business Media LLC",
+ volume = 5,
+ number = 1,
+ pages = "68",
+ month = aug,
+ year = 2021,
+ keywords = "Injury; Post-discharge; Scoping review; Socioeconomic outcomes;
+ Trauma",
+ copyright = "https://creativecommons.org/licenses/by/4.0",
+ language = "en"
+}
+
+@ARTICLE{5,
+ title = "Audit filters for improving processes of care and clinical
+ outcomes in trauma systems",
+ author = "Evans, Christopher and Howes, Daniel and Pickett, William and
+ Dagnone, Luigi",
+ abstract = "BACKGROUND: Traumatic injuries represent a considerable public
+ health burden with significant personal and societal costs. The
+ care of the severely injured patient in a trauma system
+ progresses along a continuum that includes numerous
+ interventions being provided by a multidisciplinary group of
+ healthcare personnel. Despite the recent emphasis on quality of
+ care in medicine, there has been little research to direct
+ trauma clinicians and administrators on how optimally to monitor
+ and improve upon the quality of care delivered within a trauma
+ system. Audit filters are one mechanism for improving quality of
+ care and are defined as specific clinical processes or outcomes
+ of care that, when they occur, represent unfavorable deviations
+ from an established norm and which prompt review and feedback.
+ Although audit filters are widely utilized for performance
+ improvement in trauma systems they have not been subjected to
+ systematic review of their effectiveness. OBJECTIVES: To
+ determine the effectiveness of using audit filters for improving
+ processes of care and clinical outcomes in trauma systems.
+ SEARCH STRATEGY: Our search strategy included an electronic
+ search of the Cochrane Injuries Group Specialized Register, the
+ Cochrane EPOC Group Specialized Register, CENTRAL (The Cochrane
+ Library 2008, Issue 4), MEDLINE, PubMed, EMBASE, CINAHL, and ISI
+ Web of Science: (SCI-EXPANDED and CPCI-S). We handsearched the
+ Journal of Trauma, Injury, Annals of Emergency Medicine,
+ Academic Emergency Medicine, and Injury Prevention. We searched
+ two clinical trial registries: 1) The World Health Organization
+ International Clinical Trials Registry Platform and, 2) Clinical
+ Trials.gov. We also contacted content experts for further
+ articles. The most recent electronic search was completed in
+ December 2008 and the handsearch was completed up to February
+ 2009. SELECTION CRITERIA: We searched for randomized controlled
+ trials, controlled clinical trials, controlled before-and-after
+ studies, and interrupted time series studies that used audit
+ filters as an intervention for improving processes of care,
+ morbidity, or mortality for severely injured patients. DATA
+ COLLECTION AND ANALYSIS: Two authors independently screened the
+ search results, applied inclusion criteria, and extracted data.
+ MAIN RESULTS: There were no studies identified that met the
+ inclusion criteria for this review. AUTHORS' CONCLUSIONS: We
+ were unable to identify any studies of sufficient methodological
+ quality to draw conclusions regarding the effectiveness of audit
+ filters as a performance improvement intervention in trauma
+ systems. Future research using rigorous study designs should
+ focus on the relative effectiveness of audit filters in
+ comparison to alternative quality improvement strategies at
+ improving processes of care, functional outcomes, and mortality
+ for injured patients.",
+ journal = "Cochrane Database Syst. Rev.",
+ publisher = "Wiley",
+ number = 4,
+ pages = "CD007590",
+ month = oct,
+ year = 2009,
+ language = "en"
+}
+
+
+
+ @ARTICLE{6,
+ title = "The global burden of injury: incidence, mortality,
+ disability-adjusted life years and time trends from the Global
+ Burden of Disease study 2013",
+ author = "Haagsma, Juanita A and Graetz, Nicholas and Bolliger, Ian and
+ Naghavi, Mohsen and Higashi, Hideki and Mullany, Erin C and
+ Abera, Semaw Ferede and Abraham, Jerry Puthenpurakal and Adofo,
+ Koranteng and Alsharif, Ubai and Ameh, Emmanuel A and Ammar,
+ Walid and Antonio, Carl Abelardo T and Barrero, Lope H and
+ Bekele, Tolesa and Bose, Dipan and Brazinova, Alexandra and
+ Catal{\'a}-L{\'o}pez, Ferr{\'a}n and Dandona, Lalit and Dandona,
+ Rakhi and Dargan, Paul I and De Leo, Diego and Degenhardt,
+ Louisa and Derrett, Sarah and Dharmaratne, Samath D and
+ Driscoll, Tim R and Duan, Leilei and Petrovich Ermakov, Sergey
+ and Farzadfar, Farshad and Feigin, Valery L and Franklin,
+ Richard C and Gabbe, Belinda and Gosselin, Richard A and
+ Hafezi-Nejad, Nima and Hamadeh, Randah Ribhi and Hijar, Martha
+ and Hu, Guoqing and Jayaraman, Sudha P and Jiang, Guohong and
+ Khader, Yousef Saleh and Khan, Ejaz Ahmad and Krishnaswami,
+ Sanjay and Kulkarni, Chanda and Lecky, Fiona E and Leung, Ricky
+ and Lunevicius, Raimundas and Lyons, Ronan Anthony and Majdan,
+ Marek and Mason-Jones, Amanda J and Matzopoulos, Richard and
+ Meaney, Peter A and Mekonnen, Wubegzier and Miller, Ted R and
+ Mock, Charles N and Norman, Rosana E and Orozco, Ricardo and
+ Polinder, Suzanne and Pourmalek, Farshad and Rahimi-Movaghar,
+ Vafa and Refaat, Amany and Rojas-Rueda, David and Roy, Nobhojit
+ and Schwebel, David C and Shaheen, Amira and Shahraz, Saeid and
+ Skirbekk, Vegard and S{\o}reide, Kjetil and Soshnikov, Sergey
+ and Stein, Dan J and Sykes, Bryan L and Tabb, Karen M and
+ Temesgen, Awoke Misganaw and Tenkorang, Eric Yeboah and Theadom,
+ Alice M and Tran, Bach Xuan and Vasankari, Tommi J and Vavilala,
+ Monica S and Vlassov, Vasiliy Victorovich and Woldeyohannes,
+ Solomon Meseret and Yip, Paul and Yonemoto, Naohiro and Younis,
+ Mustafa Z and Yu, Chuanhua and Murray, Christopher J L and Vos,
+ Theo",
+ abstract = "BACKGROUND: The Global Burden of Diseases (GBD), Injuries, and
+ Risk Factors study used the disability-adjusted life year (DALY)
+ to quantify the burden of diseases, injuries, and risk factors.
+ This paper provides an overview of injury estimates from the
+ 2013 update of GBD, with detailed information on incidence,
+ mortality, DALYs and rates of change from 1990 to 2013 for 26
+ causes of injury, globally, by region and by country. METHODS:
+ Injury mortality was estimated using the extensive GBD mortality
+ database, corrections for ill-defined cause of death and the
+ cause of death ensemble modelling tool. Morbidity estimation was
+ based on inpatient and outpatient data sets, 26 cause-of-injury
+ and 47 nature-of-injury categories, and seven follow-up studies
+ with patient-reported long-term outcome measures. RESULTS: In
+ 2013, 973 million (uncertainty interval (UI) 942 to 993) people
+ sustained injuries that warranted some type of healthcare and
+ 4.8 million (UI 4.5 to 5.1) people died from injuries. Between
+ 1990 and 2013 the global age-standardised injury DALY rate
+ decreased by 31\% (UI 26\% to 35\%). The rate of decline in DALY
+ rates was significant for 22 cause-of-injury categories,
+ including all the major injuries. CONCLUSIONS: Injuries continue
+ to be an important cause of morbidity and mortality in the
+ developed and developing world. The decline in rates for almost
+ all injuries is so prominent that it warrants a general
+ statement that the world is becoming a safer place to live in.
+ However, the patterns vary widely by cause, age, sex, region and
+ time and there are still large improvements that need to be
+ made.",
+ journal = "Inj. Prev.",
+ publisher = "BMJ",
+ volume = 22,
+ number = 1,
+ pages = "3--18",
+ month = feb,
+ year = 2016,
+ language = "en"
+}
+
+
+
+@ARTICLE{7,
+ title = "Trauma-the forgotten pandemic?",
+ author = "Rossiter, Nigel D",
+ abstract = "Global annual deaths from Trauma are greater than any other
+ single cause in the global working population, and, more than
+ all contagious diseases added together including COVID-19. The
+ number of people injured, either temporarily or permanently, is
+ greater than any other medical condition. This problem affects
+ Low and Middle Income Countries (LMICs) disproportionately. The
+ numbers are so great as to cause ``zone out'' and present a
+ human rights issue. This is a particular issue as Trauma
+ presently receives less than 1\% of global healthcare funding.
+ This article will highlight and discuss many of the issues and
+ raise some uncomfortable arguments showing that improvement is
+ needed, necessary and achievable.",
+ journal = "Int. Orthop.",
+ publisher = "Springer Science and Business Media LLC",
+ volume = 46,
+ number = 1,
+ pages = "3--11",
+ month = jan,
+ year = 2022,
+ keywords = "Advocacy; Global; Improvement; LMIC; Trauma",
+ language = "en"
+}
+
+
+@ARTICLE{8,
+ title = "Evaluating the quality of medical care. 1966",
+ author = "Donabedian, Avedis",
+ journal = "Milbank Q.",
+ publisher = "Wiley",
+ volume = 83,
+ number = 4,
+ pages = "691--729",
+ year = 2005,
+ copyright = "http://onlinelibrary.wiley.com/termsAndConditions\#vor",
+ language = "en"
+}
+
+@ARTICLE{9,
+ title = "Preventable mortality evaluation in the {ICU}",
+ author = "Dijkema, L Marjon and Dieperink, Willem and van Meurs, Matijs
+ and Zijlstra, Jan G",
+ abstract = "Mortality is the most widely measured outcome parameter.
+ Improvement of this outcome parameter in critical care is
+ nowadays expected not to come from new technologies or
+ treatment, but from delivering the right care at the right
+ moment in a safe way. The measurement of mortality as an outcome
+ parameter confronts us with a problem in providing follow-up to
+ the results. Especially when proven structure and process
+ interventions are applied already, the cause of a suboptimal
+ performance cannot be deduced easily. One possibility is to
+ evaluate the causes of death and to judge preventability. In
+ this article we explore the opportunities and difficulties of a
+ tool to evaluate preventable mortality in the ICU.",
+ journal = "Crit. Care",
+ publisher = "Springer Nature",
+ volume = 16,
+ number = 2,
+ pages = "309",
+ month = dec,
+ year = 2012,
+ language = "en"
+}
+
+
+@ARTICLE{10,
+ title = "Identification of preventable trauma deaths: confounded
+ inquiries?",
+ author = "Wilson, D S and McElligott, J and Fielding, L P",
+ abstract = "The published evaluation of methods for identifying preventable
+ trauma deaths contains many unstudied confounding factors. To
+ investigate the reliability of methods for identifying such
+ preventable deaths, we compared three consensus systems using
+ separate five-member general review panels assessing 20
+ non-central nervous system fatalities: panel A, independent
+ judgments; panel B, discussion of all cases preceding individual
+ judgments; and panel C, independent judgments followed by
+ discussion and equivocal case reassignment. The Kappa concordance
+ index was low for all methods (method A, 0.20; methods B and C,
+ 0.40). Of the 11 deaths judged preventable by at least one panel,
+ only one death was judged preventable by all three panels.
+ Consensus agreement (four of five assessors) was 20\% for panel
+ A, 45\% for panel B, and 10\% for panel C (difference between
+ panels B and C, p less than 0.03). In panel C, discussion
+ affected the rate of equivocal case designation from 30\% to 5\%.
+ Thus different consensus methods yielded different results. We
+ conclude that individual case review can be severely flawed and
+ therefore should not be used to measure institutional quality of
+ patient care. We recommend that assessment of institutional
+ performance should be based on objective evaluation methods,
+ which require the study of patient population outcomes, rather
+ than on subjective methods in which individual cases are
+ reviewed.",
+ journal = "J. Trauma",
+ volume = 32,
+ number = 1,
+ pages = "45--51",
+ month = jan,
+ year = 1992,
+ language = "en"
+}
+
+
+
+
+@ARTICLE{11,
+ title = "Comparison of Injury Severity Score, New Injury Severity Score,
+ Revised Trauma Score and trauma and Injury Severity Score for
+ Mortality Prediction in Elderly Trauma Patients",
+ author = "Javali, Rameshbabu Homanna and {Krishnamoorthy} and Patil,
+ Akkamahadevi and Srinivasarangan, Madhu and {Suraj} and
+ {Sriharsha}",
+ abstract = "OBJECTIVES: This study tests the accuracy of the Injury Severity
+ Score (ISS), New Injury Severity Score (NISS), Revised Trauma
+ Score (RTS) and Trauma and Injury Severity Score (TRISS) in
+ prediction of mortality in cases of geriatric trauma. DESIGN:
+ Prospective observational study. MATERIALS AND METHODS: This was
+ a prospective observational study on two hundred elderly trauma
+ patients who were admitted to JSS Hospital, Mysuru over a
+ consecutive period of 18 months between December 2016 to May
+ 2018. On the day of admission, data were collected from each
+ patient to compute the ISS, NISS, RTS, and TRISS. RESULTS: Mean
+ age of patients was 66.35 years. Most common mechanism of injury
+ was road traffic accident (94.0\%) with mortality of 17.0\%. The
+ predictive accuracies of the ISS, NISS, RTS and the TRISS were
+ compared using receiver operator characteristic (ROC) curves for
+ the prediction of mortality. Best cutoff points for predicting
+ mortality in elderly trauma patient using TRISS system was a
+ score of 91.6 (sensitivity 97\%, specificity of 88\%, area under
+ ROC curve 0.972), similarly cutoff point under the NISS was
+ score of 17(91\%, 93\%, 0.970); for ISS best cutoff point was at
+ 15(91\%, 89\%, 0.963) and for RTS it was 7.108(97\%,80\%,0.947).
+ There were statistical differences among ISS, NISS, RTS and
+ TRISS in terms of area under the ROC curve (p <0.0001).
+ CONCLUSION: TRISS was the strongest predictor of mortality in
+ elderly trauma patients when compared to the ISS, NISS and RTS.
+ HOW TO CITE THIS ARTICLE: Javali RH, Krishnamoorthy et al.
+ Comparison of Injury Severity Score, New Injury Severity Score,
+ Revised Trauma Score and Trauma and Injury Severity Score for
+ Mortality Prediction in Elderly Trauma Patients. Indian J of
+ Crit Care Med 2019;23(2):73-77.",
+ journal = "Indian J. Crit. Care Med.",
+ publisher = "Jaypee Brothers Medical Publishing",
+ volume = 23,
+ number = 2,
+ pages = "73--77",
+ month = feb,
+ year = 2019,
+ keywords = "Elderly; Injury severity score; Mortality; New injury severity
+ score; Revised trauma score; Trauma; Trauma and injury severity
+ score",
+ language = "en"
+}
+
+
+@ARTICLE{12,
+ title = "Patient and process factors associated with opportunities for
+ improvement in trauma care: a registry-based study",
+ author = "Albaaj, Hussein and Attergrim, Jonatan and Str{\"o}mmer, Lovisa
+ and Brattstr{\"o}m, Olof and Jacobsson, Martin and Wihlke,
+ Gunilla and V{\"a}sterbo, Liselott and Joneborg, Elias and Gerdin
+ W{\"a}rnberg, Martin",
+ abstract = "BACKGROUND: Trauma is one of the leading causes of morbidity and
+ mortality worldwide. Morbidity and mortality review of selected
+ patient cases is used to improve the quality of trauma care by
+ identifying opportunities for improvement (OFI). The aim of this
+ study was to assess how patient and process factors are
+ associated with OFI in trauma care. METHODS: We conducted a
+ registry-based study using all patients between 2017 and 2021
+ from the Karolinska University Hospital who had been reviewed
+ regarding the presence of OFI as defined by a morbidity and
+ mortality conference. We used bi- and multivariable logistic
+ regression to assess the associations between the following
+ patient and process factors and OFI: age, sex, respiratory rate,
+ systolic blood pressure, Glasgow Coma Scale (GCS), Injury
+ Severity Score (ISS), survival at 30 days, highest hospital care
+ level, arrival on working hours, arrival on weekends, intubation
+ status and time to first computed tomography (CT). RESULTS: OFI
+ was identified in 300 (5.8\%) out of 5182 patients. Age, missing
+ Glasgow Coma Scale, time to first CT, highest hospital care level
+ and ISS were statistically significantly associated with OFI.
+ CONCLUSION: Several patient and process factors were found to be
+ associated with OFI, indicating that patients with moderate to
+ severe trauma and those with delays to first CT are at the
+ highest odds of OFI.",
+ journal = "Scand. J. Trauma Resusc. Emerg. Med.",
+ volume = 31,
+ number = 1,
+ pages = "87",
+ month = nov,
+ year = 2023,
+ keywords = "Acute surgery; Opportunities for improvement; Trauma; Trauma care",
+ language = "en"
+}
+
+
+@ARTICLE{13,
+ title = "National assessment of opportunities for improvement in
+ preventable trauma deaths: A mixed-methods study",
+ author = "Kwon, Junsik and Lee, Myeonggyun and Jung, Kyoungwon",
+ abstract = "Trauma is a significant public health issue worldwide,
+ particularly affecting economically active age groups. Quality
+ management of trauma care at the national level is crucial to
+ improve outcomes of major trauma. In Korea, a biennial nationwide
+ survey on preventable trauma death rate is conducted. Based on
+ the survey results, we analyzed opportunities for improving the
+ trauma treatment process. Expert panels reviewed records of 8282
+ and 8482 trauma-related deaths in 2017 and 2019, respectively,
+ identifying 258 and 160 cases in each year as preventable deaths.
+ Opportunities for improvement were categorized into prehospital,
+ interhospital, and hospital stages. Hemorrhage was the primary
+ cause of death, followed by sepsis/multiorgan failure and central
+ nervous system injury. Delayed hemostatic procedures and
+ transfusions were common areas for improvement in hospital stage.
+ Interhospital transfers experienced significant delays in arrival
+ time. This study emphasizes the need to enhance trauma care by
+ refining treatment techniques, centralizing patients in
+ specialized facilities, and implementing comprehensive reviews
+ and performance improvements throughout the patient transfer
+ system. The findings offer valuable insights for addressing
+ trauma care improvement from both clinical and systemic
+ perspectives.",
+ journal = "Healthcare (Basel)",
+ volume = 11,
+ number = 16,
+ month = aug,
+ year = 2023,
+ keywords = "mortality; patient transfer; quality improvement; treatment
+ outcome; wounds and injuries",
+ language = "en"
+}
+
+
+@ARTICLE{14,
+ title = "Opportunities for improvement in the management of patients who
+ die from haemorrhage after trauma",
+ author = "O'Reilly, D and Mahendran, K and West, A and Shirley, P and
+ Walsh, M and Tai, N",
+ abstract = "BACKGROUND: Bleeding is the leading cause of preventable death
+ after injury. This retrospective study aimed to characterize
+ opportunities for performance improvement (OPIs) identified in
+ patients who died from bleeding and were considered by the
+ quality improvement system of a major trauma centre. METHODS:
+ All trauma deaths in 2006-2010 were discussed at the trauma
+ morbidity and mortality meeting. Deaths from haemorrhage were
+ identified and subjected to qualitative and quantitative
+ evaluation. OPIs were identified and remedial action was taken.
+ RESULTS: During the study interval there were 7511 trauma team
+ activations; 423 patients died. Haemorrhage was the second most
+ common cause of death, in 112 patients, and made a substantial
+ contribution to death in a further 15. For 84 of these 127
+ patients, a total of 150 OPIs were identified. Most arose in the
+ emergency department, but involved personnel from many
+ departments. Problems with decision-making were more common than
+ errors in technical skill. OPIs frequently involved the decision
+ between surgery, radiology and further investigation. Delayed
+ and inappropriate surgery occurred even when investigation and
+ diagnosis were appropriate. The mortality rate among patients
+ presenting in shock fell significantly over the study interval
+ (P < 0·026). CONCLUSION: Problems with judgement are more common
+ than those of skill. Death from traumatic haemorrhage is
+ associated with identifiable, remediable failures in care. The
+ implementation of a systematic trauma quality improvement system
+ was associated with a fall in the mortality rate among patients
+ presenting in shock.",
+ journal = "Br. J. Surg.",
+ publisher = "Oxford University Press (OUP)",
+ volume = 100,
+ number = 6,
+ pages = "749--755",
+ month = may,
+ year = 2013,
+ copyright = "https://academic.oup.com/journals/pages/open\_access/funder\_policies/chorus/standard\_publication\_model",
+ language = "en"
+}
+
+
+@ARTICLE{15,
+ title = "Leveraging telemedicine for quality improvement video review of
+ critical {ICU} events: A novel multidisciplinary form of
+ education",
+ author = "Gold, Andrew K and Huffenberger, Ann and Lane-Fall, Meghan and
+ Pascual Lopez, Jose L and Rock, Kristen C",
+ abstract = "The objectives of this study were to codify the events
+ triggering bedside recording and to report the types of
+ performance issues identified that were then used to inform
+ dedicated ICU quality improvement efforts. DESIGN: This is a
+ retrospective descriptive analysis of a video review program
+ conducted at a single institution from July 2016 to November
+ 2019. SETTING: Surgical and Trauma ICU at a single urban
+ academic quaternary care center. PATIENTS: All patients admitted
+ to the surgical and trauma ICU between July 2016 and November
+ 2019 were eligible for the study as all ICU beds in our health
+ system institutions are equipped with closed circuit video/audio
+ monitoring. Through an institutional review board approved
+ program, any event triggering the immediate bedside presence of
+ a provider in the ICU is routinely recorded at the discretion of
+ the care team or tele-intensivist. INTERVENTIONS: A database of
+ these events was created over a 3-year period, and cases were
+ reviewed for content, quality improvement, and educational
+ opportunities. Select recordings were analyzed and shared at
+ multidisciplinary/multiprofessional video review sessions.
+ MEASUREMENTS AND MAIN RESULTS: There were 286 critical events
+ video recorded and reviewed in the ICUs between July 2016 and
+ November 2019. The most commonly recorded events included:
+ cardiopulmonary arrests (n = 75), intubations (n = 71), and
+ acute clinical decompensation triggered by nonreassuring vital
+ signs (n = 57) or arrhythmias (n = 13). Of these recordings, 59
+ were shared at video review conferences, where quality of care
+ was assessed and thematic opportunities for improvement were
+ characterized. Recurrent quality improvement themes that were
+ identified included adherence to protocols, the importance of
+ teamwork and closed-loop communication, clearly designated team
+ leadership, and the use of universal precautions. CONCLUSIONS:
+ Video review in the ICU is feasible and presents valuable
+ opportunities for quality improvement and educational
+ discussions.",
+ journal = "Crit. Care Explor.",
+ publisher = "Ovid Technologies (Wolters Kluwer Health)",
+ volume = 3,
+ number = 9,
+ pages = "e0536",
+ month = sep,
+ year = 2021,
+ keywords = "intensive care; medical education; patient safety; quality
+ improvement; telemedicine",
+ language = "en"
+}
+
+
+@ARTICLE{16,
+ title = "Enhancing patient safety in the trauma/surgical intensive care
+ unit",
+ author = "Stahl, Kenneth and Palileo, Albert and Schulman, Carl I and
+ Wilson, Katherine and Augenstein, Jeffrey and Kiffin, Chauniqua
+ and McKenney, Mark",
+ abstract = "BACKGROUND: Preventable deaths due to errors in trauma patients
+ with otherwise survivable injuries account for up to 10\% of
+ fatalities in Level I trauma centers, 50\% of these errors occur
+ in the intensive care unit (ICU). The root cause of 67\% of the
+ Joint Commission sentinel events is communication errors. The
+ objective is (1) to study how critical information degrades and
+ how it is lost over 24 hours and (2) to determine whether a
+ structured checklist for ICU handoffs prevents information loss.
+ METHODS: Prospective cohort study of trauma and surgical ICU
+ teams observed with and without use of the checklist. An
+ observational period (control group) was followed by a didactic
+ session on the science and use of a checklist (study group),
+ which was used for patient management and handoffs. Information
+ was tracked for a 24-hour period and all handoffs. Comparisons
+ use chi or Fisher's exact test and a p value <0.05 was defined
+ as significant. RESULTS: Three hundred and thirty-two patient
+ ICU days were observed (119 control, 213 study) and 689 patient
+ care items (303 control, 386 study) were followed. Seventy-five
+ (10.9\%) items were lost over 24 hours; 61 of 303 (20.1\%)
+ without checklist and 14 of 386 (3.6\%) with checklist (p <
+ 0.0001). Critical laboratory values and test results were the
+ most frequent lost items (36.1\% control vs. 4.5\% study p <
+ 0.0001). Six of 75 (8.1\%) items were correctly ordered but not
+ carried out by ICU nursing staff--all caught and corrected with
+ checklist use. CONCLUSION: Critical information is degraded over
+ 24 hours in the ICU. A structured checklist significantly
+ reduces patient errors due to lost information and communication
+ lapses between trauma ICU team members at handoffs of care.",
+ journal = "J. Trauma",
+ publisher = "Ovid Technologies (Wolters Kluwer Health)",
+ volume = 67,
+ number = 3,
+ pages = "430--3; discussion 433--5",
+ month = sep,
+ year = 2009,
+ language = "en"
+}
+
+@ARTICLE{17,
+ title = "Trauma center levels defined",
+ author = "Trauma Center Association of America",
+ url={https://www.traumacenters.org/page/TraumaCenterLevels#Goals},
+ year = 2014
+}
+
+
+@ARTICLE{18,
+ title = "Traumamanual",
+ author = "Karolinska Universitetssjukhuset Solna",
+ url = {https://traumarummet.files.wordpress.com/2020/09/traumamanualen-2020.pdf},
+ year = 2020
+}
+
+@ARTICLE{19,
+ title = "R studio",
+ author = "Karolinska Universitetssjukhuset Solna",
+ year = 2020,
+ url={https://www.r-project.org}
+}
+
+@ARTICLE{20,
+ title = "The new trauma score ({NTS)}: a modification of the revised
+ trauma score for better trauma mortality prediction",
+ author = "Jeong, Jin Hee and Park, Yong Joo and Kim, Dong Hoon and Kim,
+ Tae Yun and Kang, Changwoo and Lee, Soo Hoon and Lee, Sang Bong
+ and Kim, Seong Chun and Lim, Daesung",
+ journal = "BMC Surg.",
+ publisher = "Springer Science and Business Media LLC",
+ volume = 17,
+ number = 1,
+ month = dec,
+ year = 2017,
+ language = "en"
+}
+
+@ARTICLE{22,
+ title = "Prolonged {ICU} stay and its association with 1-year trauma
+ mortality: An analysis of 19,000 American patients",
+ author = "Chaudhary, Muhammad Ali and Schoenfeld, Andrew J and Koehlmoos,
+ Tracey P and Cooper, Zara and Haider, Adil H",
+ abstract = "INTRODUCTION: Prior research on patients with traumatic injury
+ suggests high in-hospital survivability. However, little is
+ known about their long-term outcomes, especially in the context
+ of a prolonged ICU length-of-stay (LOS). We sought to determine
+ the association between prolonged ICU-LOS and 1-year survival in
+ trauma patients. METHODS: TRICARE claims data (2011-2015) were
+ queried for trauma patients with an Injury Severity Score > 9.
+ Risk-adjusted Cox models were used to determine the influence of
+ prolonged ICU LOS on 1-year mortality. RESULTS: Of 19,155
+ patients included, 40\% were admitted to the ICU. The overall
+ 1-year mortality was 3.9\% and 4.7\% in patients with ICU LOS >9
+ days. In the multivariable model older age (55-64 vs. 18-24
+ years) (HR: 47.8, CI:20.8-109.9), prior comorbidities (>1 vs. 0)
+ (HR: 2.6, CI: 2.1-3.2), discharge disposition (transfer vs
+ discharge) (HR: 2.3 CI: 1.7-3.1) and ICU-LOS (>7 vs. 1 days)
+ (HR:2.6, CI:1.7-4.0) were associated with 1-year mortality.
+ CONCLUSION: Prolonged ICU-LOS is a risk factor for 1-year
+ mortality in trauma patients. But an overall high survival
+ (>96\%) reinforces the justification for such use of the ICU in
+ trauma patients when clinically necessary.",
+ journal = "Am. J. Surg.",
+ publisher = "Elsevier BV",
+ volume = 218,
+ number = 1,
+ pages = "21--26",
+ month = jul,
+ year = 2019,
+ keywords = "1-Year mortality; Critical care; Prolonged ICU stay; TRICARE;
+ Trauma",
+ language = "en"
+}
+
+@ARTICLE{23,
+ title = "The heterogeneity of prolonged {ICU} hospitalisations",
+ author = "Viglianti, Elizabeth Marie and Kruser, Jacqueline M and
+ Iwashyna, Theodore",
+ journal = "Thorax",
+ publisher = "BMJ",
+ volume = 74,
+ number = 11,
+ pages = "1015--1017",
+ month = nov,
+ year = 2019,
+ keywords = "Clinical Epidemiology; Critical Care",
+ language = "en"
+}
+
+@ARTICLE{24,
+ title = "Persistent critical illness: baseline characteristics, intensive
+ care course, and cause of death",
+ author = "Darvall, Jai N and Boonstra, Tristan and Norman, Jen and Murphy,
+ Donal and Bailey, Michael and Iwashyna, Theodore J and Bagshaw,
+ Sean M and Bellomo, Rinaldo",
+ abstract = "OBJECTIVES: Persistent critical illness (PerCI) is associated
+ with high mortality and discharge to institutional care. Little
+ is known about factors involved in its progression,
+ complications and cause of death. We aimed to identify such
+ factors and the time when the original illness was no longer the
+ reason for intensive care unit (ICU) stay. DESIGN: Retrospective
+ matched case-control study using an accepted PerCI definition (>
+ 10 days in ICU). SETTING: Single-centre tertiary metropolitan
+ ICU. PARTICIPANTS: All adult patients admitted during a 2-year
+ period were eligible, matched on diagnostic code, gender, age
+ and risk of death. MAIN RESULTS: Seventy-two patients staying >
+ 10 days (PerCI cases) were matched to 72 control patients. The
+ original illness was no longer a cause for continued ICU stay
+ after a median of 10 days (interquartile range [IQR], 7-16)
+ versus 2 days (IQR, 0-3); P < 0.001. Patients with PerCI were
+ more likely to develop new sepsis (52.8\% v 23.6\%; P < 0.001),
+ delirium (37.5\% v 9.7\%; P < 0.001), ICU-acquired weakness
+ (15.3\% v 0\%, P = 0.001), and to be discharged to chronic care
+ or rehabilitation (37.5\% v 16.7\%; P < 0.005). Death resulting
+ from sepsis with multi-organ failure occurred in 16.7\% v 8.3\%
+ of control patients (P = 0.13), and one-third of patients with
+ PerCI were not mechanically ventilated on Day 10. CONCLUSION:
+ PerCI likely results from complications acquired after ICU
+ admission and mostly unrelated to the original illness; by Day
+ 10, the original illness does not appear to be its cause, and
+ new sepsis appears an important association.",
+ journal = "Crit. Care Resusc.",
+ publisher = "Elsevier BV",
+ volume = 21,
+ number = 2,
+ pages = "110--118",
+ month = jun,
+ year = 2019,
+ copyright = "http://creativecommons.org/licenses/by-nc-nd/4.0/",
+ language = "en"
+}
+
+@ARTICLE{25,
+ title = "Late organ failures in patients with prolonged intensive care
+ unit stays",
+ author = "Viglianti, Elizabeth M and Kramer, Rachel and Admon, Andrew J
+ and Sjoding, Michael W and Hodgson, Carol L and Bellomo, Rinaldo
+ and Iwashyna, Theodore J",
+ abstract = "The purpose of this study was to characterize the organ failures
+ that develop among patients with prolonged ICU stays, defined as
+ those who spent a minimum of 14 days in an ICU.We
+ retrospectively studied a cohort of consecutive patients from a
+ university hospital who were in an ICU for a minimum of 14 days
+ during 2014--2016. We calculated daily Sequential Organ Failure
+ Assessment (SOFA) scores from admission to ICU day 14. The
+ primary outcome was the number of new late organ failures,
+ defined as occurring on ICU day 4 through 14.In a retrospective
+ cohort of 3777 consecutive patients in six ICUs, 50 patients had
+ prolonged ICU stays. Of those 50, new cardiovascular failure
+ occurred in 24 (62\%) on day 4 or later; persistent mechanical
+ ventilation was present in only 28 (56\%).Strategies aiming to
+ reduce the development of new late organ failures may be a novel
+ target for preventing persistent critical illness.",
+ journal = "J. Crit. Care",
+ publisher = "Elsevier BV",
+ volume = 46,
+ pages = "55--57",
+ month = aug,
+ year = 2018
+}
+
+@ARTICLE{26,
+ title = "Five-year mortality and morbidity impact of prolonged versus
+ brief {ICU} stay: a propensity score matched cohort study",
+ author = "Hermans, Greet and Van Aerde, Nathalie and Meersseman, Philippe
+ and Van Mechelen, Helena and Debaveye, Yves and Wilmer,
+ Alexander and Gunst, Jan and Casaer, Michael Paul and Dubois,
+ Jasperina and Wouters, Pieter and Gosselink, Rik and Van den
+ Berghe, Greet",
+ abstract = "PURPOSE: Long-term outcomes of critical illness may be affected
+ by duration of critical illness and intensive care. We aimed to
+ investigate differences in mortality and morbidity after short
+ (<8 days) and prolonged ($\geq$8 days) intensive care unit (ICU)
+ stay. METHODS: Former EPaNIC-trial patients were included in
+ this preplanned prospective cohort, 5-year follow-up study.
+ Mortality was assessed in all. For morbidity analyses, all
+ long-stay and-for feasibility-a random sample (30\%) of
+ short-stay survivors were contacted. Primary outcomes were total
+ and post-28-day 5-year mortality. Secondary outcomes comprised
+ handgrip strength (HGF, \%pred), 6-minute-walking distance
+ (6MWD, \%pred) and SF-36 Physical Function score (PF SF-36).
+ One-to-one propensity-score matching of short-stay and long-stay
+ patients was performed for nutritional strategy, demographics,
+ comorbidities, illness severity and admission diagnosis.
+ Multivariable regression analyses were performed to explore ICU
+ factors possibly explaining any post-ICU observed outcome
+ differences. RESULTS: After matching, total and post-28-day
+ 5-year mortality were higher for long-stayers (48.2\% (95\%CI:
+ 43.9\% to 52.6\%) and 40.8\% (95\%CI: 36.4\% to 45.1\%)) versus
+ short-stayers (36.2\% (95\%CI: 32.4\% to 40.0\%) and 29.7\%
+ (95\%CI: 26.0\% to 33.5\%), p<0.001). ICU risk factors comprised
+ hypoglycaemia, use of corticosteroids, neuromuscular blocking
+ agents, benzodiazepines, mechanical ventilation, new dialysis
+ and the occurrence of new infection, whereas clonidine could be
+ protective. Among 276 long-stay and 398 short-stay 5-year
+ survivors, HGF, 6MWD and PF SF-36 were significantly lower in
+ long-stayers (matched subset HGF: 83\% (95\%CI: 60\% to 100\%)
+ versus 87\% (95\%CI: 73\% to 103\%), p=0.020; 6MWD: 85\%
+ (95\%CI: 69\% to 101\%) versus 94\% (95\%CI: 76\% to 105\%),
+ p=0.005; PF SF-36: 65 (95\%CI: 35 to 90) versus 75 (95\%CI: 55
+ to 90), p=0.002). CONCLUSION: Longer duration of intensive care
+ is associated with excess 5-year mortality and morbidity,
+ partially explained by potentially modifiable ICU factors. TRAIL
+ REGISTRATION NUMBER: NCT00512122.",
+ journal = "Thorax",
+ publisher = "BMJ",
+ volume = 74,
+ number = 11,
+ pages = "1037--1045",
+ month = nov,
+ year = 2019,
+ keywords = "critical illness; long-term outcomes; mortality; post-icu",
+ language = "en"
+}
+
+@ARTICLE{27,
+ title = "Predicting prolonged stay in the {ICU} attributable to bleeding
+ in patients offered plasma transfusion",
+ author = "Ngufor, Che and Murphree, Dennis and Upadhyaya, Sudhi and Madde,
+ Nageswar and Pathak, Jyotishman and Carter, Rickey and Kor, Daryl",
+ abstract = "In blood transfusion studies, plasma transfusion (PPT) and
+ bleeding are known to be associated with risk of prolonged ICU
+ length of stay (ICU-LOS). However, as patients can show
+ significant heterogeneity in response to a treatment, there might
+ exists subgroups with differential effects. The existence and
+ characteristics of these subpopulations in blood transfusion has
+ not been well-studied. Further, the impact of bleeding in
+ patients offered PPT on prolonged ICU-LOS is not known. This
+ study presents a causal and predictive framework to examine these
+ problems. The two-step approach first estimates the effect of
+ bleeding in PPT patients on prolonged ICU-LOS and then estimates
+ risks of bleeding and prolonged ICU-LOS. The framework integrates
+ a classification model for risks prediction and a regression
+ model to predict actual LOS. Results showed that the effect of
+ bleeding in PPT patients significantly increases risk of
+ prolonged ICU-LOS (55\%, p=0.00) while no bleeding significantly
+ reduces ICU-LOS (4\%, p=0.046).",
+ journal = "AMIA Annu. Symp. Proc.",
+ volume = 2016,
+ pages = "954--963",
+ year = 2016,
+ keywords = "Blood transfusion; bleeding; classification; machine learning;
+ perioperative",
+ language = "en"
+}
+
+@ARTICLE{28,
+ title = "Are {ICU} length of stay predictions worthwhile?",
+ author = "Kramer, Andrew A",
+ journal = "Crit. Care Med.",
+ publisher = "Ovid Technologies (Wolters Kluwer Health)",
+ volume = 45,
+ number = 2,
+ pages = "379--380",
+ month = feb,
+ year = 2017,
+ language = "en"
+}
+
+@ARTICLE{29,
+ title = "The influence of gender on {ICU} admittance",
+ author = "Larsson, Emma and Zettersten, Erik and J{\"a}derling, Gabriella
+ and Ohlsson, Anna and Bell, Max",
+ abstract = "BACKGROUND: We assume that critically ill patients are admitted
+ to an intensive care unit (ICU) based on their illness severity
+ coupled with their co-morbidities. Patient attributes such as
+ religion, nationality, socioeconomic class or gender are not
+ relevant in this setting. We aimed to explore the association of
+ patient gender with admission to the ICU amongst hospital
+ physicians working in Sweden. METHODS: Primary outcome assessed
+ was gender bias among respondents. Two different versions of an
+ online survey, with eight patient cases, were sent to physicians
+ in Sweden who within their field of specialty meet patients that
+ could be eligible for intensive care. The versions of the survey
+ were identical except that the patient gender in each case was
+ exchanged between the two surveys. Depending on the respondent's
+ birthday (odd or even number) they were directed to one of the
+ two surveys. At the end of each case the respondent was asked to
+ answer if they thought that the patient needed ICU care, yes or
+ no. The respondents were not told in advance about the design of
+ the survey. The respondents were also asked to state their age,
+ sex, field of specialty, size of hospital and title. RESULTS: Of
+ 1426 respondents, 679 and 747 answered survey 1 and 2,
+ respectively. Overall, there were no significant differences in
+ willingness to admit in between cases describing a man or woman
+ in the physician responses. DISCUSSION: Anesthesiology/intensive
+ care physicians more often choose to admit patients to the ICU
+ compared to all other specialties. Female physicians tended to
+ be more willing to admit patients, regardless of patient gender,
+ than their male counterparts. CONCLUSIONS: Using a survey, with
+ eight cases differing only with regards to the gender of the
+ patient, we demonstrate an absence of a gender bias among
+ Swedish hospital physicians.",
+ journal = "Scand. J. Trauma Resusc. Emerg. Med.",
+ publisher = "Springer Science and Business Media LLC",
+ volume = 23,
+ number = 1,
+ pages = "108",
+ month = dec,
+ year = 2015,
+ language = "en"
+}
+
+@ARTICLE{30,
+ title = "Is there an association between female gender and outcome in
+ severe trauma? A multi-center analysis in the Netherlands",
+ author = "Pape, M and Giannak{\'o}poulos, G F and Zuidema, W P and de
+ Lange-Klerk, E S M and Toor, E J and Edwards, M J R and
+ Verhofstad, M H J and Tromp, T N and van Lieshout, E M M and
+ Bloemers, F W and Geeraedts, L M G",
+ abstract = "INTRODUCTION: Little evidence suggest that female gender is
+ associated with a lower risk of mortality in severely injured
+ patients, especially in premenopausal women. Previous clinical
+ studies have shown contradictory results regarding protective
+ effects of gender on outcome after severe trauma. The objective
+ of this study was to determine the association between gender
+ and outcome (mortality and Intensive Care Unit (ICU) admission)
+ among severely injured patients in the Netherlands. METHODS: A
+ retrospective multicentre study was performed including all
+ polytrauma patients (Injury Severity Score (ISS) $\geq$16)
+ admitted to the ED of three level 1 trauma centres, between
+ January 1st, 2006 and December 31st, 2014. Data on age, gender,
+ mechanism of injury, ISS, Abbreviated Injury Scale (AIS),
+ prehospital intubation, Revised Trauma Score (RTS), systolic
+ blood pressure (SBP) and Glasgow Coma Scale (GCS) upon admission
+ at the Emergency Department was collected from three Regional
+ Trauma Registries. To determine whether gender was an
+ independent predictor of mortality and ICU admission, logistic
+ regression analysis was performed. RESULTS: Among 6865 trauma
+ patients, male patients had a significantly higher ISS compared
+ to female patients (26.3 $\pm$ 10.2 vs 25.3 $\pm$ 9.7, P = <
+ 0.0001). Blunt trauma was significantly more common in the
+ female group (95.2\% vs 92.3\%, P = < 0.0001). Males aged 16- to
+ 44-years had a significant higher in-hospital mortality rate
+ (10.4\% vs 13.4\%, P = 0.046). ICU admission rate was
+ significantly lower in females (49.3\% vs 54.5\%, P = < 0.0001).
+ In the overall group, logistic regression did not show gender as
+ an independent predictor for in-hospital mortality (OR 1.020
+ (95\% CI 0.865-1.204), P = 0.811) or mortality within 24 h (OR
+ 1.049 (95\% CI 0.829-1.327), P = 0.693). However, male gender
+ was associated with an increased likelihood for ICU admission in
+ the overall group (OR 1.205 (95\% CI 1.046-1.388), P = 0.010).
+ CONCLUSION: The current study shows that in this population of
+ severely injured patients, female sex is associated with a lower
+ in-hospital mortality rate among those aged 16- to 44-years.
+ Furthermore, female sex is independently associated with an
+ overall decreased likelihood for ICU admission. More research is
+ needed to examine the physiologic background of this protective
+ effect of female sex in severe trauma.",
+ journal = "Scand. J. Trauma Resusc. Emerg. Med.",
+ publisher = "Springer Science and Business Media LLC",
+ volume = 27,
+ number = 1,
+ pages = "16",
+ month = feb,
+ year = 2019,
+ keywords = "Female gender; Gender dimorphism; ICU admission; Mortality;
+ Severe injury; Trauma",
+ language = "en"
+}
+
+@ARTICLE{31,
+ title = "Impact of gender on treatment and outcome of {ICU} patients",
+ author = "Reinikainen, M and Niskanen, M and Uusaro, A and Ruokonen, E",
+ abstract = "BACKGROUND: Gender modifies immunologic responses caused by
+ severe trauma or critical illness. The aim of this study was to
+ investigate the impact of gender on hospital mortality, length
+ of intensive care unit (ICU) stay, and intensity of care of
+ patients treated in ICUs. METHODS: Data on 24,341 ICU patients
+ were collected from a national database. We measured severity of
+ illness with Acute Physiology and Chronic Health Evaluation II
+ (APACHE II) scores and intensity of care with Therapeutic
+ Intervention Scoring System (TISS) scores. We used logistic
+ regression analysis to test the independent effect of gender on
+ hospital mortality. We compared the lengths of ICU stay and the
+ intensity of care of men and women. RESULTS: Male gender was
+ associated with increased hospital mortality among postoperative
+ ICU patients [adjusted odds ratio 1.33 (95\% confidence interval
+ 1.12-1.58, P = 0.001)] but not among medical patients [adjusted
+ odds ratio 1.02 (95\% confidence interval 0.92-1.13, P = 0.74)].
+ Male gender was associated with an increased risk of death
+ particularly in the oldest age group (75 years or older) and
+ among the patients with relatively low APACHE II scores (<16).
+ Mean length of ICU stay was 3.2 days for men and 2.6 days for
+ women (P < 0.001). Male patients comprised 61.7\% of the study
+ population but consumed 66.0\% of days in intensive care.
+ CONCLUSION: Male gender contributes to poor outcome in
+ postoperative ICU patients. Approximately two-thirds of ICU
+ resources are consumed by male patients.",
+ journal = "Acta Anaesthesiol. Scand.",
+ publisher = "Wiley",
+ volume = 49,
+ number = 7,
+ pages = "984--990",
+ month = aug,
+ year = 2005,
+ copyright = "http://onlinelibrary.wiley.com/termsAndConditions\#vor",
+ language = "en"
+}
+
+@ARTICLE{32,
+ title = "Preinjury {ASA} score as an independent predictor of readmission
+ after major traumatic injury",
+ author = "Tran, Alexandre and Mai, Trinh and El-Haddad, Julie and Lampron,
+ Jacinthe and Yelle, Jean-Denis and Pagliarello, Giuseppe and
+ Matar, Maher",
+ journal = "Trauma Surg. Acute Care Open",
+ publisher = "BMJ",
+ volume = 2,
+ number = 1,
+ pages = "e000128",
+ month = nov,
+ year = 2017,
+ language = "en"
+}
+
+@ARTICLE{33,
+ title = "Pre-injury {ASA} physical status classification is an
+ independent predictor of mortality after trauma",
+ author = "Skaga, Nils O and Eken, Torsten and S{\o}vik, Signe and Jones, J
+ Mary and Steen, Petter A",
+ abstract = "BACKGROUND: The ability of an organism to withstand trauma is
+ determined by the injury per se and inherent properties of the
+ organism at the time of injury. We analyzed whether pre-injury
+ morbidity scored on a four-level ordinal scale according to the
+ American Society of Anesthesiologists Physical Status (ASA-PS)
+ classification system predicts mortality after trauma.
+ MATERIALS: From a total of 3,773 prospectively collected
+ patients (years 2000-2004), 3,728 patients were included. Main
+ outcome measure was mortality 30 days after injury. The effect
+ of pre-injury ASA-PS on mortality was assessed using linear
+ logistic regression analysis, controlling for Revised Trauma
+ Score (RTS), Injury Severity Score (ISS), and age. RESULTS:
+ Mortality increased with increasing pre-injury ASA-PS, age, and
+ ISS, and with decreasing RTS. Unadjusted mortality rates were
+ 5.7\% in ASA-PS 1, 12.3\% in ASA-PS 2, and 26.4\% in ASA-PS 3-4.
+ This increasing mortality trend across pre-injury ASA-PS group
+ was evident in nearly all categories of ISS, RTS, and age. Odds
+ ratio for death was 1.76 (95\% CI, 1.14-2.72) for pre-injury
+ ASA-PS 2, and 2.25 (95\% CI, 1.36-3.71) for ASA-PS 3-4 compared
+ with for ASA-PS 1 and adjusted for ISS, RTS, and age. There were
+ no interaction effects between pre-injury ASA-PS and the other
+ variables. CONCLUSIONS: Pre-injury ASA-PS score was an
+ independent predictor of mortality after trauma, also after
+ adjusting for the major variables in the traditional TRISS
+ (Trauma and Injury Severity Score) formula. Including pre-injury
+ ASA-PS score might improve the predictive power of a survival
+ prediction model without complicating it.",
+ journal = "J. Trauma",
+ publisher = "Ovid Technologies (Wolters Kluwer Health)",
+ volume = 63,
+ number = 5,
+ pages = "972--978",
+ month = nov,
+ year = 2007,
+ language = "en"
+}
+
+@ARTICLE{34,
+ title = "Classification of comorbidity in trauma: the reliability of
+ pre-injury {ASA} physical status classification",
+ author = "Ringdal, Kjetil G and Skaga, Nils Oddvar and Steen, Petter
+ Andreas and Hestnes, Morten and Laake, Petter and Jones, J Mary
+ and Lossius, Hans Morten",
+ abstract = "BACKGROUND: Pre-injury comorbidities can influence the outcomes
+ of severely injured patients. Pre-injury comorbidity status,
+ graded according to the American Society of Anesthesiologists
+ Physical Status (ASA-PS) classification system, is an
+ independent predictor of survival in trauma patients and is
+ recommended as a comorbidity score in the Utstein Trauma
+ Template for Uniform Reporting of Data. Little is known about
+ the reliability of pre-injury ASA-PS scores. The objective of
+ this study was to examine whether the pre-injury ASA-PS system
+ was a reliable scale for grading comorbidity in trauma patients.
+ METHODS: Nineteen Norwegian trauma registry coders were invited
+ to participate in a reliability study in which 50 real but
+ anonymised patient medical records were distributed. Reliability
+ was analysed using quadratic weighted kappa ($\kappa$(w))
+ analysis with 95\% CI as the primary outcome measure and
+ unweighted kappa ($\kappa$) analysis, which included unknown
+ values, as a secondary outcome measure. RESULTS: Fifteen of the
+ invitees responded to the invitation, and ten participated. We
+ found moderate ($\kappa$(w)=0.77 [95\% CI: 0.64-0.87]) to
+ substantial ($\kappa$(w)=0.95 [95\% CI: 0.89-0.99])
+ rater-against-reference standard reliability using $\kappa$(w)
+ and fair ($\kappa$=0.46 [95\% CI: 0.29-0.64]) to substantial
+ ($\kappa$=0.83 [95\% CI: 0.68-0.94]) reliability using $\kappa$.
+ The inter-rater reliability ranged from moderate
+ ($\kappa$(w)=0.66 [95\% CI: 0.45-0.81]) to substantial
+ ($\kappa$(w)=0.96 [95\% CI: 0.88-1.00]) for $\kappa$(w) and from
+ slight ($\kappa$=0.36 [95\% CI: 0.21-0.54]) to moderate
+ ($\kappa$=0.75 [95\% CI: 0.62-0.89]) for $\kappa$. CONCLUSIONS:
+ The rater-against-reference standard reliability varied from
+ moderate to substantial for the primary outcome measure and from
+ fair to substantial for the secondary outcome measure. The study
+ findings indicate that the pre-injury ASA-PS scale is a reliable
+ score for classifying comorbidity in trauma patients.",
+ journal = "Injury",
+ publisher = "Elsevier BV",
+ volume = 44,
+ number = 1,
+ pages = "29--35",
+ month = jan,
+ year = 2013,
+ language = "en"
+}
+
+@ARTICLE{35,
+ title = "On-call work: To sleep or not to sleep? It depends",
+ author = "Ferguson, Sally A and Paterson, Jessica L and Hall, Sarah J and
+ Jay, Sarah M and Aisbett, Brad",
+ abstract = "On-call working time arrangements are increasingly common,
+ involve work only in the event of an unpredictable incident and
+ exist primarily outside of standard hours. Like other
+ non-standard working time arrangements, on-call work disrupts
+ sleep and can therefore have negative effects on health, safety
+ and performance. Unlike other non-standard working time
+ arrangements, on-call work often allows sleep opportunities
+ between calls. Any sleep obtained during on-call periods will be
+ beneficial for waking performance. However, there is evidence
+ that sleep while on call may be of substantially reduced
+ restorative value because of the expectation of receiving the
+ call and apprehension about missing the call. In turn, waking
+ from sleep to respond to a call may be associated with temporary
+ increases in performance impairment. This is dependent on
+ characteristics of both the preceding sleep, the tasks required
+ upon waking and the availability and utility of any
+ countermeasures to support the transition from sleep to wake. In
+ this paper, we critically evaluate the evidence both for and
+ against sleeping during on-call periods and conclude that some
+ sleep, even if it is of reduced quality and broken by repeated
+ calls, is a good strategy. We also note, however, that
+ organisations utilising on-call working time arrangements need
+ to systematically manage the likelihood that on-call sleep can
+ be associated with temporary performance impairments upon
+ waking. Given that the majority of work in this area has been
+ laboratory-based, there is a significant need for field-based
+ investigations of the magnitude of sleep inertia, in addition to
+ the utility of sleep inertia countermeasures. Field studies
+ should include working with subject matter experts to identify
+ the real-world impacts of changes in performance associated with
+ sleeping, or not sleeping, whilst on call.",
+ journal = "Chronobiol. Int.",
+ publisher = "Informa UK Limited",
+ volume = 33,
+ number = 6,
+ pages = "678--684",
+ month = apr,
+ year = 2016,
+ keywords = "On-call; non-standard hours; performance; sleep; standby work",
+ language = "en"
+}
+
+@ARTICLE{36,
+ title = "Gravedad en pacientes traum{\'a}ticos ingresados en {UCI}.
+ Modelos fisiol{\'o}gicos y anat{\'o}micos",
+ author = "Servi{\'a}, L and Badia, M and Montserrat, N and Trujillano, J",
+ abstract = "INTRODUCTION: The goals of this project were to compare both the
+ anatomic and physiologic severity scores in trauma patients
+ admitted to intensive care unit (ICU), and to elaborate mixed
+ statistical models to improve the precision of the scores.
+ METHODS: A prospective study of cohorts. The combined
+ medical/surgical ICU in a secondary university hospital. Seven
+ hundred and eighty trauma patients admitted to ICU older than 16
+ years of age. Anatomic models (ISS and NISS) were compared and
+ combined with physiological models (T-RTS, APACHE II [APII], and
+ MPM II). The probability of death was calculated following the
+ TRISS method. The discrimination was assessed using ROC curves
+ (ABC [CI 95\%]), and the calibration using the Hosmer-Lemeshoẃs
+ H test. The mixed models were elaborated with the tree
+ classification method type Chi Square Automatic Interaction
+ Detection. RESULTS: A 14\% global mortality was recorded. The
+ physiological models presented the best discrimination values
+ (APII of 0.87 [0.84-0.90]). All models were affected by bad
+ calibration (P<.01). The best mixed model resulted from the
+ combination of APII and ISS (0.88 [0.83-0.90]). This model was
+ able to differentiate between a 7.5\% mortality for elderly
+ patients with pathological antecedents and a 25\% mortality in
+ patients presenting traumatic brain injury, from a pool of
+ patients with APII values ranging from 10 to 17 and an ISS
+ threshold of 22. CONCLUSIONS: The physiological models perform
+ better than the anatomical models in traumatic patients admitted
+ to the ICU. Patients with low scores in the physiological models
+ require an anatomic analysis of the injuries to determine their
+ severity.",
+ journal = "Med. Intensiva (Engl. Ed.)",
+ publisher = "Elsevier BV",
+ volume = 43,
+ number = 1,
+ pages = "26--34",
+ month = jan,
+ year = 2019,
+ keywords = "Modelos de gravedad; Mortality prediction; Predicci{\'o}n de
+ mortalidad; Scoring; Trauma",
+ language = "en"
+}
+
+@ARTICLE{37,
+ title = "New injury severity score ({NISS}) outperforms injury severity
+ score ({ISS}) in the evaluation of severe blunt trauma patients",
+ author = "Li, Hui and Ma, Yue-Feng",
+ abstract = "PURPOSE: The injury severity score (ISS) and new injury severity
+ score (NISS) have been widely used in trauma evaluation.
+ However, which scoring system is better in trauma outcome
+ prediction is still disputed. The purpose of this study is to
+ evaluate the value of the two scoring systems in predicting
+ trauma outcomes, including mortality, intensive care unit (ICU)
+ admission and ICU length of stay. METHODS: The data were
+ collected retrospectively from three hospitals in Zhejiang
+ province, China. The comparisons of NISS and ISS in predicting
+ outcomes were performed by using receiver operator
+ characteristic (ROC) curves and Hosmer-Lemeshow statistics.
+ RESULTS: A total of 1825 blunt trauma patients were enrolled in
+ our study. Finally, 1243 patients were admitted to ICU, and 215
+ patients died before discharge. The ISS and NISS were equivalent
+ in predicting mortality (area under ORC curve [AUC]: 0.886 vs.
+ 0.887, p = 0.9113). But for the patients with ISS $\geq$25, NISS
+ showed better performance in predicting mortality. NISS was also
+ significantly better than ISS in predicting ICU admission and
+ prolonged ICU length of stay. CONCLUSION: NISS outperforms ISS
+ in predicting the outcomes for severe blunt trauma and can be an
+ essential supplement of ISS. Considering the convenience of NISS
+ in calculation, it is advantageous to promote NISS in China's
+ primary hospitals.",
+ journal = "Chin. J. Traumatol.",
+ publisher = "Elsevier BV",
+ volume = 24,
+ number = 5,
+ pages = "261--265",
+ month = sep,
+ year = 2021,
+ keywords = "Injury severity score; Intensive care units; Mortality; New
+ injury severity score",
+ copyright = "http://creativecommons.org/licenses/by-nc-nd/4.0/",
+ language = "en"
+}
+
+@UNPUBLISHED{38,
+ title = "Predicting opportunities for improvement in trauma using machine
+ learning",
+ author = "Attergrim, Jonatan and Szolnoky, Kelvin and Str{\"o}mmer, Lovisa
+ and Brattstr{\"o}m, Olof and Whilke, Gunilla and Jacobsson,
+ Martin and W{\"a}rnberg, Martin Gerdin",
+ abstract = "1Abstract1.1RationaleThe multidisciplinary mortality and
+ morbidity conference is the core of programs that aim to improve
+ the quality of trauma care and is used to identify and address
+ opportunities for improvement based on reviewing patient cases.
+ Current systems rely on audit filters for review selection, a
+ process that is hampered by high frequencies of false
+ positives.1.2ObjectivesTo develop, validate, and compare the
+ performance of different machine learning models for predicting
+ opportunities for improvement.1.3MethodsWe conducted a registry
+ based study using all patients from the Karolinska university
+ hospital that had been reviewed regarding the presence of
+ opportunity for improvement, a binary consensus decision from the
+ mortality and morbidity conference. We developed eight binary
+ classification models using 45 predictors. Training used an
+ 80\%-20\% train-test split and 1000 resamples without replacement
+ estimated confidence intervals. Performance (sensitivity,
+ specificity, integrated calibration index, Area under the
+ receiver operating characteristics curve) was also compared to
+ current audit filters.1.4Measurements and Main ResultsThe dataset
+ included 6310 patients where opportunities for improvement were
+ present among 431 (7\%) patients. The audit filters (Area under
+ the receiver operating characteristics curve: 0.624) was
+ outperformed by all machine learning models. The best performing
+ model was LightGBM (Area under the receiver operating
+ characteristics curve: 0.789).1.5ConclusionsMachine learning
+ models outperform the currently used audit filters and could
+ prove to be valuable additions in the screening for opportunities
+ for improvement. More research is needed on how to increase model
+ performance and how to incorporate these models into trauma
+ quality improvement programs.",
+ journal = "bioRxiv",
+ month = jan,
+ year = 2023
+}
+
+@ARTICLE{39,
+ title = "{ASA} score as a predictor of 30-day perioperative readmission
+ in patients with orthopaedic trauma injuries: an {NSQIP}
+ analysis",
+ author = "Sathiyakumar, Vasanth and Molina, Cesar S and Thakore, Rachel V
+ and Obremskey, William T and Sethi, Manish K",
+ abstract = "OBJECTIVE: Our purpose was to identify the impact of the
+ physical status of the American Society of Anesthesiologists
+ (ASA) on the 30-day readmission of patients receiving operative
+ management of orthopaedic fractures using the National Surgical
+ Quality Improvement Program (NSQIP) database. METHODS: We
+ analyzed all patients with orthopaedic trauma injuries in the
+ American College of Surgeons NSQIP database from 2005 to 2011. A
+ total of 8761 patients representing 91 orthopaedic trauma
+ procedures were identified and included in analysis after
+ selection. Logistic regressions were conducted to identify the
+ predictive ability of ASA on the likelihood of readmission for
+ patients in each anatomic category (upper extremity,
+ pelvis/acetabulum, lower extremity) and the combined study
+ population. RESULTS: The ASA physical status proved the
+ strongest predictor of 30-day readmission for the selected
+ orthopaedic trauma procedures. After controlling for age,
+ gender, race, and medical comorbidities that were shown to be
+ significant independent risk factors for readmission, ASA score
+ continued to have a significant association on 30-day
+ readmissions in the combined population (odds ratio = 1.45, 95\%
+ confidence interval = 1.13-1.88, P = 0.001). For the combined
+ analysis, compared with patients with an ASA score of 1,
+ patients with an ASA score of 2 were 1.04 times as likely to
+ have a readmission (P = 0.001), patients with an ASA score of 3
+ were 3.77 times as likely to have a readmission (P = 0.001), and
+ patients with an ASA score of 4 were 13.7 times as likely to
+ have a readmission (P = 0.001). CONCLUSIONS: ASA classification
+ is an indicator for variance in readmission for patients
+ receiving operative treatment of orthopaedic fractures. Given
+ that ASA classification is a universally collected data point,
+ this method can be used in almost any hospital system and for
+ any operative service. This model may be used to more accurately
+ predict a patient's postoperative course and the expected risk
+ for readmission, such that hospitals can target these
+ ``at-risk'' individuals and reduce 30-day readmissions. LEVEL OF
+ EVIDENCE: Prognostic level II. See Instructions for authors for
+ a complete description of levels of evidence.",
+ journal = "J. Orthop. Trauma",
+ publisher = "Ovid Technologies (Wolters Kluwer Health)",
+ volume = 29,
+ number = 3,
+ pages = "e127--32",
+ month = mar,
+ year = 2015,
+ language = "en"
+}
+
+@ARTICLE{40,
+ title = "Patient variables which may predict length of stay and hospital
+ costs in elderly patients with hip fracture",
+ author = "Garcia, Anna E and Bonnaig, J V and Yoneda, Zachary T and
+ Richards, Justin E and Ehrenfeld, Jesse M and Obremskey, William
+ T and Jahangir, A Alex and Sethi, Manish K",
+ abstract = "OBJECTIVES: To investigate what factors contribute to increased
+ length of stay (LOS) and increased costs in treatment of elderly
+ patients with hip fractures. DESIGN: Retrospective chart review.
+ SETTING: All patients who presented to a large tertiary care
+ center between January 2000 and December 31, 2009. PARTICIPANTS:
+ Charts for all patients older than 60 years who presented with
+ isolated low-energy hip fractures were reviewed. Of the 719
+ patients identified, 660 were included. INTERVENTION: Patients
+ who underwent operative fixation or hemiarthroplasty secondary
+ to hip fracture were identified using a search of Current
+ Procedural Terminology (CPT) codes search. MAIN OUTCOME
+ MEASUREMENTS: Gender, height, weight, body mass index, length of
+ procedure, American Society of Anesthesiologists (ASA)
+ classification, and medical comorbidities were gathered and
+ compared with LOS and direct daily inpatient hospital cost.
+ RESULTS: No correlation existed between body mass index or
+ specific comorbidities and LOS, but ASA classification was a
+ predictor. For each ASA increase of 1, average LOS increased
+ 2.053 days (P < 0.001). Given total daily cost to the hospital
+ for these patients was $4530, each increase in ASA
+ classification translated to an increase of $9300. CONCLUSIONS:
+ ASA classification proved useful in estimating LOS and cost for
+ patients undergoing operative fixation of hip fractures. Because
+ ASA classification and cost are universally collected, this
+ method can be employed in almost any hospital. This highlights a
+ role for ASA classification in preoperative estimation of the
+ elderly patient's cost and a potential advantage for
+ incorporating patient factors in the development of tiered
+ reimbursement models. LEVEL OF EVIDENCE: Prognostic Level II.
+ See Instructions for Authors for a complete description of
+ levels of evidence.",
+ journal = "J. Orthop. Trauma",
+ publisher = "Ovid Technologies (Wolters Kluwer Health)",
+ volume = 26,
+ number = 11,
+ pages = "620--623",
+ month = nov,
+ year = 2012,
+ language = "en"
+}
+
+
+@ARTICLE{41,
+ title = "Predictive value of {SAPS} {II} and {APACHE} {II} scoring
+ systems for patient outcome in a medical intensive care unit",
+ author = "Godinjak, Amina and Iglica, Amer and Rama, Admir and Tan{\v
+ c}ica, Ira and Jusufovi{\'c}, Selma and Ajanovi{\'c}, Anes and
+ Kukuljac, Adis",
+ abstract = "OBJECTIVE: The aim is to determine SAPS II and APACHE II scores
+ in medical intensive care unit (MICU) patients, to compare them
+ for prediction of patient outcome, and to compare with actual
+ hospital mortality rates for different subgroups of patients.
+ METHODS: One hundred and seventy-four patients were included in
+ this analysis over a oneyear period in the MICU, Clinical
+ Center, University of Sarajevo. The following patient data were
+ obtained: demographics, admission diagnosis, SAPS II, APACHE II
+ scores and final outcome. RESULTS: Out of 174 patients, 70
+ patients (40.2\%) died. Mean SAPS II and APACHE II scores in all
+ patients were 48.4$\pm$17.0 and 21.6$\pm$10.3 respectively, and
+ they were significantly different between survivors and
+ non-survivors. SAPS II >50.5 and APACHE II >27.5 can predict the
+ risk of mortality in these patients. There was no statistically
+ significant difference in the clinical values of SAPS II vs
+ APACHE II (p=0.501). A statistically significant positive
+ correlation was established between the values of SAPS II and
+ APACHE II (r=0.708; p=0.001). Patients with an admission
+ diagnosis of sepsis/septic shock had the highest values of both
+ SAPS II and APACHE II scores, and also the highest hospital
+ mortality rate of 55.1\%. CONCLUSION: Both APACHE II and SAPS II
+ had an excellent ability to discriminate between survivors and
+ non-survivors. There was no significant difference in the
+ clinical values of SAPS II and APACHE II. A positive correlation
+ was established between them. Sepsis/septic shock patients had
+ the highest predicted and observed hospital mortality rate.",
+ journal = "Acta Med. Acad.",
+ publisher = "Academy of Sciences and Arts of Bosnia and Herzegovina",
+ volume = 45,
+ number = 2,
+ pages = "97--103",
+ month = nov,
+ year = 2016,
+ keywords = "APACHE II; Medical intensive care unit; SAPS II",
+ language = "en"
+}
+
+@article{42,
+ author = "World Health Organization",
+ title = "Improving the quality of health services - tools and resources",
+ year = 2018,
+ url={https://iris.who.int/bitstream/handle/10665/310944/9789241515085-eng.pdf?sequence=1}
+
+}
+
+@ARTICLE{43,
+ title = "The injury severity score: a method for describing patients with
+ multiple injuries and evaluating emergency care",
+ author = "Baker, S P and O'Neill, B and Haddon, Jr, W and Long, W B",
+ journal = "J. Trauma",
+ volume = 14,
+ number = 3,
+ pages = "187--196",
+ month = mar,
+ year = 1974,
+ language = "en"
+}
+
+@ARTICLE{44,
+ title = "A revision of the Trauma Score",
+ author = "Champion, H R and Sacco, W J and Copes, W S and Gann, D S and
+ Gennarelli, T A and Flanagan, M E",
+ abstract = "The Trauma Score (TS) has been revised. The revision includes
+ Glasgow Coma Scale (GCS), systolic blood pressure (SBP), and
+ respiratory rate (RR) and excludes capillary refill and
+ respiratory expansion, which were difficult to assess in the
+ field. Two versions of the revised score have been developed,
+ one for triage (T-RTS) and another for use in outcome
+ evaluations and to control for injury severity (RTS). T-RTS, the
+ sum of coded values of GCS, SBP, and RR, demonstrated increased
+ sensitivity and some loss in specificity when compared with a
+ triage criterion based on TS and GCS values. T-RTS correctly
+ identified more than 97\% of nonsurvivors as requiring trauma
+ center care. The T-RTS triage criterion does not require summing
+ of the coded values and is more easily implemented than the TS
+ criterion. RTS is a weighted sum of coded variable values. The
+ RTS demonstrated substantially improved reliability in outcome
+ predictions compared to the TS. The RTS also yielded more
+ accurate outcome predictions for patients with serious head
+ injuries than the TS.",
+ journal = "J. Trauma",
+ publisher = "Ovid Technologies (Wolters Kluwer Health)",
+ volume = 29,
+ number = 5,
+ pages = "623--629",
+ month = may,
+ year = 1989,
+ language = "en"
+}
+
+@ARTICLE{45,
+ title = "Importance of events per independent variable in proportional
+ hazards analysis. I. Background, goals, and general strategy",
+ author = "Concato, J and Peduzzi, P and Holford, T R and Feinstein, A R",
+ abstract = "Multivariable methods of analysis can yield problematic results
+ if methodological guidelines and mathematical assumptions are
+ ignored. A problem arising from a too-small ratio of events per
+ variable (EPV) can affect the accuracy and precision of
+ regression coefficients and their tests of statistical
+ significance. The problem occurs when a proportional hazards
+ analysis contains too few ``failure'' events (e.g., deaths) in
+ relation to the number of included independent variables. In the
+ current research, the impact of EPV was assessed for results of
+ proportional hazards analysis done with Monte Carlo simulations
+ in an empirical data set of 673 subjects enrolled in a
+ multicenter trial of coronary artery bypass surgery. The
+ research is presented in two parts: Part I describes the data
+ set and strategy used for the analyses, including the Monte
+ Carlo simulation studies done to determine and compare the
+ impact of various values of EPV in proportional hazards
+ analytical results. Part II compares the output of regression
+ models obtained from the simulations, and discusses the
+ implication of the findings.",
+ journal = "J. Clin. Epidemiol.",
+ publisher = "Elsevier BV",
+ volume = 48,
+ number = 12,
+ pages = "1495--1501",
+ month = dec,
+ year = 1995,
+ copyright = "http://creativecommons.org/licenses/by-nc-nd/4.0/",
+ language = "en"
+}
+
+@ARTICLE{46,
+ title = "Importance of events per independent variable in proportional
+ hazards regression analysis. {II}. Accuracy and precision of
+ regression estimates",
+ author = "Peduzzi, P and Concato, J and Feinstein, A R and Holford, T R",
+ abstract = "The analytical effect of the number of events per variable (EPV)
+ in a proportional hazards regression analysis was evaluated
+ using Monte Carlo simulation techniques for data from a
+ randomized trial containing 673 patients and 252 deaths, in
+ which seven predictor variables had an original significance
+ level of p < 0.10. The 252 deaths and 7 variables correspond to
+ 36 events per variable analyzed in the full data set. Five
+ hundred simulated analyses were conducted for these seven
+ variables at EPVs of 2, 5, 10, 15, 20, and 25. For each
+ simulation, a random exponential survival time was generated for
+ each of the 673 patients, and the simulated results were
+ compared with their original counterparts. As EPV decreased, the
+ regression coefficients became more biased relative to the true
+ value; the 90\% confidence limits about the simulated values did
+ not have a coverage of 90\% for the original value; large sample
+ properties did not hold for variance estimates from the
+ proportional hazards model, and the Z statistics used to test
+ the significance of the regression coefficients lost validity
+ under the null hypothesis. Although a single boundary level for
+ avoiding problems is not easy to choose, the value of EPV = 10
+ seems most prudent. Below this value for EPV, the results of
+ proportional hazards regression analyses should be interpreted
+ with caution because the statistical model may not be valid.",
+ journal = "J. Clin. Epidemiol.",
+ publisher = "Elsevier BV",
+ volume = 48,
+ number = 12,
+ pages = "1503--1510",
+ month = dec,
+ year = 1995,
+ language = "en"
+}
+
+@MISC{noauthor_undated-pr,
+ howpublished = "\url{https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/36806064/https://pubmed-ncbi-nlm-nih-gov.proxy.kib.ki.se/36806064}",
+ note = "Accessed: 2024-4-16"
+}
+
+@ARTICLE{47,
+ title = "Facilitators and barriers impacting in-hospital Trauma Quality
+ Improvement Program ({TQIP}) implementation across country
+ income levels: a scoping review",
+ author = "Kapanadze, George and Berg, Johanna and Sun, Yue and Gerdin
+ W{\"a}rnberg, Martin",
+ abstract = "OBJECTIVE: Trauma is a leading cause of mortality and morbidity
+ globally, disproportionately affecting low/middle-income
+ countries (LMICs). Understanding the factors determining
+ implementation success for in-hospital Trauma Quality
+ Improvement Programs (TQIPs) is critical to reducing the global
+ trauma burden. We synthesised topical literature to identify key
+ facilitators and barriers to in-hospital TQIP implementation
+ across country income levels. DESIGN: Scoping review. DATA
+ SOURCES: PubMed, Web of Science and Global Index Medicus
+ databases were searched from June 2009 to January 2022.
+ ELIGIBILITY CRITERIA: Published literature involving any study
+ design, written in English and evaluating any implemented
+ in-hospital quality improvement programme in trauma populations
+ worldwide. Literature that was non-English, unpublished and
+ involved non-hospital TQIPs was excluded. DATA EXTRACTION AND
+ SYNTHESIS: Two reviewers completed a three-stage screening
+ process using Covidence, with any discrepancies resolved through
+ a third reviewer. Content analysis using the Consolidated
+ Framework for Implementation Research identified facilitator and
+ barrier themes for in-hospital TQIP implementation. RESULTS:
+ Twenty-eight studies met the eligibility criteria from 3923
+ studies identified. The most discussed in-hospital TQIPs in
+ included literature were trauma registries. Facilitators and
+ barriers were similar across all country income levels. The main
+ facilitator themes identified were the prioritisation of staff
+ education and training, strengthening stakeholder dialogue and
+ providing standardised best-practice guidelines. The key barrier
+ theme identified in LMICs was poor data quality, while
+ high-income countries (HICs) had reduced communication across
+ professional hierarchies. CONCLUSIONS: Stakeholder
+ prioritisation of in-hospital TQIPs, along with increased
+ knowledge and consensus of trauma care best practices, are
+ essential efforts to reduce the global trauma burden. The
+ primary focus of future studies on in-hospital TQIPs in LMICs
+ should target improving registry data quality, while
+ interventions in HICs should target strengthening communication
+ channels between healthcare professionals.",
+ journal = "BMJ Open",
+ publisher = "BMJ",
+ volume = 13,
+ number = 2,
+ pages = "e068219",
+ month = feb,
+ year = 2023,
+ keywords = "change management; quality in health care; trauma management",
+ language = "en"
+}
+
+@ARTICLE{48,
+ title = "{WHO} releases Guidelines for trauma quality improvement
+ programmes",
+ author = "Mock, C",
+ journal = "Inj. Prev.",
+ publisher = "BMJ",
+ volume = 15,
+ number = 5,
+ pages = "359",
+ month = oct,
+ year = 2009,
+ language = "en"
+}
+
+@ARTICLE{49,
+ title = "The impact of a pan-regional inclusive trauma system on quality
+ of care",
+ author = "Cole, Elaine and Lecky, Fiona and West, Anita and Smith, Neil
+ and Brohi, Karim and Davenport, Ross",
+ journal = "Ann. Surg.",
+ publisher = "Ovid Technologies (Wolters Kluwer Health)",
+ volume = 264,
+ number = 1,
+ pages = "188--194",
+ month = jul,
+ year = 2016,
+ language = "en"
+}
+
+@ARTICLE{50,
+ title = "{ICU} management of trauma patients",
+ author = "Tisherman, Samuel A and Stein, Deborah M",
+ abstract = "OBJECTIVES: To describe the current state of the art regarding
+ management of the critically ill trauma patient with an emphasis
+ on initial management in the ICU. DATA SOURCES AND STUDY
+ SELECTION: A PubMed literature review was performed for relevant
+ articles in English related to the management of adult humans
+ with severe trauma. Specific topics included airway management,
+ hemorrhagic shock, resuscitation, and specific injuries to the
+ chest, abdomen, brain, and spinal cord. DATA EXTRACTION AND DATA
+ SYNTHESIS: The basic principles of initial management of the
+ critically ill trauma patients include rapid identification and
+ management of life-threatening injuries with the goal of
+ restoring tissue oxygenation and controlling hemorrhage as
+ rapidly as possible. The initial assessment of the patient is
+ often truncated for procedures to manage life-threatening
+ injuries. Major, open surgical procedures have often been
+ replaced by nonoperative or less-invasive approaches, even for
+ critically ill patients. Consequently, much of the early
+ management has been shifted to the ICU, where the goal is to
+ continue resuscitation to restore homeostasis while completing
+ the initial assessment of the patient and watching closely for
+ failure of nonoperative management, complications of procedures,
+ and missed injuries. CONCLUSIONS: The initial management of
+ critically ill trauma patients is complex. Multiple, sometimes
+ competing, priorities need to be considered. Close collaboration
+ between the intensivist and the surgical teams is critical for
+ optimizing patient outcomes.",
+ journal = "Crit. Care Med.",
+ publisher = "Ovid Technologies (Wolters Kluwer Health)",
+ volume = 46,
+ number = 12,
+ pages = "1991--1997",
+ month = dec,
+ year = 2018,
+ language = "en"
+}
+
+@ARTICLE{51,
+ title = "Outcomes of daytime procedures performed by attending surgeons
+ after night work",
+ author = "Govindarajan, Anand and Urbach, David R and Kumar, Matthew and
+ Li, Qi and Murray, Brian J and Juurlink, David and Kennedy, Erin
+ and Gagliardi, Anna and Sutradhar, Rinku and Baxter, Nancy N",
+ abstract = "BACKGROUND: Sleep loss in attending physicians has an unclear
+ effect on patient outcomes. In this study, we examined the
+ effect of medical care provided by physicians after midnight on
+ the outcomes of their scheduled elective procedures performed
+ during the day. METHODS: We conducted a population-based,
+ retrospective, matched-cohort study in Ontario, Canada. Patients
+ undergoing 1 of 12 elective daytime procedures performed by a
+ physician who had treated patients from midnight to 7 a.m. were
+ matched in a 1:1 ratio to patients undergoing the same procedure
+ by the same physician on a day when the physician had not
+ treated patients after midnight. Outcomes included death,
+ readmission, complications, length of stay, and procedure
+ duration. We used generalized estimating equations to compare
+ outcomes between patient groups. RESULTS: We included 38,978
+ patients, treated by 1448 physicians, in the study, of whom
+ 40.6\% were treated at an academic center. We found no
+ significant difference in the primary outcome (death,
+ readmission, or complication) between patients who underwent a
+ daytime procedure performed by a physician who had provided
+ patient care after midnight and those who underwent a procedure
+ performed by a physician who had not treated patients after
+ midnight (22.2\% and 22.4\%, respectively; P=0.66; adjusted odds
+ ratio, 0.99; 95\% confidence interval, 0.95 to 1.03). We also
+ found no significant difference in outcomes after stratification
+ for academic versus nonacademic center, physician's age, or type
+ of procedure. Secondary analyses revealed no significant
+ difference between patient groups in length of stay or procedure
+ duration. CONCLUSIONS: Overall, the risks of adverse outcomes of
+ elective daytime procedures were similar whether or not the
+ physician had provided medical services the previous night.
+ (Funded by the University of Toronto Dean's Fund and others.).",
+ journal = "N. Engl. J. Med.",
+ publisher = "New England Journal of Medicine (NEJM/MMS)",
+ volume = 373,
+ number = 9,
+ pages = "845--853",
+ month = aug,
+ year = 2015,
+ language = "en"
+}
+
+@article{52,
+ author = "Global Health Data Exchange",
+ year = 2019,
+ url={https://vizhub.healthdata.org/gbd-results/}
+
+}
+
+@ARTICLE{53,
+ title = "American College of Surgeons' Committee on Trauma, and the
+ International {ATLS} working group",
+ author = "The, Atls",
+ journal = "Journal of Trauma and Acute Care Surgery",
+ volume = 74,
+ number = 5,
+ pages = "1363--1366",
+ year = 2013
+}
+
+@ARTICLE{54,
+ title = "European Society of Intensive Care Medicine, and Society of
+ Critical Care Medicine: An Official American Thoracic
+ {Society/European} Society of Intensive Care {Medicine/Society}
+ of Critical Care Medicine Clinical Practice Guideline:
+ Mechanical ventilation in adult patients with acute respiratory
+ distress syndrome",
+ author = "Fan, E and Sorbo, Del and Goligher, L",
+ journal = "Am J Respir Crit Care Med",
+ publisher = "American Thoracic Society",
+ volume = 195,
+ year = 2017
+}
+
+@ARTICLE{55,
+ title = "Ventilator bundle and its effects on mortality among {ICU}
+ patients: A meta-analysis",
+ author = "Pileggi, Claudia and Mascaro, Valentina and Bianco, Aida and
+ Nobile, Carmelo G A and Pavia, Maria",
+ abstract = "Objectives: To assess the effectiveness of the ventilator bundle
+ in the reduction of mortality in ICU patients. Data Sources:
+ PubMed, Scopus, Web of Science, Cochrane Library for studies
+ published until June 2017. Study Selection: Included studies:
+ randomized controlled trials or any kind of nonrandomized
+ intervention studies, made reference to a ventilator bundle
+ approach, assessed mortality in ICU-ventilated adult patients.
+ Data Extraction: Items extracted: study characteristics,
+ description of the bundle approach, number of patients in the
+ comparison groups, hospital/ICU mortality, ventilator-associated
+ pneumonia--related mortality, assessment of compliance to
+ ventilator bundle and its score. Data Synthesis: Thirteen
+ articles were included. The implementation of a ventilator
+ bundle significantly reduced mortality (odds ratio, 0.90; 95\%
+ CI, 0.84--0.97), with a stronger effect with a restriction to
+ studies that reported mortality in ventilator-associated
+ pneumonia patients (odds ratio, 0.71; 95\% CI, 0.52--0.97), to
+ studies that provided active educational activities was analyzed
+ (odds ratio, 0.88; 95\% CI, 0.78--0.99), and when the role of
+ care procedures within the bundle (odds ratio, 0.87; 95\% CI,
+ 0.77--0.99). No survival benefit was associated with compliance
+ to ventilator bundles. However, these results may have been
+ confounded by the differential implementation of evidence-based
+ procedures at baseline, which showed improved survival in the
+ study subgroup that did not report implementation of these
+ procedures at baseline (odds ratio, 0.82; 95\% CI, 0.70--0.96).
+ Conclusions: Simple interventions in common clinical practice
+ applied in a coordinated way as a part of a bundle care are
+ effective in reducing mortality in ventilated ICU patients. More
+ prospective controlled studies are needed to define the effect
+ of ventilator bundles on survival outcomes.",
+ journal = "Crit. Care Med.",
+ publisher = "Ovid Technologies (Wolters Kluwer Health)",
+ volume = 46,
+ number = 7,
+ pages = "1167--1174",
+ month = jul,
+ year = 2018,
+ language = "en"
+}
+
+@ARTICLE{56,
+ title = "A review of inhaled nitric oxide and aerosolized epoprostenol in
+ acute lung injury or acute respiratory distress syndrome",
+ author = "Dzierba, Amy L and Abel, Erik E and Buckley, Mitchell S and Lat,
+ Ishaq",
+ abstract = "Acute respiratory distress syndrome (ARDS) and acute lung injury
+ (ALI) are conditions associated with an estimated mortality of
+ 40--50\%. The use of inhaled vasodilators can help to improve
+ oxygenation without hemodynamic effects. This article reviews
+ relevant studies addressing the safety and efficacy of inhaled
+ nitric oxide (iNO) and aerosolized epoprostenol (aEPO) in the
+ treatment of life-threatening hypoxemia associated with ARDS and
+ ALI. In addition, the article also provides a practicable guide
+ to the clinical application of these therapies. Nine prospective
+ randomized controlled trials were included for iNO reporting on
+ changes in oxygenation or clinical outcomes. Seven reports of
+ aEPO were examined for changes in oxygenation. Based on
+ currently available data, the use of either iNO or aEPO is safe
+ to use in patients with ALI or ARDS to transiently improve
+ oxygenation. No differences have been observed in survival,
+ ventilator-free days, or attenuation in disease severity.
+ Further studies with consistent end points using standard
+ delivery devices and standard modes of mechanical ventilation
+ are needed to determine the overall benefit with iNO or aEPO.",
+ journal = "Pharmacotherapy",
+ publisher = "Wiley",
+ volume = 34,
+ number = 3,
+ pages = "279--290",
+ month = mar,
+ year = 2014,
+ language = "en"
+}
+
+
+@ARTICLE{57,
+ title = "Veno-venous extracorporeal membrane oxygenation ({VV} {ECMO}) for
+ acute respiratory failure following injury: Outcomes in a
+ high-volume adult trauma center with a dedicated unit for {VV}
+ {ECMO}",
+ author = "Menaker, Jay and Tesoriero, Ronald B and Tabatabai, Ali and
+ Rabinowitz, Ronald P and Cornachione, Christopher and Lonergan,
+ Terence and Dolly, Katelyn and Rector, Raymond and O'Connor,
+ James V and Stein, Deborah M and Scalea, Thomas M",
+ abstract = "INTRODUCTION: The use of veno-venous extracorporeal membrane
+ oxygenation (VV ECMO) has increased over the past decade. The
+ purpose of this study was to evaluate outcomes in adult trauma
+ patients requiring VV ECMO. METHODS: Data were collected on adult
+ trauma patients admitted between January 1, 2015, and November 1,
+ 2016. Demographics, injury-specific data, ECMO data, and survival
+ to discharge were recorded. Medians [interquartile range (IQR)]
+ were reported. A p value $\leq$0.05 was considered statistically
+ significant. RESULTS: Eighteen patients required VV ECMO during
+ the study period. Median age was 28.5 years (IQR 24-43). Median
+ injury severity score (ISS) was 27 (IQR 21-41); median PaO2/FiO2
+ (P/F) prior to ECMO cannulation was 61 (IQR 50-70). Median time
+ from injury to cannulation was 3 (IQR 0-6) days. Median duration
+ of ECMO was 266 (IQR 177-379) hours. Survival to discharge was
+ 78\%. Survivors had a significantly higher ISS (p = 0.03), longer
+ intensive care unit length of stay (ICU LOS) (p < 0.0004),
+ hospital LOS (p < 0.000004), and time on the ventilator (p <
+ 0.0003). Median time of injury to cannulation was significantly
+ longer in patients who survived to discharge (p = 0.01). There
+ was no difference in P/F ratio prior to cannulation (p = ns).
+ CONCLUSION: We have demonstrated improved outcome of patients
+ requiring VV ECMO following injury compared to historical data.
+ Although shorter time from injury to cannulation for VV ECMO was
+ associated with death, select patients who meet criteria for VV
+ ECMO early following injury should be referred/transferred to a
+ tertiary care facility that specializes in trauma and ECMO care.",
+ journal = "World J. Surg.",
+ volume = 42,
+ number = 8,
+ pages = "2398--2403",
+ month = aug,
+ year = 2018,
+ language = "en"
+}
+
+@ARTICLE{58,
+ title = "Failure to clear elevated lactate predicts 24-hour mortality in
+ trauma patients",
+ author = "Dezman, Zachary D W and Comer, Angela C and Smith, Gordon S and
+ Narayan, Mayur and Scalea, Thomas M and Hirshon, Jon Mark",
+ abstract = "BACKGROUND: Lactate clearance is a standard resuscitation goal
+ in patients in nontraumatic shock but has not been investigated
+ adequately as a tool to identify trauma patients at risk of
+ dying. Our objective was to determine if trauma patients with
+ impaired lactate clearance have a higher 24-hour mortality rate
+ than patients whose lactate concentration normalizes. METHODS: A
+ retrospective chart review identified patients who were admitted
+ directly from the scene of injury to an urban trauma center
+ between 2010 and 2013 and who had at least one lactate
+ concentration measurement within 24 hours. Transfers, patients
+ without lactate measurement, and those who were dead on arrival
+ were excluded. Of the 26,545 screened patients, 18,304
+ constituted the initial lactate measurement population, and
+ 3,887 were the lactate clearance cohorts. RESULTS: Initial
+ lactate had an area under the receiver operating characteristic
+ curve of 0.86 and 0.73 for mortality at 24 hours and in the
+ hospital, respectively. An initial concentration of 3 mmol/L or
+ greater had a sensitivity of 0.86 and a specificity of 0.73 for
+ mortality at 24 hours. The mortality rate among patients with
+ elevated lactate concentrations (n = 2,381; 5.6 [2.8] mmol/L)
+ that did not decline to less than 2.0 mmol/L in response to
+ resuscitative efforts (mean [SD] second measurement, 3.7 [1.9]
+ mmol/L) was nearly seven times higher (4.1\% vs. 0.6\%, p <
+ 0.001) than among those with an elevated concentration (n =
+ 1,506, 5.3 [2.7] mmol/L) that normalized (1.4 [0.4] mmol/L).
+ Logistic regression analysis showed that failure to clear
+ lactate was associated with death more than any other feature
+ (odds ratio, 7.4; 95\% confidence interval, 1.5-35.5), except
+ having an Injury Severity Score (ISS) greater than 25 (odds
+ ratio, 8.2; 95\% confidence interval, 2.7-25.2). CONCLUSION:
+ Failure to clear lactate is a strong negative prognostic marker
+ after injury. An initial lactate measurement combined with a
+ second measurement for high-risk individuals might constitute a
+ useful method of risk stratifying injured patients. LEVEL OF
+ EVIDENCE: Prognostic study, level III.",
+ journal = "J. Trauma Acute Care Surg.",
+ publisher = "Ovid Technologies (Wolters Kluwer Health)",
+ volume = 79,
+ number = 4,
+ pages = "580--585",
+ month = oct,
+ year = 2015,
+ language = "en"
+}
+
+@ARTICLE{59,
+ title = "Predictors of mortality of trauma patients admitted to the
+ {ICU}: a retrospective observational study☆",
+ author = "Papadimitriou-Olivgeris, Matthaios and Panteli, Eleftheria and
+ Koutsileou, Kyriaki and Boulovana, Maria and Zotou, Anastasia
+ and Marangos, Markos and Fligou, Fotini",
+ abstract = "BACKGROUND AND OBJECTIVES: Worldwide, trauma is one of the
+ leading causes of morbidity and mortality. The aim of the
+ present study is to identify the predictors of mortality of
+ trauma patients requiring Intensive Care Unit (ICU) admission.
+ METHODS: This retrospective study was conducted in the ICU of
+ our institution in Greece during a six-year period (2010-215).
+ RESULTS: Among 326 patients, trauma was caused by road traffic
+ accidents in .5\%, followed by falls (21.1\%) and violence
+ (7.4\%). Thirty-day mortality was 27.3\%. Multivariate analysis
+ showed that higher New Injury Severity Score (NISS), severe
+ head/neck injury, acute kidney injury, septic shock and
+ hemorrhagic shock were significantly associated with mortality
+ while higher Revised Injury Severity Classification, version II
+ (RISC II) and the administration of enteral nutrition were
+ associated with survival. NISS showed the higher accuracy in
+ predicting 30-day mortality followed by RISC II, while scores
+ based only in physiological variables had lower predictive
+ ability. CONCLUSIONS: Increased mortality was strongly
+ associated with the severity of the injury upon admission.
+ Traumatic brain injury, septic shock and acute kidney injury
+ have also been found among the strongest predictors of
+ mortality. NISS can be considered as a statistically superior
+ score in predicting mortality of severely injured patients.",
+ journal = "Braz. J. Anesthesiol.",
+ publisher = "Elsevier BV",
+ volume = 71,
+ number = 1,
+ pages = "23--30",
+ month = jan,
+ year = 2021,
+ keywords = "New Injury Severity Score (NISS); Revised Injury Severity
+ Classification, version II (RISC II); Road traffic accident;
+ Sepsis; Traumatic brain injury",
+ copyright = "http://creativecommons.org/licenses/by-nc-nd/4.0/",
+ language = "en"
+}
+
+@ARTICLE{60,
+ title = "Physiology, Trauma",
+ author = "Dumovich, J and Singh, P",
+ journal = "Physiology, Trauma. In StatPearls. StatPearls Publishing",
+ year = 2022
+}
+
+@article{61,
+ author = "LÖF",
+ title = "Säker traumavård",
+ url={https://lof.se/patientsakerhet/vara-projekt/saker-traumavard}
+ }
+
+@ARTICLE{62,
+ title = "Classifying errors in preventable and potentially preventable
+ trauma deaths: a 9-year review using the Joint Commission's
+ standardized methodology",
+ author = "Vioque, Sandra M and Kim, Patrick K and McMaster, Janet and
+ Gallagher, John and Allen, Steven R and Holena, Daniel N and
+ Reilly, Patrick M and Pascual, Jose L",
+ abstract = "BACKGROUND: Benchmarking and classification of avoidable errors
+ in trauma care are difficult as most reports classify errors
+ using variable locally derived schemes. We sought to classify
+ errors in a large trauma population using standardized Joint
+ Commission taxonomy. METHODS: All preventable/potentially
+ preventable deaths identified at an urban, level-1 trauma center
+ (January 2002 to December 2010) were abstracted from the trauma
+ registry. Errors deemed avoidable were classified within the
+ 5-node (impact, type, domain, cause, and prevention) Joint
+ Commission taxonomy. RESULTS: Of the 377 deaths in 11,100 trauma
+ contacts, 106 (7.7\%) were preventable/potentially preventable
+ deaths related to 142 avoidable errors. Most common error types
+ were in clinical performance (inaccurate diagnosis). Error
+ domain involved primarily the emergency department (therapeutic
+ interventions), caused mostly by knowledge deficits.
+ Communication improvement was the most common mitigation
+ strategy. CONCLUSION: Standardized classification of errors in
+ preventable trauma deaths most often involve clinical
+ performance in the early phases of care and can be mitigated
+ with universal strategies.",
+ journal = "Am. J. Surg.",
+ publisher = "Elsevier BV",
+ volume = 208,
+ number = 2,
+ pages = "187--194",
+ month = aug,
+ year = 2014,
+ keywords = "Avoidable errors; Joint Commission taxonomy of avoidable medical
+ errors; Trauma preventable deaths",
+ language = "en"
+}
+
+@ARTICLE{63,
+ title = "Errors in adult trauma resuscitation: a systematic review",
+ author = "Nikouline, Anton and Quirion, Andrew and Jung, James J and
+ Nolan, Brodie",
+ abstract = "INTRODUCTION: Trauma resuscitation at dedicated trauma centers
+ typically consist of ad-hoc teams performing critical tasks in a
+ time-limited manner. This creates a high stakes environment apt
+ or avoidable errors. Reporting of errors in trauma resuscitation
+ is generally center-dependent and lacks common terminology.
+ METHODS: We conducted a systematic review by searching Ovid
+ Medline, Scopus and Embase from inception to February 24, 2021
+ for errors in adult trauma resuscitation. English studies
+ published after 2001 were included. Studies were assessed by two
+ independent reviewers for meeting inclusion/exclusion criteria.
+ Errors were characterized from the included studies and a
+ summary table was developed. Our review was prospectively
+ registered with the International Prospective Register of
+ Systematic Reviews (PROSPERO) (CRD42020152875). RESULTS: The
+ literature search retrieved 4658 articles with 26 meeting
+ eligibility criteria. Errors were identified by morbidity and
+ mortality rounds or other committee in 62\%, missed injuries on
+ tertiary assessment or radiology review in 12\%, deviations from
+ algorithmic guidelines in 12\% or predefined for chest tube
+ complications, critical incident reporting, aspiration or delays
+ in care. In total there were 39 unique error types identified
+ and divided into 9 categories including Emergency Medical
+ Services handover, airway, assessment of injuries, patient
+ monitoring and access, transfusion/blood related, management of
+ injuries, team communication/dynamics, procedure error and
+ disposition. CONCLUSIONS: Overall, our systematic review
+ identified 39 unique error types in trauma resuscitation.
+ Identifying these errors is imperative in developing systems for
+ improvement of trauma care.",
+ journal = "CJEM",
+ publisher = "Springer Science and Business Media LLC",
+ volume = 23,
+ number = 4,
+ pages = "537--546",
+ month = jul,
+ year = 2021,
+ keywords = "Critical Care; Emergency treatment; Medical errors;
+ Resuscitation; Trauma",
+ language = "en"
+}
+
+@ARTICLE{64,
+ title = "Complications and in-hospital mortality in trauma patients
+ treated in intensive care units in the United States, 2013",
+ author = "Prin, Meghan and Li, Guohua",
+ abstract = "BACKGROUND: Traumatic injury is a leading cause of morbidity and
+ mortality worldwide, but epidemiologic data about trauma
+ patients who require intensive care unit (ICU) admission are
+ scant. This study aimed to describe the annual incidence of ICU
+ admission for adult trauma patients, including an assessment of
+ risk factors for hospital complications and mortality in this
+ population. METHODS: This was a retrospective study of adults
+ hospitalized at Level 1 and Level 2 trauma centers after trauma
+ and recorded in the National Trauma Data Bank in 2013. Multiple
+ logistic regression analyses were performed to determine
+ predictors of hospital complications and hospital mortality for
+ those who required ICU admission. RESULTS: There were an
+ estimated total of 1.03 million ICU admissions for trauma at
+ Level 1 and Level 2 trauma centers in the United States in 2013,
+ yielding an annual incidence of 3.3 per 1000 population. The
+ annual incidence was highest in men (4.6 versus 1.9 per 100,000
+ for women), those aged 80 years or older (7.8 versus 3.6-4.3 per
+ 100,000 in other age groups), and residents in the Western US
+ Census region (3.9 versus 2.7 to 3.6 per 100,000 in other
+ regions). The most common complications in patients admitted to
+ the ICU were pneumonia (10.9 \%), urinary tract infection (4.7
+ \%), and acute respiratory distress syndrome (4.4 \%). Hospital
+ mortality was significantly higher for ICU patients who
+ developed one or more complications (16.9 \% versus 10.7 \% for
+ those who did not develop any complications, p < 0.001).
+ CONCLUSIONS: Admission to the ICU after traumatic injury is
+ common, and almost a quarter of these patients experience
+ hospital complications. Hospital complications are associated
+ with significantly increased risk of mortality.",
+ journal = "Inj. Epidemiol.",
+ publisher = "Springer Science and Business Media LLC",
+ volume = 3,
+ number = 1,
+ pages = "18",
+ month = dec,
+ year = 2016,
+ keywords = "Complications; Critical care; Hospitalization; Intensive care
+ unit; Trauma",
+ copyright = "https://creativecommons.org/licenses/by/4.0",
+ language = "en"
+}
+
+@ARTICLE{65,
+ title = "Epidemiology of weaning outcome according to a new definition.
+ The {WIND} study",
+ author = "B{\'e}duneau, Ga{\"e}tan and Pham, T{\`a}i and Schortgen,
+ Fr{\'e}d{\'e}rique and Piquilloud, Lise and Zogheib, Elie and
+ Jonas, Maud and Grelon, Fabien and Runge, Isabelle and {Nicolas
+ Terzi} and Grang{\'e}, Steven and Barberet, Guillaume and
+ Guitard, Pierre-Gildas and Frat, Jean-Pierre and Constan, Adrien
+ and Chretien, Jean-Marie and Mancebo, Jordi and Mercat, Alain
+ and Richard, Jean-Christophe M and Brochard, Laurent and {WIND
+ (Weaning according to a New Definition) Study Group and the REVA
+ (R{\'e}seau Europ{\'e}en de Recherche en Ventilation
+ Artificielle) Network ‡}",
+ abstract = "RATIONALE: The weaning process concerns all patients receiving
+ mechanical ventilation. A previous classification into simple,
+ prolonged, and difficult weaning ignored weaning failure and
+ presupposed the use of spontaneous breathing trials. OBJECTIVES:
+ To describe the weaning process, defined as starting with any
+ attempt at separation from mechanical ventilation and its
+ prognosis, according to a new operational classification working
+ for all patients under ventilation. METHODS: This was a
+ multinational prospective multicenter observational study over 3
+ months of all patients receiving mechanical ventilation in 36
+ intensive care units, with daily collection of ventilation and
+ weaning modalities. Pragmatic definitions of separation attempt
+ and weaning success allowed us to allocate patients in four
+ groups. MEASUREMENTS AND MAIN RESULTS: A total of 2,729 patients
+ were enrolled. Although half of them could not be classified
+ using the previous definition, 99\% entered the groups on the
+ basis of our new definition as follows: 24\% never started a
+ weaning process, 57\% had a weaning process of less than 24
+ hours (group 1), 10\% had a difficult weaning of more than 1 day
+ and less than 1 week (group 2), and 9\% had a prolonged weaning
+ duration of 1 week or more (group 3). Duration of ventilation,
+ intensive care unit stay, and mortality (6, 17, and 29\% for the
+ three groups, respectively) all significantly increased from one
+ group to the next. The unadjusted risk of dying was 19\% after
+ the first separation attempt and increased to 37\% after 10
+ days. CONCLUSIONS: A new classification allows us to categorize
+ all weaning situations. Every additional day without a weaning
+ success after the first separation attempt increases the risk of
+ dying.",
+ journal = "Am. J. Respir. Crit. Care Med.",
+ publisher = "American Thoracic Society",
+ volume = 195,
+ number = 6,
+ pages = "772--783",
+ month = mar,
+ year = 2017,
+ keywords = "mechanical ventilation; outcome; separation attempt; weaning",
+ language = "en"
+}
+
+@ARTICLE{66,
+ title = "Risk factors for prolonged mechanical ventilation and weaning
+ failure: A systematic review",
+ author = "Trudzinski, Franziska C and Neetz, Benjamin and Bornitz, Florian
+ and M{\"u}ller, Michael and Weis, Aline and Kronsteiner,
+ Dorothea and Herth, Felix J F and Sturm, Noemi and Gassmann,
+ Vicky and Frerk, Timm and Neurohr, Claus and Ghiani, Alessandro
+ and Joves, Biljana and Schneider, Armin and Szecsenyi, Joachim
+ and von Schumann, Selina and Meis, Jan",
+ abstract = "INTRODUCTION: Prolonged mechanical ventilation (PMV) and weaning
+ failure are factors associated with prolonged hospital length of
+ stay and increased morbidity and mortality. In addition to the
+ burden these places on patients and their families, it also
+ imposes high costs on the public health system. The aim of this
+ systematic review was to identify risk factors for PMV and
+ weaning failure. METHODS: The study was conducted according to
+ PRISMA guidelines. After a comprehensive search of the COCHRANE
+ Library, CINHAL, Web of Science, MEDLINE, and the LILACS
+ Database a PubMed request was made on June 8, 2020. Studies that
+ examined risk factors for PMV, defined as mechanical ventilation
+ $\geq$96 h, weaning failure, and prolonged weaning in German and
+ English were considered eligible; reviews, meta-analyses, and
+ studies in very specific patient populations whose results are
+ not necessarily applicable to the majority of ICU patients as
+ well as pediatric studies were excluded from the analysis. This
+ systematic review was registered in the PROSPERO register under
+ the number CRD42021271038. RESULTS: Of 532 articles identified,
+ 23 studies with a total of 23,418 patients met the inclusion
+ criteria. Fourteen studies investigated risk factors of PMV
+ including prolonged weaning, 9 studies analyzed risk factors of
+ weaning failure. The concrete definitions of these outcomes
+ varied considerably between studies. For PMV, a variety of risk
+ factors were identified, including comorbidities, site of
+ intubation, various laboratory or blood gas parameters,
+ ventilator settings, functional parameters, and critical care
+ scoring systems. The risk of weaning failure was mainly related
+ to age, previous home mechanical ventilation (HMV), cause of
+ ventilation, and preexisting underlying diseases. Elevated PaCO2
+ values during spontaneous breathing trials were indicative of
+ prolonged weaning and weaning failure. CONCLUSION: A direct
+ comparison of risk factors was not possible because of the
+ heterogeneity of the studies. The large number of different
+ definitions and relevant parameters reflects the heterogeneity
+ of patients undergoing PMV and those discharged to HMV after
+ unsuccessful weaning. Multidimensional scores are more likely to
+ reflect the full spectrum of patients ventilated in different
+ ICUs than single risk factors.",
+ journal = "Respiration",
+ publisher = "S. Karger AG",
+ volume = 101,
+ number = 10,
+ pages = "959--969",
+ month = aug,
+ year = 2022,
+ keywords = "Home mechanical ventilation; Mechanical ventilation; Prolonged
+ mechanical ventilation; Weaning",
+ copyright = "https://creativecommons.org/licenses/by/4.0/",
+ language = "en"
+}
+
+@ARTICLE{67,
+ title = "Preventable or potentially preventable mortality at a mature
+ trauma center",
+ author = "Teixeira, Pedro G R and Inaba, Kenji and Hadjizacharia, Pantelis
+ and Brown, Carlos and Salim, Ali and Rhee, Peter and Browder,
+ Timothy and Noguchi, Thomas T and Demetriades, Demetrios",
+ abstract = "OBJECTIVE: The objective of this study was to analyze the
+ preventable and potentially preventable deaths occurring at a
+ mature Level I trauma center. METHODS: All trauma patients that
+ died during their initial hospital admission during an 8-year
+ period (January, 1998 to December, 2005) were analyzed. The
+ deaths were initially reviewed at a weekly Morbidity and
+ Mortality (M\&M) conference followed by a multidisciplinary
+ (Trauma Surgery, Critical Care, Emergency Medicine,
+ Neurosurgery, Nursing, and Coroner) Combined Trauma Death Review
+ Committee, and were classified into nonpreventable, potentially
+ preventable, and preventable deaths. All preventable and
+ potentially preventable deaths were identified for the purpose
+ of the study. Quality improvement death forms included data on
+ epidemiology, vital signs, injury severity, type of injury,
+ probability of survival with Trauma and Injury Severity Score
+ methodology, preventability (nonpreventable, potentially
+ preventable, and preventable deaths), errors in the evaluation
+ and management of the patient, and classification of errors
+ (system, judgment, knowledge). Additional injury details,
+ clinical course, circumstances leading to the death and autopsy
+ findings were abstracted from the trauma registry and individual
+ chart review. RESULTS: During the study period, 35,311 patients
+ meeting trauma registry criteria were admitted and a total of
+ 2,081 (5.9\%) deaths occurred. Fifty-one deaths were classified
+ as preventable or potentially preventable deaths (0.1\% of
+ admissions, 2.5\% of deaths). Eleven of them (0.53\% of deaths)
+ were classified as preventable and 40 (1.92\% of deaths) as
+ potentially preventable deaths. Mean age was 40 years, 66.7\%
+ were men, mean Injury Severity Score was 27, 74.5\% were blunt.
+ The most common cause of death was bleeding (20, 39.2\%)
+ followed by multiple organ dysfunction syndrome (14, 27.5\%) and
+ cardiorespiratory arrest (8, 15.6\%). This was caused by a delay
+ in treatment (27, 52.9\%), clinical judgment error (11, 21.6\%),
+ missed diagnosis (6, 11.8\%), technical error (4, 7.8\%), and
+ other (3, 5.9\%). The deaths peaked at two time periods: 26
+ (51.1\%) during the first 24 hours and 16 (31.4\%) after 7 days.
+ Only one patient (2.0\%) died in the first hour. The most common
+ location of death was the intensive care unit (28, 54.9\%),
+ operating room (13, 25.5\%), and emergency room (5, 9.8\%).
+ CONCLUSION: Preventable or potentially preventable deaths are
+ rare but do occur at an academic Level I trauma center. Delay in
+ treatment and error in judgment are the leading causes of
+ preventable and potentially preventable deaths.",
+ journal = "J. Trauma",
+ publisher = "Ovid Technologies (Wolters Kluwer Health)",
+ volume = 63,
+ number = 6,
+ pages = "1338--46; discussion 1346--7",
+ month = dec,
+ year = 2007,
+ language = "en"
+}
+
+@ARTICLE{68,
+ title = "Opportunities for improvement in the management of patients who
+ die from haemorrhage after trauma",
+ author = "O'Reilly, D and Mahendran, K and West, A and Shirley, P and
+ Walsh, M and Tai, N",
+ abstract = "BACKGROUND: Bleeding is the leading cause of preventable death
+ after injury. This retrospective study aimed to characterize
+ opportunities for performance improvement (OPIs) identified in
+ patients who died from bleeding and were considered by the
+ quality improvement system of a major trauma centre. METHODS:
+ All trauma deaths in 2006-2010 were discussed at the trauma
+ morbidity and mortality meeting. Deaths from haemorrhage were
+ identified and subjected to qualitative and quantitative
+ evaluation. OPIs were identified and remedial action was taken.
+ RESULTS: During the study interval there were 7511 trauma team
+ activations; 423 patients died. Haemorrhage was the second most
+ common cause of death, in 112 patients, and made a substantial
+ contribution to death in a further 15. For 84 of these 127
+ patients, a total of 150 OPIs were identified. Most arose in the
+ emergency department, but involved personnel from many
+ departments. Problems with decision-making were more common than
+ errors in technical skill. OPIs frequently involved the decision
+ between surgery, radiology and further investigation. Delayed
+ and inappropriate surgery occurred even when investigation and
+ diagnosis were appropriate. The mortality rate among patients
+ presenting in shock fell significantly over the study interval
+ (P < 0·026). CONCLUSION: Problems with judgement are more common
+ than those of skill. Death from traumatic haemorrhage is
+ associated with identifiable, remediable failures in care. The
+ implementation of a systematic trauma quality improvement system
+ was associated with a fall in the mortality rate among patients
+ presenting in shock.",
+ journal = "Br. J. Surg.",
+ publisher = "Oxford University Press (OUP)",
+ volume = 100,
+ number = 6,
+ pages = "749--755",
+ month = may,
+ year = 2013,
+ copyright = "https://academic.oup.com/journals/pages/open\_access/funder\_policies/chorus/standard\_publication\_model",
+ language = "en"
+}
+
+@article{69,
+title = {{ICU} {Management} of {Trauma} {Patients}},
+volume = {46},
+issn = {0090-3493},
+url = {https://journals.lww.com/00003246-201812000-00013},
+doi = {10.1097/CCM.0000000000003407},
+abstract = {Objectives:
+To describe the current state of the art regarding management of the critically ill trauma patient with an emphasis on initial management in the ICU.
+
+
+Data Sources and Study Selection:
+A PubMed literature review was performed for relevant articles in English related to the management of adult humans with severe trauma. Specific topics included airway management, hemorrhagic shock, resuscitation, and specific injuries to the chest, abdomen, brain, and spinal cord.
+
+
+Data Extraction and Data Synthesis:
+The basic principles of initial management of the critically ill trauma patients include rapid identification and management of life-threatening injuries with the goal of restoring tissue oxygenation and controlling hemorrhage as rapidly as possible. The initial assessment of the patient is often truncated for procedures to manage life-threatening injuries. Major, open surgical procedures have often been replaced by nonoperative or less-invasive approaches, even for critically ill patients. Consequently, much of the early management has been shifted to the ICU, where the goal is to continue resuscitation to restore homeostasis while completing the initial assessment of the patient and watching closely for failure of nonoperative management, complications of procedures, and missed injuries.
+
+
+Conclusions:
+The initial management of critically ill trauma patients is complex. Multiple, sometimes competing, priorities need to be considered. Close collaboration between the intensivist and the surgical teams is critical for optimizing patient outcomes.},
+language = {en},
+number = {12},
+urldate = {2024-05-31},
+journal = {Critical Care Medicine},
+author = {Tisherman, Samuel A. and Stein, Deborah M.},
+month = dec,
+year = {2018},
+pages = {1991--1997},
+}
+
+@article{70,
+title = {Indicators of the quality of trauma care and the performance of trauma systems},
+volume = {99},
+issn = {1365-2168},
+url = {http://dx.doi.org/10.1002/bjs.7754},
+doi = {10.1002/bjs.7754},
+number = {Supplement\_1},
+journal = {British Journal of Surgery},
+author = {Gruen, R L and Gabbe, B J and Stelfox, H T and Cameron, P A},
+month = dec,
+year = {2011},
+note = {Publisher: Oxford University Press (OUP)},
+keywords = {Delivery of Health Care, Emergency Medical Services, Wounds and Injuries, Traumatology, Humans, Benchmarking, Hospital Mortality, Quality Improvement, Quality Indicators, Health Care, Quality of Life, Treatment Outcome},
+pages = {97--104},
+file = {Gruen et al. - 2011 - Indicators of the quality of trauma care and the p.pdf:/Users/martin/Zotero/storage/Z7QSTRQU/Gruen et al. - 2011 - Indicators of the quality of trauma care and the p.pdf:application/pdf},
+}
+and
+@book{american_college_of_surgeons_resources_2022,
+address = {Chicago, IL 60611-3295},
+title = {Resources for {Optimal} {Care} of the {Injured} {Patient}},
+isbn = {978-1-73692-129-6},
+publisher = {American College of Surgeons},
+author = {{American College of Surgeons}},
+year = {2022},
+file = {American College of Surgeons - 2022 - Resources for Optimal Care of the Injured Patient.pdf:/Users/martin/Zotero/storage/87WEJE59/American College of Surgeons - 2022 - Resources for Optimal Care of the Injured Patient.pdf:application/pdf},
+}
+
+@ARTICLE{71,
+ title = "Trauma score",
+ author = "Champion, H R and Sacco, W J and Carnazzo, A J and Copes, W and
+ Fouty, W J",
+ abstract = "The Trauma Score (TS), a simple physiological measure of injury
+ severity, is presented as a modification of the previously
+ reported Triage Index by consensus physician peer review.
+ Performance of the Trauma Score is presented as an index of
+ injury severity both alone and in combination with an anatomic
+ index of injury severity, the Injury Severity Score (ISS) and
+ patient age. The application of these tools for field triage and
+ evaluation of care of the trauma victim is proposed.",
+ journal = "Crit. Care Med.",
+ publisher = "Ovid Technologies (Wolters Kluwer Health)",
+ volume = 9,
+ number = 9,
+ pages = "672--676",
+ month = sep,
+ year = 1981,
+ language = "en"
+}
+
+
+@ARTICLE{72,
+ title = "Opportunities for improvement in the management of patients who
+ die from haemorrhage after trauma",
+ author = "O'Reilly, D and Mahendran, K and West, A and Shirley, P and
+ Walsh, M and Tai, N",
+ abstract = "BACKGROUND: Bleeding is the leading cause of preventable death
+ after injury. This retrospective study aimed to characterize
+ opportunities for performance improvement (OPIs) identified in
+ patients who died from bleeding and were considered by the
+ quality improvement system of a major trauma centre. METHODS:
+ All trauma deaths in 2006-2010 were discussed at the trauma
+ morbidity and mortality meeting. Deaths from haemorrhage were
+ identified and subjected to qualitative and quantitative
+ evaluation. OPIs were identified and remedial action was taken.
+ RESULTS: During the study interval there were 7511 trauma team
+ activations; 423 patients died. Haemorrhage was the second most
+ common cause of death, in 112 patients, and made a substantial
+ contribution to death in a further 15. For 84 of these 127
+ patients, a total of 150 OPIs were identified. Most arose in the
+ emergency department, but involved personnel from many
+ departments. Problems with decision-making were more common than
+ errors in technical skill. OPIs frequently involved the decision
+ between surgery, radiology and further investigation. Delayed
+ and inappropriate surgery occurred even when investigation and
+ diagnosis were appropriate. The mortality rate among patients
+ presenting in shock fell significantly over the study interval
+ (P < 0·026). CONCLUSION: Problems with judgement are more common
+ than those of skill. Death from traumatic haemorrhage is
+ associated with identifiable, remediable failures in care. The
+ implementation of a systematic trauma quality improvement system
+ was associated with a fall in the mortality rate among patients
+ presenting in shock.",
+ journal = "Br. J. Surg.",
+ publisher = "Oxford University Press (OUP)",
+ volume = 100,
+ number = 6,
+ pages = "749--755",
+ month = may,
+ year = 2013,
+ copyright = "https://academic.oup.com/journals/pages/open\_access/funder\_policies/chorus/standard\_publication\_model",
+ language = "en"
+}
+
+@ARTICLE{73,
+ title = "Preventable or potentially preventable mortality at a mature
+ trauma center",
+ author = "Teixeira, Pedro G R and Inaba, Kenji and Hadjizacharia, Pantelis
+ and Brown, Carlos and Salim, Ali and Rhee, Peter and Browder,
+ Timothy and Noguchi, Thomas T and Demetriades, Demetrios",
+ abstract = "OBJECTIVE: The objective of this study was to analyze the
+ preventable and potentially preventable deaths occurring at a
+ mature Level I trauma center. METHODS: All trauma patients that
+ died during their initial hospital admission during an 8-year
+ period (January, 1998 to December, 2005) were analyzed. The
+ deaths were initially reviewed at a weekly Morbidity and
+ Mortality (M\&M) conference followed by a multidisciplinary
+ (Trauma Surgery, Critical Care, Emergency Medicine,
+ Neurosurgery, Nursing, and Coroner) Combined Trauma Death Review
+ Committee, and were classified into nonpreventable, potentially
+ preventable, and preventable deaths. All preventable and
+ potentially preventable deaths were identified for the purpose
+ of the study. Quality improvement death forms included data on
+ epidemiology, vital signs, injury severity, type of injury,
+ probability of survival with Trauma and Injury Severity Score
+ methodology, preventability (nonpreventable, potentially
+ preventable, and preventable deaths), errors in the evaluation
+ and management of the patient, and classification of errors
+ (system, judgment, knowledge). Additional injury details,
+ clinical course, circumstances leading to the death and autopsy
+ findings were abstracted from the trauma registry and individual
+ chart review. RESULTS: During the study period, 35,311 patients
+ meeting trauma registry criteria were admitted and a total of
+ 2,081 (5.9\%) deaths occurred. Fifty-one deaths were classified
+ as preventable or potentially preventable deaths (0.1\% of
+ admissions, 2.5\% of deaths). Eleven of them (0.53\% of deaths)
+ were classified as preventable and 40 (1.92\% of deaths) as
+ potentially preventable deaths. Mean age was 40 years, 66.7\%
+ were men, mean Injury Severity Score was 27, 74.5\% were blunt.
+ The most common cause of death was bleeding (20, 39.2\%)
+ followed by multiple organ dysfunction syndrome (14, 27.5\%) and
+ cardiorespiratory arrest (8, 15.6\%). This was caused by a delay
+ in treatment (27, 52.9\%), clinical judgment error (11, 21.6\%),
+ missed diagnosis (6, 11.8\%), technical error (4, 7.8\%), and
+ other (3, 5.9\%). The deaths peaked at two time periods: 26
+ (51.1\%) during the first 24 hours and 16 (31.4\%) after 7 days.
+ Only one patient (2.0\%) died in the first hour. The most common
+ location of death was the intensive care unit (28, 54.9\%),
+ operating room (13, 25.5\%), and emergency room (5, 9.8\%).
+ CONCLUSION: Preventable or potentially preventable deaths are
+ rare but do occur at an academic Level I trauma center. Delay in
+ treatment and error in judgment are the leading causes of
+ preventable and potentially preventable deaths.",
+ journal = "J. Trauma",
+ publisher = "Ovid Technologies (Wolters Kluwer Health)",
+ volume = 63,
+ number = 6,
+ pages = "1338--46; discussion 1346--7",
+ month = dec,
+ year = 2007,
+ language = "en"
+}
+
+@ARTICLE{74,
+ title = "Learning from 2523 trauma deaths in India- opportunities to
+ prevent in-hospital deaths",
+ author = "Roy, Nobhojit and Kizhakke Veetil, Deepa and Khajanchi, Monty
+ Uttam and Kumar, Vineet and Solomon, Harris and Kamble, Jyoti
+ and Basak, Debojit and Tomson, G{\"o}ran and von Schreeb, Johan",
+ abstract = "BACKGROUND: A systematic analysis of trauma deaths is a step
+ towards trauma quality improvement in Indian hospitals. This
+ study estimates the magnitude of preventable trauma deaths in
+ five Indian hospitals, and uses a peer-review process to
+ identify opportunities for improvement (OFI) in trauma care
+ delivery. METHODS: All trauma deaths that occurred within 30
+ days of hospitalization in five urban university hospitals in
+ India were retrospectively abstracted for demography, mechanism
+ of injury, transfer status, injury description by clinical,
+ investigation and operative findings. Using mixed methods, they
+ were quantitatively stratified by the standardized Injury
+ Severity Score (ISS) into mild (1-8), moderate (9-15), severe
+ (16-25), profound (26-75) ISS categories, and by time to death
+ within 24 h, 7, or 30 days. Using peer-review and Delphi
+ methods, we defined optimal trauma care within the Indian
+ context and evaluated each death for preventability, using the
+ following categories: Preventable (P), Potentially preventable
+ (PP), Non-preventable (NP) and Non-preventable but care could
+ have been improved (NPI). RESULTS: During the 18 month study
+ period, there were 11,671 trauma admissions and 2523 deaths
+ within 30 days (21.6\%). The overall proportion of preventable
+ deaths was 58\%, among 2057 eligible deaths. In patients with a
+ mild ISS score, 71\% of deaths were preventable. In the moderate
+ category, 56\% were preventable, and 60\% in the severe group
+ and 44\% in the profound group were preventable. Traumatic brain
+ injury and burns accounted for the majority of non-preventable
+ deaths. The important areas for improvement in the preventable
+ deaths subset, inadequacies in airway management (14.3\%) and
+ resuscitation with hemorrhage control (16.3\%). System-related
+ issues included lack of protocols, lack of adherence to
+ protocols, pre-hospital delays and delays in imaging.
+ CONCLUSION: Fifty-eight percent of all trauma deaths were
+ classified as preventable. Two-thirds of the deaths with injury
+ severity scores of less than 16 were preventable. This large
+ subgroup of Indian urban trauma patients could possibly be saved
+ by urgent attention and corrective action. Low-cost
+ interventions such as airway management, fluid resuscitation,
+ hemorrhage control and surgical decision-making protocols, were
+ identified as OFI. Establishment of clinical protocols and
+ timely processes of trauma care delivery are the next steps
+ towards improving care.",
+ journal = "BMC Health Serv. Res.",
+ publisher = "Springer Science and Business Media LLC",
+ volume = 17,
+ number = 1,
+ pages = "142",
+ month = feb,
+ year = 2017,
+ language = "en"
+}
+
+@ARTICLE{75,
+ title = "Errors in adult trauma resuscitation: a systematic review",
+ author = "Nikouline, Anton and Quirion, Andrew and Jung, James J and
+ Nolan, Brodie",
+ abstract = "INTRODUCTION: Trauma resuscitation at dedicated trauma centers
+ typically consist of ad-hoc teams performing critical tasks in a
+ time-limited manner. This creates a high stakes environment apt
+ or avoidable errors. Reporting of errors in trauma resuscitation
+ is generally center-dependent and lacks common terminology.
+ METHODS: We conducted a systematic review by searching Ovid
+ Medline, Scopus and Embase from inception to February 24, 2021
+ for errors in adult trauma resuscitation. English studies
+ published after 2001 were included. Studies were assessed by two
+ independent reviewers for meeting inclusion/exclusion criteria.
+ Errors were characterized from the included studies and a
+ summary table was developed. Our review was prospectively
+ registered with the International Prospective Register of
+ Systematic Reviews (PROSPERO) (CRD42020152875). RESULTS: The
+ literature search retrieved 4658 articles with 26 meeting
+ eligibility criteria. Errors were identified by morbidity and
+ mortality rounds or other committee in 62\%, missed injuries on
+ tertiary assessment or radiology review in 12\%, deviations from
+ algorithmic guidelines in 12\% or predefined for chest tube
+ complications, critical incident reporting, aspiration or delays
+ in care. In total there were 39 unique error types identified
+ and divided into 9 categories including Emergency Medical
+ Services handover, airway, assessment of injuries, patient
+ monitoring and access, transfusion/blood related, management of
+ injuries, team communication/dynamics, procedure error and
+ disposition. CONCLUSIONS: Overall, our systematic review
+ identified 39 unique error types in trauma resuscitation.
+ Identifying these errors is imperative in developing systems for
+ improvement of trauma care.",
+ journal = "CJEM",
+ publisher = "Springer Science and Business Media LLC",
+ volume = 23,
+ number = 4,
+ pages = "537--546",
+ month = jul,
+ year = 2021,
+ keywords = "Critical Care; Emergency treatment; Medical errors;
+ Resuscitation; Trauma",
+ language = "en"
+}
+
+
+@article{76,
+ author = "SweTrau årsrapport 2023",
+ year = 2023,
+ url={https://rcsyd.se/swetrau/wp-content/uploads/sites/10/2024/05/Arsrapport-SweTrau-2023.pdf}
+
+}
+
+
+@ARTICLE{77,
+ title = "The injury severity score: a method for describing patients with
+ multiple injuries and evaluating emergency care",
+ author = "Baker, S P and O'Neill, B and Haddon, Jr, W and Long, W B",
+ journal = "J. Trauma",
+ volume = 14,
+ number = 3,
+ pages = "187--196",
+ month = mar,
+ year = 1974,
+ language = "en"
+}
+
+@ARTICLE{78,
+ title = "Characteristics and outcomes in adult patients receiving
+ mechanical ventilation: a 28-day international study",
+ author = "Esteban, Andr{\'e}s and Anzueto, Antonio and Frutos, Fernando
+ and Al{\'\i}a, Inmaculada and Brochard, Laurent and Stewart,
+ Thomas E and Benito, Salvador and Epstein, Scott K and
+ Apeztegu{\'\i}a, Carlos and Nightingale, Peter and Arroliga,
+ Alejandro C and Tobin, Martin J and {Mechanical Ventilation
+ International Study Group}",
+ abstract = "CONTEXT: The outcome of patients receiving mechanical
+ ventilation for particular indications has been studied, but the
+ outcome in a large number of unselected, heterogeneous patients
+ has not been reported. OBJECTIVE: To determine the survival of
+ patients receiving mechanical ventilation and the relative
+ importance of factors influencing survival. DESIGN, SETTING, AND
+ SUBJECTS: Prospective cohort of consecutive adult patients
+ admitted to 361 intensive care units who received mechanical
+ ventilation for more than 12 hours between March 1, 1998, and
+ March 31, 1998. Data were collected on each patient at
+ initiation of mechanical ventilation and daily throughout the
+ course of mechanical ventilation for up to 28 days. MAIN OUTCOME
+ MEASURE: All-cause mortality during intensive care unit stay.
+ RESULTS: Of the 15 757 patients admitted, a total of 5183 (33\%)
+ received mechanical ventilation for a mean (SD) duration of 5.9
+ (7.2) days. The mean (SD) length of stay in the intensive care
+ unit was 11.2 (13.7) days. Overall mortality rate in the
+ intensive care unit was 30.7\% (1590 patients) for the entire
+ population, 52\% (120) in patients who received ventilation
+ because of acute respiratory distress syndrome, and 22\% (115)
+ in patients who received ventilation for an exacerbation of
+ chronic obstructive pulmonary disease. Survival of unselected
+ patients receiving mechanical ventilation for more than 12 hours
+ was 69\%. The main conditions independently associated with
+ increased mortality were (1) factors present at the start of
+ mechanical ventilation (odds ratio [OR], 2.98; 95\% confidence
+ interval [CI], 2.44-3.63; P35 cm H(2)O), and (3) developments
+ occurring over the course of mechanical ventilation (OR, 8.71;
+ 95\% CI, 5.44-13.94; P<.001 for ratio of PaO(2) to fraction of
+ inspired oxygen <100). CONCLUSION: Survival among mechanically
+ ventilated patients depends not only on the factors present at
+ the start of mechanical ventilation, but also on the development
+ of complications and patient management in the intensive care
+ unit.",
+ journal = "JAMA",
+ publisher = "American Medical Association (AMA)",
+ volume = 287,
+ number = 3,
+ pages = "345--355",
+ month = jan,
+ year = 2002,
+ language = "en"
+}
+
+@ARTICLE{79,
+ title = "In-house versus on-call trauma surgeon coverage: A systematic
+ review and meta-analysis",
+ author = "de la Mar, Alexander C J and Lokerman, Robin D and Waalwijk, Job
+ F and Ochen, Yassine and van der Vliet, Quirine M J and
+ Hietbrink, Falco and Houwert, R Marijn and Leenen, Luke P H and
+ van Heijl, Mark",
+ abstract = "BACKGROUND: A rapid trauma response is essential to provide
+ optimal care for severely injured patients. However, it is
+ currently unclear if the presence of an in-house trauma surgeon
+ affects this response during call and influences outcomes. This
+ study compares in-hospital mortality and process-related
+ outcomes of trauma patients treated by a 24/7 in-house versus an
+ on-call trauma surgeon. METHODS: PubMed/Medline, Embase, and
+ CENTRAL databases were searched on the first of November 2020.
+ All studies comparing patients treated by a 24/7 in-house versus
+ an on-call trauma surgeon were considered eligible for
+ inclusion. A meta-analysis of mortality rates including all
+ severely injured patients (i.e., Injury Severity Score of
+ $\geq$16) was performed. Random-effect models were used to pool
+ mortality rates, reported as risk ratios. The main outcome
+ measure was in-hospital mortality. Process-related outcomes were
+ chosen as secondary outcome measures. RESULTS: In total, 16
+ observational studies, combining 64,337 trauma patients, were
+ included. The meta-analysis included 8 studies, comprising 7,490
+ severely injured patients. A significant reduction in mortality
+ rate was found in patients treated in the 24/7 in-house trauma
+ surgeon group compared with patients treated in the on-call
+ trauma surgeon group (risk ratio, 0.86; 95\% confidence
+ interval, 0.78-0.95; p = 0.002; I2 = 0\%). In 10 of 16 studies,
+ at least 1 process-related outcome improved after the in-house
+ trauma surgeon policy was implemented. CONCLUSION: A 24/7
+ in-house trauma surgeon policy is associated with reduced
+ mortality rates for severely injured patients treated at level I
+ trauma centers. In addition, presence of an in-house trauma
+ surgeon during call may improve process-related outcomes. This
+ review recommends implementation of a 24/7 in-house attending
+ trauma surgeon at level I trauma centers. However, the final
+ decision on attendance policy might depend on center and
+ region-specific conditions. LEVEL OF EVIDENCE: Systematic
+ review/meta-analysis, level III.",
+ journal = "J. Trauma Acute Care Surg.",
+ publisher = "Ovid Technologies (Wolters Kluwer Health)",
+ volume = 91,
+ number = 2,
+ pages = "435--444",
+ month = aug,
+ year = 2021,
+ language = "en"
+}
+
+@ARTICLE{80,
+ title = "Time to computed tomography: does this affect trauma patient
+ outcomes? A retrospective analysis at an Australian major trauma
+ centre",
+ author = "Ng, Cedric L H and Kim, Jason and Dobson, Ben and Campbell, Don
+ and Wullschleger, Martin",
+ abstract = "BACKGROUND: Computed tomography (CT) is an essential diagnostic
+ tool for severe multi-trauma patients. International guidelines
+ recommend an optimal time of 1 h from arrival. The aim of this
+ study was to determine the time interval from arrival at the
+ emergency department to CT for all trauma patients and the
+ effects on in-hospital mortality and hospital length of stay.
+ METHODS: Retrospective study of all patients who triggered a
+ trauma call and underwent CT scanning at the Gold Coast
+ University Hospital from January 2016 to December 2017.
+ Exclusion criteria were scans performed at peripheral hospitals
+ or performed more than 5 h after arrival to emergency
+ department. RESULTS: One thousand six hundred and nineteen
+ eligible trauma patients were admitted over the study period and
+ underwent CT scanning. Median time to CT was found to be 43 min.
+ CTs done within 1 h compared to those done after 1 h from
+ emergency department arrival were found to have a higher mean
+ injury severity score (11 $\pm$ 10 versus 9 $\pm$ 9, P = 0.003),
+ a longer mean hospital length of stay (9 $\pm$ 21 versus 7 $\pm$
+ 13 days, P = 0.012) and no difference in mortality rates (2.2\%
+ versus 2.1\%, P = 1.000). Age, injury severity score and
+ intubation status were identified as independent predictors for
+ longer hospital length of stay and higher mortality while time
+ to CT did not. Injury severity score was shown to be an
+ independent predictor of time to CT. CONCLUSION: Our time to CT
+ scanning is well within the timeframe recommended by
+ international guidelines. Early CT scanning may also improve
+ outcomes in severely injured trauma patients.",
+ journal = "ANZ J. Surg.",
+ publisher = "Wiley",
+ volume = 89,
+ number = 11,
+ pages = "1475--1479",
+ month = nov,
+ year = 2019,
+ keywords = "computed tomography; imaging; time; trauma",
+ copyright = "http://onlinelibrary.wiley.com/termsAndConditions\#vor",
+ language = "en"
+}
+
+@ARTICLE{81,
+ title = "Advancing age and trauma: triage destination compliance and
+ mortality in Victoria, Australia",
+ author = "Cox, Shelley and Morrison, Chris and Cameron, Peter and Smith,
+ Karen",
+ abstract = "OBJECTIVE: To describe the association between increasing age,
+ pre-hospital triage destination compliance, and patient outcomes
+ for adult trauma patients. METHODS: A retrospective data review
+ was conducted of adult trauma patients attended by Ambulance
+ Victoria (AV) between 2007 and 2011. AV pre-hospital data was
+ matched to Victorian State Trauma Registry (VSTR) hospital data.
+ Inclusion criteria were adult patients sustaining a traumatic
+ mechanism of injury. Patients sustaining secondary traumatic
+ injuries from non-traumatic causes were excluded. The primary
+ outcomes were destination compliance and in-hospital mortality.
+ These outcomes were evaluated using multivariable logistic
+ regression. RESULTS: There were 326,035 adult trauma patients
+ from 2007 to 2011, and 18.7\% met the AV pre-hospital trauma
+ triage criteria. The VSTR classified 7461 patients as confirmed
+ major trauma (40.9\%>55 years). Whilst the trauma triage
+ criteria have high sensitivity (95.8\%) and a low under-triage
+ rate (4.2\%), the adjusted odds of destination compliance for
+ older trauma patients were between 23.7\% and 41.4\% lower
+ compared to younger patients. The odds of death increased 8\%
+ for each year above age 55 years (OR: 1.08; 95\% CI: 1.07,
+ 1.09). CONCLUSIONS: Despite effective pre-hospital trauma triage
+ criteria, older trauma patients are less likely to be
+ transported to a major trauma service and have poorer outcomes
+ than younger adult trauma patients. It is likely that the
+ benefit of access to definitive trauma care may vary across age
+ groups according to trauma cause, patient history, comorbidities
+ and expected patient outcome. Further research is required to
+ explore how the Victorian trauma system can be optimised to meet
+ the needs of a rapidly ageing population.",
+ journal = "Injury",
+ publisher = "Elsevier BV",
+ volume = 45,
+ number = 9,
+ pages = "1312--1319",
+ month = sep,
+ year = 2014,
+ keywords = "Advancing age; Definitive care; Destination compliance; Major
+ trauma service; Mortality; Pre-hospital; Trauma; Triage",
+ language = "en"
+}
+
+@ARTICLE{82,
+ title = "The Utstein template for uniform reporting of data following
+ major trauma: a joint revision by {SCANTEM}, {TARN}, {DGU-TR}
+ and {RITG}",
+ author = "Ringdal, Kjetil G and Coats, Timothy J and Lefering, Rolf and Di
+ Bartolomeo, Stefano and Steen, Petter Andreas and R{\o}ise, Olav
+ and Handolin, Lauri and Lossius, Hans Morten and {Utstein TCD
+ expert panel}",
+ abstract = "BACKGROUND: In 1999, an Utstein Template for Uniform Reporting
+ of Data following Major Trauma was published. Few papers have
+ since been published based on that template, reflecting a lack
+ of international consensus on its feasibility and use. The aim
+ of the present revision was to further develop the Utstein
+ Template, particularly with a major reduction in the number of
+ core data variables and the addition of more precise definitions
+ of data variables. In addition, we wanted to define a set of
+ inclusion and exclusion criteria that will facilitate uniform
+ comparison of trauma cases. METHODS: Over a ten-month period,
+ selected experts from major European trauma registries and
+ organisations carried out an Utstein consensus process based on
+ a modified nominal group technique. RESULTS: The expert panel
+ concluded that a New Injury Severity Score > 15 should be used
+ as a single inclusion criterion, and five exclusion criteria
+ were also selected. Thirty-five precisely defined core data
+ variables were agreed upon, with further division into core data
+ for Predictive models, System Characteristic Descriptors and for
+ Process Mapping. CONCLUSION: Through a structured consensus
+ process, the Utstein Template for Uniform Reporting of Data
+ following Major Trauma has been revised. This revision will
+ enhance national and international comparisons of trauma
+ systems, and will form the basis for improved prediction models
+ in trauma care.",
+ journal = "Scand. J. Trauma Resusc. Emerg. Med.",
+ publisher = "Springer Nature",
+ volume = 16,
+ number = 1,
+ pages = "7",
+ month = aug,
+ year = 2008,
+ language = "en"
+}
+
+@article{83,
+ author = "Intensive Care Society",
+ title = "Levels of Adult Critical Care Second Edition Consensus Statement",
+ month = march,
+ year = 2021,
+ url={https://www.cc3n.org.uk/uploads/9/8/4/2/98425184/2021-03__levels_of_care_second_edition.pdf}
+
+}
diff --git a/functions/Deceased.R b/functions/Deceased.R
new file mode 100644
index 0000000..922f069
--- /dev/null
+++ b/functions/Deceased.R
@@ -0,0 +1,16 @@
+#Använd tidigare kod för OFI för att få rätt kohort - finns i table1.R
+
+# Convert DateTime_ArrivalAtHospital and DeceasedDate to Date format
+ofi$DateTime_ArrivalAtHospital <- as.Date(ofi$DateTime_ArrivalAtHospital)
+ofi$DeceasedDate <- as.Date(ofi$DeceasedDate)
+# Calculate the difference in days
+ofi$days_to_deceased <- as.numeric(ofi$DeceasedDate - ofi$DateTime_ArrivalAtHospital)
+# Calculate the difference in days
+ofi$days_to_deceased <- as.numeric(ofi$DeceasedDate - ofi$DateTime_ArrivalAtHospital)
+# Count patients deceased within 1 day and 2 days
+deceased_within_1_day <- sum(ofi$days_to_deceased == 1, na.rm = TRUE)
+deceased_within_2_days <- sum(ofi$days_to_deceased <= 2 & ofi$days_to_deceased > 0, na.rm = TRUE)
+
+# Print the results
+cat("Patients deceased within 1 day:", deceased_within_1_day, "\n")
+cat("Patients deceased within 2 days:", deceased_within_2_days, "\n")
diff --git a/functions/example_function.R b/functions/example_function.R
index 7f83d91..8fb9d5d 100644
--- a/functions/example_function.R
+++ b/functions/example_function.R
@@ -1,7 +1,385 @@
-## This file shows how to write a function in R
+#INSTALLING PACKAGES
+#devtools::install_github("martingerdin/noacsr")
+#devtools::install_github("martingerdin/rofi")
+library(dotenv)
+library(noacsr)
+library(rofi)
+noacsr::source_all_functions()
+data <- import_data()
-example_function <- function() {
- ## This is a comment and will not be interpreted by R
- x <- 1 + 2
- return (x)
+merged.data <- merge_data(data)
+merged.data$ofi <- create_ofi(merged.data)
+
+
+#install.packages("dplyr")
+library(dplyr)
+#install.packages("gtsummary")
+library(gtsummary)
+
+
+#FLOWCHART: included/excluded
+#install.packages("Gmisc")
+library(Gmisc, quietly = TRUE)
+library(glue)
+#install.packages("htmlTable")
+library(htmlTable)
+library(grid)
+library(magrittr)
+
+org_cohort <- boxGrob(glue("Total patient cases in trauma quality database",
+ "n = {pop}",
+ pop = txtInt(14022),
+ .sep = "\n"))
+eligible <- boxGrob(glue("Eligible",
+ "n = {pop}",
+ pop = txtInt(1744),
+ .sep = "\n"))
+included <- boxGrob(glue("Included",
+ "n = {incl}",
+ incl = txtInt(1700),
+ .sep = "\n"))
+excluded <- boxGrob(glue("Excluded (n = {tot}):",
+ " - Not admitted to the ICU: {icu}",
+ " - Patients < 15 years: {age}",
+ " - Dead on arrival: {doa}",
+ " - No data on OFI: {ofi}",
+ tot = 12278,
+ icu = 14022-2679,
+ age = 2679-2676,
+ doa = 2676-2670,
+ ofi = 2670-1744,
+ .sep = "\n"),
+ just = "left")
+excluded1 <- boxGrob(glue("Excluded: missing data (n = {x}):",
+ " - Respiratory rate {rr}",
+ " - Systolic blood pressure: {sbp}",
+ " - Glasgow come scale {gcs}",
+ x = 11,
+ rr = 8,
+ sbp = 2,
+ gcs = 1,
+ .sep = "\n"),
+ just = "left")
+
+grid.newpage()
+vert <- spreadVertical(org_cohort,
+ eligible = eligible,
+ included = included)
+
+# Move excluded box
+excluded <- moveBox(excluded,
+ x = 0.8,
+ y = 0.6)
+
+excluded1 <- moveBox(excluded1,
+ x = 0.8,
+ y = 0.3)
+
+# Connect boxes vertically
+for (i in 1:(length(vert) - 1)) {
+ connectGrob(vert[[i]], vert[[i + 1]], type = "vert") %>%
+ print
}
+
+# Connect excluded box horizontally
+connectGrob(vert$eligible, excluded, type = "L")
+connectGrob(vert$included, excluded1, type = "L")
+
+# Print boxes
+vert
+excluded
+excluded1
+
+
+##CLEANING DATA
+subdat <- merged.data %>%
+ select(ofi, pt_Gender, pt_age_yrs, ed_gcs_sum, ed_sbp_value, ed_rr_value,
+ res_survival, pre_intubated, ed_intubated, dt_ed_first_ct, ISS, DateTime_ArrivalAtHospital, FirstTraumaDT_NotDone,
+ host_care_level, hosp_vent_days, pt_asa_preinjury, pre_gcs_sum,
+ pre_rr_value, pre_sbp_value, Fr1.12, ed_rr_rtscat, ed_sbp_rtscat, pre_rr_rtscat, pre_sbp_rtscat, iva_dagar_n)
+
+#Converting subdat$ofi to logical so subset can be used
+subdat$ofi <- ifelse(subdat$ofi == "Yes", TRUE, FALSE)
+
+#Only those in IVA
+iva <- subset(subdat, subset = (host_care_level == 5))
+
+#Removing pt_yrs < 15
+adult <- subset(iva, subset = (pt_age_yrs > 14))
+
+#Deceased on arrival
+alive <- subset(adult, subset = (Fr1.12 == 2 | is.na(Fr1.12)))
+
+#Removing ofi = NA
+ofi <- alive %>% subset(!is.na(ofi))
+
+
+#DEFINING VARIABLES FOR TABLE 1
+#Gender
+ofi$Sex <- ifelse(ofi$pt_Gender == 1, "Male",
+ ifelse(ofi$pt_Gender == 2, "Female",
+ ifelse(ofi$pt_Gender == 999, NA, NA)))
+
+#Age
+ofi$Age <- ofi$pt_age_yrs
+
+#Intubation
+ofi$Intubation1 <- ifelse(ofi$pre_intubated == 1, "Intubation",
+ ifelse(ofi$pre_intubated == 2, "No intubation",
+ ifelse(ofi$pre_intubated == 999, "Unknown",
+ ifelse(ofi$ed_intubated == 1, "Intubation",
+ ifelse(ofi$ed_intubated == 2, "No intubation",
+ ifelse(ofi$ed_intubated == 999, "Unknown", "Unknown"))))))
+
+#Intubation combined with ventilator days
+ofi$Intubation <- ifelse(ofi$Intubation1 == "No intubation", "No intubation",
+ ifelse(ofi$Intubation1 == "Intubation" & ofi$hosp_vent_days == 0, "Intubation 1-3 days",
+ ifelse(ofi$Intubation1 == "Intubation" & ofi$hosp_vent_days %in% 1:7, "Intubation 1-7 days",
+ ifelse(ofi$Intubation1 == "Intubation" & ofi$hosp_vent_days > 7, "Intubation > 7 days",
+ ifelse(ofi$Intubation1 == "Unknown", "Unknown", NA)))))
+
+#Respiratory rate
+ofi$RespiratoryRate <- ifelse(is.na(ofi$ed_rr_value), ofi$pre_rr_value, ofi$ed_rr_value)
+
+#ofi$rts_rr <- ifelse(ofi$RespiratoryRate == 0, 0,
+# ifelse(ofi$RespiratoryRate %in% 1:5, 1,
+# ifelse(ofi$RespiratoryRate %in% 6:9, 2,
+# ifelse(ofi$RespiratoryRate %in% 10:29, 3,
+# ifelse(ofi$RespiratoryRate > 29, 4, NA)))))
+
+#ofi$RespiratoryRate <- ifelse(is.na(ofi$RespiratoryRate) & (ofi$ed_rr_rtscat == 0 | ofi$pre_rr_rtscat) == 0, 0, ofi$RespiratoryRate)
+#ofi$RespiratoryRate <- ifelse(is.na(ofi$RespiratoryRate) & (ofi$ed_rr_rtscat == 1 | ofi$pre_rr_rtscat) == 1, 3.5, ofi$RespiratoryRate)
+#ofi$RespiratoryRate <- ifelse(is.na(ofi$RespiratoryRate) & (ofi$ed_rr_rtscat == 2 | ofi$pre_rr_rtscat) == 2, 7.941176, ofi$RespiratoryRate)
+#ofi$RespiratoryRate <- ifelse(is.na(ofi$RespiratoryRate) & (ofi$ed_rr_rtscat == 3 | ofi$pre_rr_rtscat) == 3, 18.56378, ofi$RespiratoryRate)
+#ofi$RespiratoryRate <- ifelse(is.na(ofi$RespiratoryRate) & (ofi$ed_rr_rtscat == 4 | ofi$pre_rr_rtscat) == 4, 34.09009, ofi$RespiratoryRate)
+
+#mean(na.omit(ofi$RespiratoryRate[ofi$rts_rr == 0]))
+#NaN
+#mean(na.omit(ofi$RespiratoryRate[ofi$rts_rr == 1]))
+#3.5
+#mean(na.omit(ofi$RespiratoryRate[ofi$rts_rr == 2]))
+#7.941176
+#mean(na.omit(ofi$RespiratoryRate[ofi$rts_rr == 3]))
+#18.56378
+#mean(na.omit(ofi$RespiratoryRate[ofi$rts_rr == 4 & ofi$RespiratoryRate != 99]))
+#34.09009
+
+#Systolic blood pressure
+ofi$SystolicBloodPressure <- ifelse(is.na(ofi$ed_sbp_value), ofi$pre_sbp_value, ofi$ed_sbp_value)
+
+#ofi$rts_sbp <- ifelse(ofi$SystolicBloodPressure == 0, 0,
+# ifelse(ofi$SystolicBloodPressure %in% 1:49, 1,
+# ifelse(ofi$SystolicBloodPressure %in% 50:75, 2,
+# ifelse(ofi$SystolicBloodPressure %in% 76:89, 3,
+# ifelse(ofi$SystolicBloodPressure > 89, 4, NA)))))
+
+#mean(na.omit(ofi$SystolicBloodPressure[ofi$rts_sbp == 0]))
+#0
+#mean(na.omit(ofi$SystolicBloodPressure[ofi$rts_sbp == 1]))
+#43.25
+#mean(na.omit(ofi$SystolicBloodPressure[ofi$rts_sbp == 2]))
+#62.09346
+#mean(na.omit(ofi$SystolicBloodPressure[ofi$rts_sbp == 3]))
+#82.21978
+#mean(na.omit(ofi$SystolicBloodPressure[ofi$rts_sbp == 4]))
+#138.2123
+
+#ofi$SystolicBloodPressure <- ifelse(is.na(ofi$SystolicBloodPressure) & (ofi$ed_sbp_rtscat == 0 | ofi$pre_sbp_rtscat == 0), 0, ofi$SystolicBloodPressure)
+#ofi$SystolicBloodPressure <- ifelse(is.na(ofi$SystolicBloodPressure) & (ofi$ed_sbp_rtscat == 1 | ofi$pre_sbp_rtscat == 1), 43.25, ofi$SystolicBloodPressure)
+#ofi$SystolicBloodPressure <- ifelse(is.na(ofi$SystolicBloodPressure) & (ofi$ed_sbp_rtscat == 2 | ofi$pre_sbp_rtscat == 2), 62.09346, ofi$SystolicBloodPressure)
+#ofi$SystolicBloodPressure <- ifelse(is.na(ofi$SystolicBloodPressure) & (ofi$ed_sbp_rtscat == 3 | ofi$pre_sbp_rtscat == 3), 82.21978, ofi$SystolicBloodPressure)
+#ofi$SystolicBloodPressure <- ifelse(is.na(ofi$SystolicBloodPressure) & (ofi$ed_sbp_rtscat == 4 | ofi$pre_sbp_rtscat == 4), 138.2123, ofi$SystolicBloodPressure)
+
+#Glasgow Coma Scale
+ofi$GlasgowComaScale <- ifelse(ofi$ed_gcs_sum == 99, 99,
+ ifelse(ofi$ed_gcs_sum == 999, NA,
+ ifelse(ofi$ed_gcs_sum == 3, 3,
+ ifelse(ofi$ed_gcs_sum == 4, 4,
+ ifelse(ofi$ed_gcs_sum == 5, 5,
+ ifelse(ofi$ed_gcs_sum == 6, 6,
+ ifelse(ofi$ed_gcs_sum == 7, 7,
+ ifelse(ofi$ed_gcs_sum == 8, 8,
+ ifelse(ofi$ed_gcs_sum == 9, 9,
+ ifelse(ofi$ed_gcs_sum == 10, 10,
+ ifelse(ofi$ed_gcs_sum == 11, 11,
+ ifelse(ofi$ed_gcs_sum == 12, 12,
+ ifelse(ofi$ed_gcs_sum == 13, 13,
+ ifelse(ofi$ed_gcs_sum == 14, 14,
+ ifelse(ofi$ed_gcs_sum == 15, 15, NA)))))))))))))))
+
+
+ofi$GlasgowComaScale <- ifelse(is.na(ofi$ed_gcs_sum), ofi$pre_gcs_sum, ofi$ed_gcs_sum)
+
+#RTS score
+ofi$RTSGCS <- ifelse(ofi$GlasgowComaScale %in% 13:15, 4,
+ ifelse(ofi$GlasgowComaScale %in% 9:12, 3,
+ ifelse(ofi$GlasgowComaScale %in% 6:8, 2,
+ ifelse(ofi$GlasgowComaScale %in% 4:5, 1,
+ ifelse(ofi$GlasgowComaScale == 3, 0,
+ ifelse(ofi$GlasgowComaScale == 99, 0, NA))))))
+
+ofi$RTSSBP <- ifelse(ofi$SystolicBloodPressure > 89, 4,
+ ifelse(ofi$SystolicBloodPressure %in% 76:89, 3,
+ ifelse(ofi$SystolicBloodPressure %in% 50:75, 2,
+ ifelse(ofi$SystolicBloodPressure %in% 1:49, 1,
+ ifelse(ofi$SystolicBloodPressure == 0, 0,
+ ifelse(ofi$SystolicBloodPressure == 99, 0, NA))))))
+
+ofi$RTSRR <- ifelse(ofi$RespiratoryRate > 29, 4,
+ ifelse(ofi$RespiratoryRate %in% 10:29, 3,
+ ifelse(ofi$RespiratoryRate %in% 6:9, 2,
+ ifelse(ofi$RespiratoryRate %in% 1:5, 1,
+ ifelse(ofi$RespiratoryRate == 0, 0,
+ ifelse(ofi$RespiratoryRate == 99, 0, NA))))))
+
+ofi$RTS <- (0.9368*ofi$RTSGCS + 0.7326*ofi$RTSSBP + 0.2908*ofi$RTSRR)
+
+
+
+
+#Working hours: arrived between 8 am and 5 pm
+ofi$hour <- format(ofi$DateTime_ArrivalAtHospital, "%H")
+ofi$WorkingHoursTF <- ifelse(ofi$hour == "08" | ofi$hour == "09" | ofi$hour == "10" | ofi$hour == "11" | ofi$hour == "12" | ofi$hour == "13" | ofi$hour == "14" | ofi$hour == "15" | ofi$hour == "16", TRUE, FALSE)
+ofi$WorkingHours <- ifelse(ofi$WorkingHoursTF == TRUE, "Yes",
+ ifelse(ofi$WorkingHoursTF == FALSE, "No", NA))
+
+#Weekend: arrived on Saturday or Sunday
+ofi$Weekdays <- weekdays(ofi$DateTime_ArrivalAtHospital)
+ofi$WeekendTF <- ifelse(ofi$Weekdays == "Saturday" | ofi$Weekdays == "Sunday", TRUE, FALSE)
+ofi$Weekend <- ifelse(ofi$WeekendTF == TRUE, "Yes",
+ ifelse(ofi$WeekendTF == FALSE, "No", NA))
+
+#Duty shift
+ofi$OnDuty <- ifelse(ofi$Weekend == "Yes", 1,
+ ifelse(ofi$WorkingHours == "No", 1, 0))
+
+#Time to first CT
+ofi$TimeFCT <- ofi$dt_ed_first_ct
+
+#Days in the ICU
+ofi$daysinICU <- ifelse(ofi$iva_dagar_n < 7, "< 7 days",
+ ifelse(ofi$iva_dagar_n > 7, "> 7 days", NA))
+
+#Pt ASA preinjury
+ofi$ASApreinjury <- ifelse(ofi$pt_asa_preinjury == 1 | ofi$pt_asa_preinjury == 2, "ASA 1-2",
+ ifelse(ofi$pt_asa_preinjury %in% 3:7, "ASA 3-7",
+ ifelse(ofi$pt_asa_preinjury == 999, NA, NA)))
+
+#Survival after 30 days
+ofi$Survival <- ifelse(ofi$res_survival == 1, "Dead",
+ ifelse(ofi$res_survival == 2, "Alive",
+ ifelse(ofi$res_survival == 999, NA, NA)))
+
+
+#OFI
+ofi$OpportunityForImprovement <- ifelse(ofi$ofi == TRUE, "Opportunity for improvement",
+ ifelse(ofi$ofi == FALSE, "No opportunity for improvement", NA))
+
+ofi$OpportunityForImprovement1 <- ifelse(ofi$OpportunityForImprovement == "Opportunity for improvement", 1,
+ ifelse(ofi$OpportunityForImprovement == "No opportunity for improvement", 0, NA))
+
+#TABLE 1: Sample characteristics
+#Creating new table with defined data
+library(dplyr)
+library(gt)
+library(forcats)
+library(gtsummary)
+
+table1 <- ofi %>%
+ select(Sex, Age, Intubation, RTS, ISS, TimeFCT, OnDuty, daysinICU,
+ ASApreinjury, Survival, OpportunityForImprovement)
+
+
+table1$Intubation <- ifelse(is.na(table1$Intubation), "Unknown", table1$Intubation)
+table1 <- na.omit(table1)
+
+table2 <- table1 %>%
+ mutate(Intubation = factor(Intubation, levels = c("No intubation", "Intubation 1-7 days", "Intubation > 7 days", "Unknown"))) %>%
+ tbl_summary(by = OpportunityForImprovement,
+ type = list(OnDuty ~ "dichotomous"),
+ label = list(RTS = "Revised Trauma Score",
+ ISS = "Injury Severity Score",
+ TimeFCT = "Time to first CT",
+ daysinICU = "Days in the ICU",
+ OnDuty = "On duty",
+ ASApreinjury = "ASA preinjury"),
+ statistic = list(
+ all_continuous() ~ "{mean} ({sd})",
+ all_categorical() ~ "{n} ({p}%)"
+ ),
+ missing = "ifany",
+ missing_text = "Missing",
+ digits = all_continuous() ~ 2
+ ) %>%
+ modify_table_styling(
+ columns = label,
+ rows = label == "On duty",
+ footnote = "Arrival at the hospital on Saturday or Sunday, or arrival at the hospital before 8 am or after 5 pm"
+ ) %>%
+ bold_labels() %>%
+ add_overall(last = TRUE) %>%
+ modify_caption("Table 1. Sample Characteristics
") %>%
+ print()
+
+#TABLE 2: Adjusted and unadjusted logistic regression
+# Data Preparation
+tablereg <- ofi %>%
+ select(Sex, Age, Intubation, RTS, ISS,TimeFCT, OnDuty, daysinICU, TimeFCT,
+ ASApreinjury, Survival, OpportunityForImprovement1)
+
+tablereg$Intubation <- ifelse(is.na(tablereg$Intubation), "Unknown", table1$Intubation)
+tablereg$Intubation <- fct_relevel(tablereg$Intubation, "No intubation", "Intubation 1-7 days", "Intubation > 7 days", "Unknown")
+
+
+# Unadjusted Table
+table3a <- tbl_uvregression(data = tablereg,
+ method = glm,
+ y = OpportunityForImprovement1,
+ method.args = list(family = binomial),
+ label = list(
+ RTS = "Revised Trauma Score",
+ daysinICU = "Days in the ICU",
+ TimeFInt = "Time to first intervention",
+ ASApreinjury = "ASA preinjury",
+ OnDuty = "On duty",
+ TimeFCT = "Time to first CT"
+ )) %>%
+ bold_labels() %>%
+ bold_p(t = 0.05)
+
+ #print(table3a)
+
+# Adjusted Table
+#Defining the data in data frame as factors to get output levels instead of NULL
+#tablereg$OpportunityForImprovement1 <- factor(tablereg$OpportunityForImprovement1)
+#tablereg$Sex <- factor(tablereg$Sex)
+#tablereg$Age <- factor(tablereg$Age)
+#tablereg$Intubation <- factor(tablereg$Intubation)
+#tablereg$RTS <- factor(tablereg$RTS)
+#tablereg$ISS <- factor(tablereg$ISS)
+#tablereg$OnDuty <- factor(tablereg$OnDuty)
+#tablereg$TimeFCT <- factor(tablereg$TimeFCT)
+#tablereg$ASApreinjury <- factor(tablereg$ASApreinjury)
+#tablereg$Survival <- factor(tablereg$Survival)
+
+#Creating linear regression
+adjusted_table <- glm(OpportunityForImprovement1 ~ Sex + Age + Intubation + RTS + ISS + OnDuty + daysinICU + TimeFCT + ASApreinjury + Survival, family = binomial, data = tablereg)
+
+table3b <- tbl_regression(adjusted_table,
+ label = list(RTS = "Revised Trauma Score",
+ daysinICU = "Days in the ICU",
+ ASApreinjury = "ASA preinjury",
+ TimeFCT = "Time to first CT")) %>%
+ bold_labels() %>%
+ bold_p(t = 0.05)
+
+ # print(table3b)
+
+# Merging Tables
+table3_merge <- tbl_merge(tbls = list(table3a, table3b),
+ tab_spanner = c("**Unadjusted**", "**Adjusted**")) %>%
+modify_caption("Table 2. Unadjusted and adjusted logistic regression analyses of associations between patient level factors and opportunities for improvement
")
+
+print(table3_merge)
+
+
\ No newline at end of file
diff --git a/functions/flowchart.R b/functions/flowchart.R
new file mode 100644
index 0000000..33dbcdc
--- /dev/null
+++ b/functions/flowchart.R
@@ -0,0 +1,75 @@
+
+#FLOWCHART: included/excluded
+#install.packages("Gmisc")
+library(Gmisc, quietly = TRUE)
+library(glue)
+#install.packages("htmlTable")
+library(htmlTable)
+library(grid)
+library(magrittr)
+
+org_cohort <- boxGrob(glue("Total patient cases in trauma quality database",
+ "n = {pop}",
+ pop = txtInt(14022),
+ .sep = "\n"))
+eligible <- boxGrob(glue("Eligible",
+ "n = {pop}",
+ pop = txtInt(1742),
+ .sep = "\n"))
+included <- boxGrob(glue("Included (n = {incl}):",
+ "- OFI: {ofi1}",
+ "- No OFI: {ofi2}",
+ ofi1 = 143,
+ ofi2 = 1306,
+ incl = txtInt(1449),
+ .sep = "\n"),
+ just = "left")
+excluded <- boxGrob(glue("Excluded (n = {tot}):",
+ " - Not admitted to the ICU: {icu}",
+ " - Patients < 15 years: {age}",
+ " - Dead on arrival: {doa}",
+ " - No data on OFI: {ofi}",
+ tot = 12278,
+ icu = 14022-2679,
+ age = 2679-2676,
+ doa = 2676-2670,
+ ofi = 2670-1742,
+ .sep = "\n"),
+ just = "left")
+excluded1 <- boxGrob(glue("Excluded: missing data (n = {x})",
+ x = 1742 - 1449,
+ .sep = "\n"),
+ just = "left")
+
+grid.newpage()
+vert <- spreadVertical(org_cohort,
+ eligible = eligible,
+ included = included)
+
+# Move excluded box
+excluded <- moveBox(excluded,
+ x = 0.8,
+ y = 0.7)
+
+excluded1 <- moveBox(excluded1,
+ x = 0.8,
+ y = 0.4)
+
+#Defining small arrow
+small_arrow <- arrow(length = unit(0.1, "inches"), type = "closed")
+
+# Connect boxes vertically
+for (i in 1:(length(vert) - 1)) {
+ connectGrob(vert[[i]], vert[[i + 1]], type = "vert", arrow = small_arrow) %>%
+ print
+}
+
+# Connect excluded box horizontally
+connectGrob(vert$eligible, excluded, type = "L", arrow = small_arrow)
+connectGrob(vert$included, excluded1, type = "L", arrow = small_arrow)
+
+# Print boxes
+vert
+excluded
+excluded1
+
diff --git a/functions/ofi_categories.R b/functions/ofi_categories.R
new file mode 100644
index 0000000..17883b8
--- /dev/null
+++ b/functions/ofi_categories.R
@@ -0,0 +1,201 @@
+
+# Load necessary libraries
+library(dplyr)
+library(stringr)
+library(gtsummary)
+library(gt)
+
+# Select different OFIs
+ofi_categories <- ofi %>%
+ select(Problemomrade_.FMP, Sex, Age, Intubation, RTS, ISS, TimeFCT, OnDuty, daysinICU,
+ ASApreinjury, Survival, OpportunityForImprovement)
+
+# Translating OFIs and identifying NAs
+ofi_categories$Problemomrade_.FMP <- str_replace_all(
+ str_to_lower(ofi_categories$Problemomrade_.FMP),
+ c(
+ "ok" = NA_character_,
+ "nej" = NA_character_,
+ "inget problemområde" = NA_character_,
+ "föredömligt handlagd" = NA_character_,
+ "dokumetation" = "Documentation",
+ "handläggning" = "Patient management",
+ "logistik/teknik" = "Logistics/technical",
+ "lång tid till op" = "Delay to surgery",
+ "lång tid till dt" = "Delay to CT",
+ "kompetens brist" = "Competence",
+ "kommunikation" = "Communication",
+ "kommunikation+missad skada" = "Communication + missed injury",
+ "handläggning/logistik" = "Patient management/logistics",
+ "handläggning+dokumentation" = "Patient management + documentation",
+ "handläggning prehosp" = "Prehospital management",
+ "traumakriterier/styrning" = "Trauma criteria/guidelines",
+ "tertiär survey" = "Tertiary survey",
+ "bristande rutin" = "Inadequate routine",
+ "annat" = "Other",
+ "missad skada" = "Missed injury",
+ "resurs" = "Resources",
+ "triage på akm" = "Triage in the ED",
+ "triage på akutmottagningen" = "Triage in the ED",
+ "vårdnivå" = "Level of care",
+ "vårdnivå\\+\r\nmissade skador" = "Level of care + missed injury",
+ "handläggning\r\ndokumentation" = "Patient management + documentation"
+ )
+)
+
+ofi_categories$Problemomrade_.FMP <- ifelse(ofi_categories$Problemomrade_.FMP == "Patient management/logistik", "Patient management/logistics", ofi_categories$Problemomrade_.FMP)
+ofi_categories$Problemomrade_.FMP <- ifelse(ofi_categories$Problemomrade_.FMP == "Level of care+ missade skador", "Level of care + missed injuries", ofi_categories$Problemomrade_.FMP)
+
+# Assign broad categories based on translated OFIs
+ofi_categories <- ofi_categories %>%
+ mutate(
+ BroadCategory = case_when(
+ Problemomrade_.FMP %in% c("Missed injury", "Tertiary survey") ~ "Missed diagnosis",
+ Problemomrade_.FMP %in% c("Delay to surgery", "Delay to CT") ~ "Delay in treatment",
+ Problemomrade_.FMP %in% c("Triage in the ED", "Level of care", "Patient management", "Communication") ~ "Clinical judgement error",
+ Problemomrade_.FMP %in% c("Documentation") ~ "Documentation Issues",
+ Problemomrade_.FMP %in% c("Technical error") ~ "Technical error",
+ Problemomrade_.FMP %in% c("Trauma criteria/guidelines", "Inadequate routine") ~ "Inadequate protocols",
+ Problemomrade_.FMP %in% c("Competence", "Resources", "Logistics/technical") ~ "Inadequate resources",
+ Problemomrade_.FMP %in% c("Other", "Patient management/logistics", "Prehospital management", "Level of care + missed injury") ~ "Other errors",
+ TRUE ~ "Other errors"
+ )
+ )
+
+# Handle NA values in the Intubation column
+ofi_categories <- ofi_categories %>%
+ mutate(Intubation = ifelse(is.na(Intubation), "Unknown", Intubation))
+
+# Remove rows where any variable other than Problemomrade_.FMP is NA
+clean <- ofi_categories %>%
+ filter(if_all(c(Sex, Age, Intubation, RTS, ISS, TimeFCT, OnDuty, daysinICU, ASApreinjury, Survival, OpportunityForImprovement), ~ !is.na(.)))
+
+# Create a combined column for BroadCategory and Problemomrade_.FMP
+clean <- clean %>%
+ mutate(Category = paste(BroadCategory, Problemomrade_.FMP, sep = ": ")) %>%
+ select(Category, OpportunityForImprovement)
+
+# Filter to include only rows with Opportunity for Improvement
+clean <- clean %>%
+ filter(OpportunityForImprovement == "Opportunity for improvement")
+
+# Create a summary table
+table_ofic <- clean %>%
+ tbl_summary(by = OpportunityForImprovement,
+ statistic = list(
+ all_categorical() ~ "{n}"
+ ),
+ missing = "ifany",
+ missing_text = "Missing",
+ digits = all_continuous() ~ 0
+ ) %>%
+ bold_labels() %>%
+ modify_caption("Table 1. Opportunities for improvement categories on the patient cohort of the study
") %>%
+ modify_table_styling(
+ columns = label,
+ rows = label == "Clinical judgement error: Communication",
+ footnote = "[active] failures in communication with parties outside the treating team, e.g. consultants, receiving unit etc., did not consult neurosurgeon before transfer"
+ ) %>%
+ modify_table_styling(
+ columns = label,
+ rows = label == "Clinical judgement error: Level of care",
+ footnote = "pt transferred to inappropriate level of care given available information/protocols (not due to lack of resources), pt transferred to IMCU rather than ICU"
+ ) %>%
+ modify_table_styling(
+ columns = label,
+ rows = label == "Clinical judgement error: Patient management",
+ footnote = "[active] failure to perform the appropriate exams and interventions in appropriate order and time given available information/protocols (not due to lack of resources) sent pt to CT with inadequately secured airway"
+ ) %>%
+ modify_table_styling(
+ columns = label,
+ rows = label == "Clinical judgement error: Triage in the ED",
+ footnote = "[active] pt assigned inappropriate level given available information/protocols, failure to activate trauma team, ED = emergency department"
+ ) %>%
+ modify_table_styling(
+ columns = label,
+ rows = label == "Delay in treatment: Delay to CT",
+ footnote = "[active] failure to perform computed tomography when indicated/in protocols"
+ ) %>%
+ modify_table_styling(
+ columns = label,
+ rows = label == "Delay in treatment: Delay to surgery",
+ footnote = "[active] failure to move pt to OR within an appropriate time, let's monitor this pt further before deciding on surgery"
+ ) %>%
+ modify_table_styling(
+ columns = label,
+ rows = label == "Inadequate protocols: Trauma criteria/guidelines",
+ footnote = "[passive] maladapted protocols/guidelines, the trauma team activation protocol should assign this pt level 1"
+ ) %>%
+ modify_table_styling(
+ columns = label,
+ rows = label == "Inadequate resources: Logistics/technical",
+ footnote = "[passive] required equipment was unavailable/out of service, IT system was down making pt records unavailable"
+ ) %>%
+ modify_table_styling(
+ columns = label,
+ rows = label == "Inadequate resources: Resources",
+ footnote = "[passive] insufficient available resources (not active errors such as failure to activate backup team), no OR was available"
+ ) %>%
+ modify_table_styling(
+ columns = label,
+ rows = label == "Missed diagnosis: Missed injury",
+ footnote = "[active] failure to identify injury that should have been identified given available information, reasonable clinical judgment and protocols (includes missed injuries due to skipped exam"
+ ) %>%
+ modify_table_styling(
+ columns = label,
+ rows = label == "Missed diagnosis: Tertiary survey",
+ footnote = "[active] failure to perform tertiary survey within appropriate time when indicated/in protocols"
+ ) %>%
+ modify_table_styling(
+ columns = label,
+ rows = label == "Other errors: Level of care+ missade skador",
+ footnote = "[active] pt transferred to inappropriate level of care given available information/protocols (not due to lack of resources), pt transferred to IMCU rather than ICU"
+ ) %>%
+ modify_table_styling(
+ columns = label,
+ rows = label == "Other errors: Patient management/logistics",
+ footnote = "[other] name implies interaction with systems"
+ ) %>%
+ modify_table_styling(
+ columns = label,
+ rows = label == "Category",
+ footnote = "Definitions of categories of opportunities for improvements used"
+ ) %>%
+ print()
+
+
+
+
+
+# lookup to categorize ofi
+ # MISSED INJURY
+ # Missed injury (active failure): failure to identify injury despite adequate information. Includes misinterpretation of imagery etc.
+ # Examples: surveys, radiology, explorative procedures
+ # DELAYED TREATMENT
+ # Delay in treatment (active failure): inappropriate delay from arrival/assessment to treatment causing pt harm. Does **not** include delays due to clinical judgment errors, i.e. "I don't think a DT is needed".
+ # Examples: failure to activate trauma team, delay in hemorrhage control, delay in moving to OR, failure to activate backup surgical team, ddelay in moving from ED to OR, OR unavailable, ddelay to angio, delay in performing intervention, delay in intubation, delay due to interhospital transfer, delay in interhospital transfer, initial transfer to wrong department
+ # CLINICAL JUDGMENT ERROR
+ # Clinical judgment error (active failure): inappropriate plan of actions/management, given available information, causing pt harm.
+ # Examples: Inadequate monitoring, medication errors, wrong level of care/unit, wrong protocol/procedure applied, wrong treatment
+ # TECHNICAL ERROR (SKILL)
+ # Technical error (active failure): inadequate performance of intervention/procedure causing pt harm.
+ # INADEQUATE PROTOCOLS
+ # Inadequate protocols (passive failure): inadequate protocols/guidelines etc. leading to pt harm.
+ # "Inadequate protocols" = c(
+ # "bristande rutin" # [passive] no/maladapted formalized routine for type cases, "no guidelines for pediatric multitrauma"
+ # INADEQUATE RESOURCES
+ # Inadequate resources (passive failure): lack of resources leading to pt harm.
+ # Examples: lack of trauma/backup team, lack of equipment/training etc.
+ # "Inadequate resources" = c(
+ # "kompetens brist", # [passive] required competence not available, "no neurosurgeon on site/available"
+# ),
+ # OTHER
+ # Anything else.
+ # "Other errors" = c(
+ # "annat", # [other]
+ # "handläggning prehosp", # [other] unclear
+ # "neurokirurg" # [other] unclear
+
+
+
+
diff --git a/functions/table1.R b/functions/table1.R
new file mode 100644
index 0000000..6571475
--- /dev/null
+++ b/functions/table1.R
@@ -0,0 +1,258 @@
+
+#INSTALLING PACKAGES
+#devtools::install_github("martingerdin/noacsr")
+#devtools::install_github("martingerdin/rofi")
+library(dotenv)
+library(noacsr)
+library(rofi)
+#noacsr::source_all_functions()
+data <- import_data()
+
+merged.data <- merge_data(data)
+merged.data$ofi <- create_ofi(merged.data)
+
+#install.packages("dplyr")
+library(dplyr)
+#install.packages("gtsummary")
+library(gtsummary)
+library(tidyverse)
+library(officer)
+
+
+#FLOWCHART: included/excluded
+#install.packages("Gmisc")
+library(Gmisc, quietly = TRUE)
+library(glue)
+#install.packages("htmlTable")
+library(htmlTable)
+library(grid)
+library(magrittr)
+
+##CLEANING DATA
+subdat <- merged.data %>%
+ select(ofi, pt_Gender, pt_age_yrs, ed_gcs_sum, ed_sbp_value, ed_rr_value,
+ res_survival, pre_intubated, ed_intubated, dt_ed_first_ct, ISS, DateTime_ArrivalAtHospital, FirstTraumaDT_NotDone,
+ host_care_level, hosp_vent_days, pt_asa_preinjury, pre_gcs_sum,
+ pre_rr_value, pre_sbp_value, Fr1.12, ed_rr_rtscat, ed_sbp_rtscat, pre_rr_rtscat, pre_sbp_rtscat, iva_dagar_n, Problemomrade_.FMP, DeceasedDate, Deceased)
+
+#Converting subdat$ofi to logical so subset can be used
+subdat$ofi <- ifelse(subdat$ofi == "Yes", TRUE, FALSE)
+
+#Only those in IVA
+iva <- subset(subdat, subset = (host_care_level == 5))
+
+#Removing pt_yrs < 15
+adult <- subset(iva, subset = (pt_age_yrs > 14))
+
+#Deceased on arrival
+alive <- subset(adult, subset = (Fr1.12 == 2 | is.na(Fr1.12)))
+
+#Removing ofi = NA
+ofi <- alive %>% subset(!is.na(ofi))
+
+
+#DEFINING VARIABLES FOR TABLE 1
+#Gender
+ofi$Sex <- ifelse(ofi$pt_Gender == 1, "Male",
+ ifelse(ofi$pt_Gender == 2, "Female",
+ ifelse(ofi$pt_Gender == 999, NA, NA)))
+
+#Age
+ofi$Age <- ofi$pt_age_yrs
+
+#Intubation
+#ofi$Intubation1 <- ifelse(ofi$pre_intubated == 1, "Intubation",
+# ifelse(ofi$pre_intubated == 2, "Not intubated",
+# ifelse(ofi$pre_intubated == 999, "Unknown",
+# ifelse(ofi$ed_intubated == 1, "Intubation",
+# ifelse(ofi$ed_intubated == 2, "Not intubated",
+# ifelse(ofi$ed_intubated == 999, "Unknown", "Unknown"))))))
+
+#Intubation combined with ventilator days
+#ofi$Intubation <- ifelse(ofi$Intubation1 == "Not intubated", "Not intubated",
+# ifelse(ofi$Intubation1 == "Intubation" & ofi$hosp_vent_days %in% 0:2, "Mechanical ventilation 0-2 days",
+## ifelse(ofi$Intubation1 == "Intubation" & ofi$hosp_vent_days %in% 3:7, "Mechanical ventilation 3-7 days",
+# ifelse(ofi$Intubation1 == "Intubation" & ofi$hosp_vent_days > 7, "Mechanical ventilation > 7 days",
+# ifelse(ofi$Intubation1 == "Unknown", "Unknown", "Unknown")))))
+
+ofi$Intubation <- ifelse(ofi$hosp_vent_days %in% 0:2, "Mechanical ventilation 0-2 days",
+ ifelse(ofi$hosp_vent_days %in% 3:7, "Mechanical ventilation 3-7 days",
+ ifelse(ofi$hosp_vent_days > 7, "Mechanical ventilation > 7 days", "Unknown")))
+
+
+#Kontinuerlig variabel mechanical ventilation
+ofi$mechanical.ventilation.cont <- ifelse(ofi$hosp_vent_days > 0, ofi$hosp_vent_days, "Unknown")
+
+
+#Respiratory rate
+ofi$RespiratoryRate <- ifelse(is.na(ofi$ed_rr_value), ofi$pre_rr_value, ofi$ed_rr_value)
+
+#Systolic blood pressure
+ofi$SystolicBloodPressure <- ifelse(is.na(ofi$ed_sbp_value), ofi$pre_sbp_value, ofi$ed_sbp_value)
+
+#Glasgow Coma Scale
+ofi$GlasgowComaScale <- ifelse(ofi$ed_gcs_sum == 99, 99,
+ ifelse(ofi$ed_gcs_sum == 999, NA,
+ ifelse(ofi$ed_gcs_sum == 3, 3,
+ ifelse(ofi$ed_gcs_sum == 4, 4,
+ ifelse(ofi$ed_gcs_sum == 5, 5,
+ ifelse(ofi$ed_gcs_sum == 6, 6,
+ ifelse(ofi$ed_gcs_sum == 7, 7,
+ ifelse(ofi$ed_gcs_sum == 8, 8,
+ ifelse(ofi$ed_gcs_sum == 9, 9,
+ ifelse(ofi$ed_gcs_sum == 10, 10,
+ ifelse(ofi$ed_gcs_sum == 11, 11,
+ ifelse(ofi$ed_gcs_sum == 12, 12,
+ ifelse(ofi$ed_gcs_sum == 13, 13,
+ ifelse(ofi$ed_gcs_sum == 14, 14,
+ ifelse(ofi$ed_gcs_sum == 15, 15, NA)))))))))))))))
+
+
+ofi$GlasgowComaScale <- ifelse(is.na(ofi$ed_gcs_sum), ofi$pre_gcs_sum, ofi$ed_gcs_sum)
+
+#RTS score
+ofi$RTSGCS <- ifelse(ofi$GlasgowComaScale %in% 13:15, 4,
+ ifelse(ofi$GlasgowComaScale %in% 9:12, 3,
+ ifelse(ofi$GlasgowComaScale %in% 6:8, 2,
+ ifelse(ofi$GlasgowComaScale %in% 4:5, 1,
+ ifelse(ofi$GlasgowComaScale == 3, 0,
+ ifelse(ofi$GlasgowComaScale == 99, 0, NA))))))
+
+ofi$RTSSBP <- ifelse(ofi$SystolicBloodPressure > 89, 4,
+ ifelse(ofi$SystolicBloodPressure %in% 76:89, 3,
+ ifelse(ofi$SystolicBloodPressure %in% 50:75, 2,
+ ifelse(ofi$SystolicBloodPressure %in% 1:49, 1,
+ ifelse(ofi$SystolicBloodPressure == 0, 0,
+ ifelse(ofi$SystolicBloodPressure == 99, 0, NA))))))
+
+ofi$RTSRR <- ifelse(ofi$RespiratoryRate %in% 10:29, 4,
+ ifelse(ofi$RespiratoryRate >29, 3,
+ ifelse(ofi$RespiratoryRate %in% 6:9, 2,
+ ifelse(ofi$RespiratoryRate %in% 1:5, 1,
+ ifelse(ofi$RespiratoryRate == 0, 0,
+ ifelse(ofi$RespiratoryRate == 99, 0, NA))))))
+
+ofi$RTS <- (0.9368*ofi$RTSGCS + 0.7326*ofi$RTSSBP + 0.2908*ofi$RTSRR)
+#ofi$RTS <- (ofi$RTSGCS + ofi$RTSSBP + ofi$RTSRR)
+
+
+
+#Working hours: arrived between 8 am and 5 pm
+ofi$hour <- format(ofi$DateTime_ArrivalAtHospital, "%H")
+ofi$WorkingHoursTF <- ifelse(ofi$hour == "08" | ofi$hour == "09" | ofi$hour == "10" | ofi$hour == "11" | ofi$hour == "12" | ofi$hour == "13" | ofi$hour == "14" | ofi$hour == "15" | ofi$hour == "16", TRUE, FALSE)
+ofi$WorkingHours <- ifelse(ofi$WorkingHoursTF == TRUE, "Yes",
+ ifelse(ofi$WorkingHoursTF == FALSE, "No", NA))
+
+#Weekend: arrived on Saturday or Sunday
+ofi$Weekdays <- weekdays(ofi$DateTime_ArrivalAtHospital)
+ofi$WeekendTF <- ifelse(ofi$Weekdays == "Saturday" | ofi$Weekdays == "Sunday", TRUE, FALSE)
+ofi$Weekend <- ifelse(ofi$WeekendTF == TRUE, "Yes",
+ ifelse(ofi$WeekendTF == FALSE, "No", NA))
+
+#Duty shift
+ofi$OnDuty <- ifelse(ofi$Weekend == "Yes", 1,
+ ifelse(ofi$WorkingHours == "No", 1, 0))
+
+#Time to first CT
+ofi$TimeFCT <- ofi$dt_ed_first_ct
+
+#Days in the ICU
+ofi$daysinICU <- ifelse(ofi$iva_dagar_n < 7 | ofi$iva_dagar_n == 7, "≤ 7 days",
+ ifelse(ofi$iva_dagar_n > 7, "> 7 days", NA))
+
+#Kontinuerlig variabel ICU LOS
+ofi$icu.los.cont <- ofi$iva_dagar_n
+
+#Pt ASA preinjury
+ofi$ASApreinjury <- ifelse(ofi$pt_asa_preinjury == 1 | ofi$pt_asa_preinjury == 2, "ASA 1-2",
+ ifelse(ofi$pt_asa_preinjury %in% 3:5, "ASA 3-5",
+ ifelse(ofi$pt_asa_preinjury == 999, NA, NA)))
+
+#Survival after 30 days
+ofi$Survival <- ifelse(ofi$res_survival == 1, "Dead",
+ ifelse(ofi$res_survival == 2, "Alive",
+ ifelse(ofi$res_survival == 999, NA, NA)))
+
+#Jonatans version
+ofi$Deceased <- as.logical(as.character(ofi$Deceased))
+ofi$DiedWithin24Hours <- ifelse(ofi$Deceased == FALSE, FALSE, NA)
+ofi$DateTime_ArrivalAtHospital <- as.Date(ofi$DateTime_ArrivalAtHospital)
+ofi$DeceasedDate <- as.Date(ofi$DeceasedDate)
+time_diff <- as.numeric(ofi$DeceasedDate - ofi$DateTime_ArrivalAtHospital)
+ofi$DiedWithin24Hours <- ifelse(
+ ofi$Deceased == TRUE & time_diff <= 1, TRUE,
+ ifelse(ofi$Deceased == TRUE & time_diff > 1, FALSE,
+ ofi$DiedWithin24Hours))
+
+#24-h mortality
+# Konvertera datumen till Date-format
+#ofi$DateTime_ArrivalAtHospital <- as.Date(ofi$DateTime_ArrivalAtHospital)
+#ofi$DeceasedDate <- as.Date(ofi$DeceasedDate)
+
+# Beräkna skillnaden i dagar mellan ankomst och avliden datum
+#ofi$days_to_deceased <- as.numeric(ofi$DeceasedDate - ofi$DateTime_ArrivalAtHospital)
+
+# Klassificera patienter som "Dead" eller "Alive" baserat på villkoren
+#ofi$Mortality <- ifelse(!is.na(ofi$days_to_deceased) & ofi$days_to_deceased <= 1, "Dead",
+ # ifelse(!is.na(ofi$days_to_deceased) & ofi$days_to_deceased > 1, "Alive",
+# ifelse(is.na(ofi$DeceasedDate) & ofi$Deceased == FALSE, "Alive", NA)))
+
+
+
+#OFI
+ofi$OpportunityForImprovement <- ifelse(ofi$ofi == TRUE, "Opportunity for improvement",
+ ifelse(ofi$ofi == FALSE, "No opportunity for improvement", NA))
+
+ofi$OpportunityForImprovement1 <- ifelse(ofi$OpportunityForImprovement == "Opportunity for improvement", 1,
+ ifelse(ofi$OpportunityForImprovement == "No opportunity for improvement", 0, NA))
+
+
+table1 <- ofi %>%
+ select(Sex, Age, Intubation, mechanical.ventilation.cont, RTS, ISS, TimeFCT, OnDuty, daysinICU,
+ icu.los.cont, ASApreinjury, Survival, DiedWithin24Hours, OpportunityForImprovement)
+
+
+table1 <- table1 %>%
+ filter(if_all(.cols = -c(DiedWithin24Hours, mechanical.ventilation.cont, icu.los.cont), .fns = ~ !is.na(.)))
+
+
+table2 <- table1 %>%
+ mutate(Intubation = factor(Intubation, levels = c("Mechanical ventilation 0-2 days", "Mechanical ventilation 3-7 days", "Mechanical ventilation > 7 days"))) %>%
+ tbl_summary(by = OpportunityForImprovement,
+ type = list(OnDuty ~ "dichotomous"),
+ label = list(RTS = "Revised Trauma Score",
+ ISS = "Injury Severity Score",
+ Intubation = "Mechanical ventilation",
+ mechanical.ventilation.cont = "Mechanical ventilation in days",
+ TimeFCT = "Time to first CT, in minutes",
+ daysinICU = "ICU length of stay",
+ icu.los.cont = "ICU length of stay in days",
+ OnDuty = "On call hours",
+ ASApreinjury = "ASA preinjury",
+ DiedWithin24Hours = "24-hour mortality"),
+ statistic = list(
+ # all_continuous() ~ "{mean} ({sd})",
+ all_continuous() ~ c("{median} ({p25}, {p75})"),
+ all_categorical() ~ "{n} ({p}%)"
+ # daysinICU ~ "{mean} ({sd})"
+ # Intubation ~ "{mean} ({sd})"
+ #Intubation och daysinICU måste göras till en numeric för att kunna visa medelvärde och standardavvikelse men blir då en ny rad i tabellen?
+ ),
+ missing = "ifany",
+ missing_text = "Missing",
+ missing_stat = "{N_miss} ({p_miss}%)",
+ digits = all_continuous() ~ 0
+ ) %>%
+ modify_table_styling(
+ columns = label,
+ rows = label == "On call hours",
+ footnote = "Arrival at the hospital on Saturday or Sunday, or arrival at the hospital before 8 am or after 5 pm"
+ ) %>%
+ bold_labels() %>%
+ add_overall(last = TRUE) %>%
+ add_stat_label() %>%
+ #add_p() %>%
+ #bold_p(t=0.05) %>%
+ modify_caption("Table 1. Sample Characteristics
") %>%
+ # as_flex_table() %>%
+ print()
diff --git a/functions/table2.R b/functions/table2.R
new file mode 100644
index 0000000..1183622
--- /dev/null
+++ b/functions/table2.R
@@ -0,0 +1,59 @@
+#TABLE 2: Adjusted and unadjusted logistic regression
+# Data Preparation
+tablereg <- ofi %>%
+ select(Sex, Age, Intubation, RTS, ISS,TimeFCT, OnDuty, daysinICU, TimeFCT,
+ ASApreinjury, Survival, OpportunityForImprovement1)
+
+tablereg$Intubation <- ifelse(is.na(tablereg$Intubation), "Unknown", table1$Intubation)
+tablereg$Intubation <- fct_relevel(tablereg$Intubation, "Mechanical ventilation 0-2 days", "Mechanical ventilation 3-7 days", "Mechanical ventilation > 7 days", "Unknown")
+tablereg$daysinICU <- fct_relevel(tablereg$daysinICU, "≤ 7 days", "> 7 days")
+tablereg$Survival <- fct_relevel(tablereg$Survival, "Dead", "Alive")
+
+tablereg <- na.omit(tablereg)
+
+# Unadjusted Table
+table3a <- tbl_uvregression(data = tablereg,
+ method = glm,
+ y = OpportunityForImprovement1,
+ method.args = list(family = binomial),
+ exponentiate = TRUE,
+ label = list(
+ RTS = "Revised Trauma Score",
+ daysinICU = "Days in the ICU",
+ TimeFInt = "Time to first intervention",
+ Intubation = "Mechanical ventilation",
+ ASApreinjury = "ASA preinjury",
+ OnDuty = "On call hours",
+ TimeFCT = "Time to first CT, in minutes"
+ )) %>%
+ bold_labels() %>%
+ bold_p(t = 0.05)
+
+#print(table3a)
+
+# Adjusted Table
+#Creating linear regression
+adjusted_table <- glm(OpportunityForImprovement1 ~ Sex + Age + Intubation + RTS + ISS + OnDuty + daysinICU + TimeFCT + ASApreinjury + Survival, family = binomial, data = tablereg)
+
+table3b <- tbl_regression(adjusted_table,
+ exponentiate = TRUE,
+ label = list(RTS = "Revised Trauma Score",
+ daysinICU = "Days in the ICU",
+ Intubation = "Mechanical ventilation",
+ OnDuty = "On call hours",
+ ASApreinjury = "ASA preinjury",
+ TimeFCT = "Time to first CT, in minutes")) %>%
+ bold_labels() %>%
+ bold_p(t = 0.05)
+
+# print(table3b)
+
+# Merging Tables
+table3_merge <- tbl_merge(tbls = list(table3a, table3b),
+ tab_spanner = c("**Unadjusted**", "**Adjusted**")) %>%
+ # modify_column_merge(pattern = "{OR} ({ci}) {p.value}") %>%
+ modify_caption("Table 3. Unadjusted and adjusted logistic regression analyses of associations between patient level factors and opportunities for improvement (N = 1449).
")
+
+
+print(table3_merge)
+
diff --git a/functions/table3.R b/functions/table3.R
new file mode 100644
index 0000000..0f0e7c7
--- /dev/null
+++ b/functions/table3.R
@@ -0,0 +1,76 @@
+#TABLE 2: Adjusted and unadjusted logistic regression
+# Data Preparation
+ofi_alive <- subset(tablereg, subset = (Survival == "Alive"))
+
+tablereg1 <- ofi_alive %>%
+ select(Sex, Age, Intubation, RTS, ISS,TimeFCT, OnDuty, daysinICU, TimeFCT,
+ ASApreinjury, OpportunityForImprovement1)
+
+tablereg1$daysinICU <- fct_relevel(tablereg1$daysinICU, "≤ 7 days", "> 7 days")
+
+# Unadjusted Table
+#table3aalive <- tbl_uvregression(data = tablereg1,
+# method = glm,
+# y = OpportunityForImprovement1,
+# method.args = list(family = binomial),
+# exponentiate = TRUE,
+# label = list(
+# RTS = "Revised Trauma Score",
+# daysinICU = "Days in the ICU",
+# TimeFInt = "Time to first intervention",
+# ASApreinjury = "ASA preinjury",
+# OnDuty = "On call hours",
+# Intubation = "Mechanical ventilation",
+# TimeFCT = "Time to first CT"
+# )) %>%
+# bold_labels() %>%
+# bold_p(t = 0.05) %>%
+# hide_n = TRUE
+
+# Adjusted Table
+#Creating linear regression
+adjusted_table1 <- glm(OpportunityForImprovement1 ~ Sex + Age + Intubation + RTS + ISS + OnDuty + daysinICU + TimeFCT + ASApreinjury, family = binomial, data = tablereg1)
+
+table3aalive <- tbl_regression(adjusted_table1,
+ exponentiate = TRUE,
+ label = list(RTS = "Revised Trauma Score",
+ daysinICU = "Days in the ICU",
+ OnDuty = "On call hours",
+ Intubation = "Mechanical ventilation",
+ ASApreinjury = "ASA preinjury",
+ TimeFCT = "Time to first CT, in minutes")) %>%
+ bold_labels() %>%
+ add_n() %>%
+ bold_p(t = 0.05)
+
+#Creating with patients who died
+ofi_dead <- subset(tablereg, subset = (Survival == "Dead"))
+
+tablereg2 <- ofi_dead %>%
+ select(Sex, Age, Intubation, RTS, ISS,TimeFCT, OnDuty, daysinICU, TimeFCT,
+ ASApreinjury, OpportunityForImprovement1)
+
+tablereg2$daysinICU <- fct_relevel(tablereg2$daysinICU, "≤ 7 days", "> 7 days")
+
+adjusted_table2 <- glm(OpportunityForImprovement1 ~ Sex + Age + Intubation + RTS + ISS + OnDuty + daysinICU + TimeFCT + ASApreinjury, family = binomial, data = tablereg2)
+
+table3bdead <- tbl_regression(adjusted_table2,
+ exponentiate = TRUE,
+ label = list(RTS = "Revised Trauma Score",
+ daysinICU = "Days in the ICU",
+ OnDuty = "On call hours",
+ Intubation = "Mechanical ventilation",
+ ASApreinjury = "ASA preinjury",
+ TimeFCT = "Time to first CT, in minutes")) %>%
+ bold_labels() %>%
+ bold_p(t = 0.05)
+
+# Merging Tables
+table3b_merge <- tbl_merge(tbls = list(table3aalive, table3bdead),
+ tab_spanner = c("**Alive**", "**Dead**")) %>%
+ # modify_table_styling(table3b_merge, hide_n = TRUE) %>%
+ modify_caption("Table 4. Adjusted logistic regression analyses of associations between patient level factors and opportunities for improvement in patients alive and dead 30 days after hospitalization (N = 1163).
")
+
+# Print the merged table
+print(table3b_merge)
+
diff --git a/main.R b/main.R
index 330e319..8944b4f 100644
--- a/main.R
+++ b/main.R
@@ -1,26 +1,323 @@
-## Welcome!
+#INSTALLING PACKAGES
+#devtools::install_github("martingerdin/noacsr")
+#devtools::install_github("martingerdin/rofi")
+library(dotenv)
+library(noacsr)
+library(rofi)
+#noacsr::source_all_functions()
+data <- import_data()
-## This is your project's main script file and together with
-## manuscript.Rmd it provides and entry point for you and other people
-## coming to the project. The code in this file should give an outline
-## of the different steps conducted in your study, from importing data
-## to producing results.
+merged.data <- merge_data(data)
+merged.data$ofi <- create_ofi(merged.data)
-## This file should be relatively short, and most of the heavy
-## lifting should be done by specialised functions. These functions
-## live in the folder functions/ and you create a new function using
-## create_function().
-## Feel free to remove this introductory text as you get started.
+#install.packages("dplyr")
+library(dplyr)
+#install.packages("gtsummary")
+library(gtsummary)
+library(tidyverse)
-## Source all functions (if you tick the box "Source on save" in
-## RStudio functions will be automatically sourced when you save
-## them). They all need to be sourced however when you compile your
-## manuscript file or run this file as a job, as that happens in a
-## clean R session.
-noacsr::source_all_functions()
-## Import data
-data <- import_data(test = TRUE)
+#FLOWCHART: included/excluded
+#install.packages("Gmisc")
+library(Gmisc, quietly = TRUE)
+library(glue)
+#install.packages("htmlTable")
+library(htmlTable)
+library(grid)
+library(magrittr)
+
+org_cohort <- boxGrob(glue("Total patient cases in trauma quality database",
+ "n = {pop}",
+ pop = txtInt(14022),
+ .sep = "\n"))
+eligible <- boxGrob(glue("Eligible",
+ "n = {pop}",
+ pop = txtInt(1742),
+ .sep = "\n"))
+included <- boxGrob(glue("Included (n = {incl}):",
+ "- OFI: {ofi1}",
+ "- No OFI: {ofi2}",
+ ofi1 = 143,
+ ofi2 = 1306,
+ incl = txtInt(1449),
+ .sep = "\n"),
+ just = "left")
+excluded <- boxGrob(glue("Excluded (n = {tot}):",
+ " - Not admitted to the ICU: {icu}",
+ " - Patients < 15 years: {age}",
+ " - Dead on arrival: {doa}",
+ " - No data on OFI: {ofi}",
+ tot = 12278,
+ icu = 14022-2679,
+ age = 2679-2676,
+ doa = 2676-2670,
+ ofi = 2670-1742,
+ .sep = "\n"),
+ just = "left")
+excluded1 <- boxGrob(glue("Excluded: missing data (n = {x})",
+ x = 1742 - 1449,
+ .sep = "\n"),
+ just = "left")
+
+grid.newpage()
+vert <- spreadVertical(org_cohort,
+ eligible = eligible,
+ included = included)
+
+# Move excluded box
+excluded <- moveBox(excluded,
+ x = 0.8,
+ y = 0.7)
+
+excluded1 <- moveBox(excluded1,
+ x = 0.8,
+ y = 0.4)
+
+# Connect boxes vertically
+for (i in 1:(length(vert) - 1)) {
+ connectGrob(vert[[i]], vert[[i + 1]], type = "vert") %>%
+ print
+}
+
+# Connect excluded box horizontally
+connectGrob(vert$eligible, excluded, type = "L")
+connectGrob(vert$included, excluded1, type = "L")
+
+# Print boxes
+vert
+excluded
+excluded1
+
+
+##CLEANING DATA
+subdat <- merged.data %>%
+ select(ofi, pt_Gender, pt_age_yrs, ed_gcs_sum, ed_sbp_value, ed_rr_value,
+ res_survival, pre_intubated, ed_intubated, dt_ed_first_ct, ISS, DateTime_ArrivalAtHospital, FirstTraumaDT_NotDone,
+ host_care_level, hosp_vent_days, pt_asa_preinjury, pre_gcs_sum,
+ pre_rr_value, pre_sbp_value, Fr1.12, ed_rr_rtscat, ed_sbp_rtscat, pre_rr_rtscat, pre_sbp_rtscat, iva_dagar_n)
+
+#Converting subdat$ofi to logical so subset can be used
+subdat$ofi <- ifelse(subdat$ofi == "Yes", TRUE, FALSE)
+
+#Only those in IVA
+iva <- subset(subdat, subset = (host_care_level == 5))
+
+#Removing pt_yrs < 15
+adult <- subset(iva, subset = (pt_age_yrs > 14))
+
+#Deceased on arrival
+alive <- subset(adult, subset = (Fr1.12 == 2 | is.na(Fr1.12)))
+
+#Removing ofi = NA
+ofi <- alive %>% subset(!is.na(ofi))
+
+
+#DEFINING VARIABLES FOR TABLE 1
+#Gender
+ofi$Sex <- ifelse(ofi$pt_Gender == 1, "Male",
+ ifelse(ofi$pt_Gender == 2, "Female",
+ ifelse(ofi$pt_Gender == 999, NA, NA)))
+
+#Age
+ofi$Age <- ofi$pt_age_yrs
+
+#Intubation
+ofi$Intubation1 <- ifelse(ofi$pre_intubated == 1, "Intubation",
+ ifelse(ofi$pre_intubated == 2, "No intubation",
+ ifelse(ofi$pre_intubated == 999, "Unknown",
+ ifelse(ofi$ed_intubated == 1, "Intubation",
+ ifelse(ofi$ed_intubated == 2, "No intubation",
+ ifelse(ofi$ed_intubated == 999, "Unknown", "Unknown"))))))
+
+#Intubation combined with ventilator days
+ofi$Intubation <- ifelse(ofi$Intubation1 == "No intubation", "No intubation",
+ ifelse(ofi$Intubation1 == "Intubation" & ofi$hosp_vent_days == 0, "Intubation 1-3 days",
+ ifelse(ofi$Intubation1 == "Intubation" & ofi$hosp_vent_days %in% 1:7, "Intubation 1-7 days",
+ ifelse(ofi$Intubation1 == "Intubation" & ofi$hosp_vent_days > 7, "Intubation > 7 days",
+ ifelse(ofi$Intubation1 == "Unknown", "Unknown", NA)))))
+
+#Respiratory rate
+ofi$RespiratoryRate <- ifelse(is.na(ofi$ed_rr_value), ofi$pre_rr_value, ofi$ed_rr_value)
+
+#Systolic blood pressure
+ofi$SystolicBloodPressure <- ifelse(is.na(ofi$ed_sbp_value), ofi$pre_sbp_value, ofi$ed_sbp_value)
+
+#Glasgow Coma Scale
+ofi$GlasgowComaScale <- ifelse(ofi$ed_gcs_sum == 99, 99,
+ ifelse(ofi$ed_gcs_sum == 999, NA,
+ ifelse(ofi$ed_gcs_sum == 3, 3,
+ ifelse(ofi$ed_gcs_sum == 4, 4,
+ ifelse(ofi$ed_gcs_sum == 5, 5,
+ ifelse(ofi$ed_gcs_sum == 6, 6,
+ ifelse(ofi$ed_gcs_sum == 7, 7,
+ ifelse(ofi$ed_gcs_sum == 8, 8,
+ ifelse(ofi$ed_gcs_sum == 9, 9,
+ ifelse(ofi$ed_gcs_sum == 10, 10,
+ ifelse(ofi$ed_gcs_sum == 11, 11,
+ ifelse(ofi$ed_gcs_sum == 12, 12,
+ ifelse(ofi$ed_gcs_sum == 13, 13,
+ ifelse(ofi$ed_gcs_sum == 14, 14,
+ ifelse(ofi$ed_gcs_sum == 15, 15, NA)))))))))))))))
+
+
+ofi$GlasgowComaScale <- ifelse(is.na(ofi$ed_gcs_sum), ofi$pre_gcs_sum, ofi$ed_gcs_sum)
+
+#RTS score
+ofi$RTSGCS <- ifelse(ofi$GlasgowComaScale %in% 13:15, 4,
+ ifelse(ofi$GlasgowComaScale %in% 9:12, 3,
+ ifelse(ofi$GlasgowComaScale %in% 6:8, 2,
+ ifelse(ofi$GlasgowComaScale %in% 4:5, 1,
+ ifelse(ofi$GlasgowComaScale == 3, 0,
+ ifelse(ofi$GlasgowComaScale == 99, 0, NA))))))
+
+ofi$RTSSBP <- ifelse(ofi$SystolicBloodPressure > 89, 4,
+ ifelse(ofi$SystolicBloodPressure %in% 76:89, 3,
+ ifelse(ofi$SystolicBloodPressure %in% 50:75, 2,
+ ifelse(ofi$SystolicBloodPressure %in% 1:49, 1,
+ ifelse(ofi$SystolicBloodPressure == 0, 0,
+ ifelse(ofi$SystolicBloodPressure == 99, 0, NA))))))
+
+ofi$RTSRR <- ifelse(ofi$RespiratoryRate > 29, 4,
+ ifelse(ofi$RespiratoryRate %in% 10:29, 3,
+ ifelse(ofi$RespiratoryRate %in% 6:9, 2,
+ ifelse(ofi$RespiratoryRate %in% 1:5, 1,
+ ifelse(ofi$RespiratoryRate == 0, 0,
+ ifelse(ofi$RespiratoryRate == 99, 0, NA))))))
+
+ofi$RTS <- (0.9368*ofi$RTSGCS + 0.7326*ofi$RTSSBP + 0.2908*ofi$RTSRR)
+
+
+
+
+#Working hours: arrived between 8 am and 5 pm
+ofi$hour <- format(ofi$DateTime_ArrivalAtHospital, "%H")
+ofi$WorkingHoursTF <- ifelse(ofi$hour == "08" | ofi$hour == "09" | ofi$hour == "10" | ofi$hour == "11" | ofi$hour == "12" | ofi$hour == "13" | ofi$hour == "14" | ofi$hour == "15" | ofi$hour == "16", TRUE, FALSE)
+ofi$WorkingHours <- ifelse(ofi$WorkingHoursTF == TRUE, "Yes",
+ ifelse(ofi$WorkingHoursTF == FALSE, "No", NA))
+
+#Weekend: arrived on Saturday or Sunday
+ofi$Weekdays <- weekdays(ofi$DateTime_ArrivalAtHospital)
+ofi$WeekendTF <- ifelse(ofi$Weekdays == "Saturday" | ofi$Weekdays == "Sunday", TRUE, FALSE)
+ofi$Weekend <- ifelse(ofi$WeekendTF == TRUE, "Yes",
+ ifelse(ofi$WeekendTF == FALSE, "No", NA))
+
+#Duty shift
+ofi$OnDuty <- ifelse(ofi$Weekend == "Yes", 1,
+ ifelse(ofi$WorkingHours == "No", 1, 0))
+
+#Time to first CT
+ofi$TimeFCT <- ofi$dt_ed_first_ct
+
+#Days in the ICU
+ofi$daysinICU <- ifelse(ofi$iva_dagar_n < 7 | ofi$iva_dagar_n == 7, "≤ 7 days",
+ ifelse(ofi$iva_dagar_n > 7, "> 7 days", NA))
+
+#Pt ASA preinjury
+ofi$ASApreinjury <- ifelse(ofi$pt_asa_preinjury == 1 | ofi$pt_asa_preinjury == 2, "ASA 1-2",
+ ifelse(ofi$pt_asa_preinjury %in% 3:5, "ASA 3-5",
+ ifelse(ofi$pt_asa_preinjury == 999, NA, NA)))
+
+#Survival after 30 days
+ofi$Survival <- ifelse(ofi$res_survival == 1, "Dead",
+ ifelse(ofi$res_survival == 2, "Alive",
+ ifelse(ofi$res_survival == 999, NA, NA)))
+
+
+#OFI
+ofi$OpportunityForImprovement <- ifelse(ofi$ofi == TRUE, "Opportunity for improvement",
+ ifelse(ofi$ofi == FALSE, "No opportunity for improvement", NA))
+
+ofi$OpportunityForImprovement1 <- ifelse(ofi$OpportunityForImprovement == "Opportunity for improvement", 1,
+ ifelse(ofi$OpportunityForImprovement == "No opportunity for improvement", 0, NA))
+
+#TABLE 1: Sample characteristics
+#Creating new table with defined data
+library(dplyr)
+library(gt)
+
+table1 <- ofi %>%
+ select(Sex, Age, Intubation, RTS, ISS, TimeFCT, OnDuty, daysinICU,
+ ASApreinjury, Survival, OpportunityForImprovement)
+
+
+table1$Intubation <- ifelse(is.na(table1$Intubation), "Unknown", table1$Intubation)
+table1 <- na.omit(table1)
+
+table2 <- table1 %>%
+ mutate(Intubation = factor(Intubation, levels = c("No intubation", "Intubation 1-7 days", "Intubation > 7 days", "Unknown"))) %>%
+ tbl_summary(by = OpportunityForImprovement,
+ type = list(OnDuty ~ "dichotomous"),
+ label = list(RTS = "Revised Trauma Score",
+ ISS = "Injury Severity Score",
+ TimeFCT = "Time to first CT",
+ daysinICU = "Days in the ICU",
+ OnDuty = "On duty",
+ ASApreinjury = "ASA preinjury"),
+ statistic = list(
+ all_continuous() ~ "{mean} ({sd})",
+ all_categorical() ~ "{n} ({p}%)"
+ ),
+ missing = "ifany",
+ missing_text = "Missing",
+ digits = all_continuous() ~ 2
+ ) %>%
+ modify_table_styling(
+ columns = label,
+ rows = label == "On duty",
+ footnote = "Arrival at the hospital on Saturday or Sunday, or arrival at the hospital before 8 am or after 5 pm"
+ ) %>%
+ bold_labels() %>%
+ add_overall(last = TRUE) %>%
+ modify_caption("Table 1. Sample Characteristics
") %>%
+ print()
+
+#TABLE 2: Adjusted and unadjusted logistic regression
+# Data Preparation
+tablereg <- ofi %>%
+ select(Sex, Age, Intubation, RTS, ISS,TimeFCT, OnDuty, daysinICU, TimeFCT,
+ ASApreinjury, Survival, OpportunityForImprovement1)
+
+tablereg$Intubation <- ifelse(is.na(tablereg$Intubation), "Unknown", table1$Intubation)
+tablereg$Intubation <- fct_relevel(tablereg$Intubation, "No intubation", "Intubation 1-7 days", "Intubation > 7 days", "Unknown")
+
+
+# Unadjusted Table
+table3a <- tbl_uvregression(data = tablereg,
+ method = glm,
+ y = OpportunityForImprovement1,
+ method.args = list(family = binomial),
+ label = list(
+ RTS = "Revised Trauma Score",
+ daysinICU = "Days in the ICU",
+ TimeFInt = "Time to first intervention",
+ ASApreinjury = "ASA preinjury",
+ OnDuty = "On duty",
+ TimeFCT = "Time to first CT"
+ )) %>%
+ bold_labels() %>%
+ bold_p(t = 0.05)
+
+#print(table3a)
+
+# Adjusted Table
+#Creating linear regression
+adjusted_table <- glm(OpportunityForImprovement1 ~ Sex + Age + Intubation + RTS + ISS + OnDuty + daysinICU + TimeFCT + ASApreinjury + Survival, family = binomial, data = tablereg)
+
+table3b <- tbl_regression(adjusted_table,
+ label = list(RTS = "Revised Trauma Score",
+ daysinICU = "Days in the ICU",
+ ASApreinjury = "ASA preinjury",
+ TimeFCT = "Time to first CT")) %>%
+ bold_labels() %>%
+ bold_p(t = 0.05)
+
+# print(table3b)
+
+# Merging Tables
+table3_merge <- tbl_merge(tbls = list(table3a, table3b),
+ tab_spanner = c("**Unadjusted**", "**Adjusted**")) %>%
+ modify_caption("Table 2. Unadjusted and adjusted logistic regression analyses of associations between patient level factors and opportunities for improvement
")
+
+print(table3_merge)
-## Whatever you do next, maybe clean data?
diff --git a/manuscript.Rmd b/manuscript.Rmd
index ea724ae..8632041 100644
--- a/manuscript.Rmd
+++ b/manuscript.Rmd
@@ -1,258 +1,300 @@
---
-title: "Manuscript title here"
-output: html_document
-author: "Your name here"
+title: "Opportunities for improvement in adult trauma patients admitted to the intensive care unit: A registry-based study"
+output: "html_document"
+author: "Elin Sun Cao"
bibliography: bibliography.bib
csl: vancouver.csl
+editor_options:
+ markdown:
+ wrap: 72
---
-```{r setup, include=FALSE}
-knitr::opts_chunk$set(echo = TRUE)
-```
+# Abstract
+## Förbättringsmöjligheter i vården av vuxna traumapatienter som vårdats på intensivvårdsavdelningen: En register-baserad studie
+
+### Bakgrund
+Trauma är en ledande orsak till mortalitet och morbiditet i världen. En hörnsten i förbättringsprogram för trauma är multidisciplinära mortalitets- och morbiditetskonferenser. Syftet med dessa konferenser är att identifiera förbättringsmöjligheter (OFI). Många patienter med svårt trauma blir inlagda på intensivvårdsavdelningen (IVA). Trots detta är kännedomen om OFI hos denna patientgrupp begränsad.
+
+### Syfte
+Karaktärisera OFI hos vuxna traumapatienter som vårdats på IVA och bedöma hur patient- och processfaktorer är associerade med OFI hos denna patientgrupp.
+
+### Metod
+Detta är en registerbaserad studie som inkluderar traumapatienter mellan 2014–2023 som vårdats på Karolinska Universitetssjukhuset i Solna, varit inlagda på IVA och som har granskats avseende förekomsten av OFI vid en mortalitets- och morbiditetskonferens. Bivariabel och multivariabel logistisk regression användes för att bedöma sambandet mellan förekomsten av OFI och utvalda patient- och processfaktorer.
+
+### Resultat
+OFI identifierades hos 143 (9,9%) av 1449 patienter. Högre Revised Trauma Score (RTS) (OR 1.22; 95% CI 1.10, 1.36; p<0,001), > 7 dagar på intensivvårdsavdelningen (IVA) (OR 1.83; 95% CI 1.21, 2.75; p=0.004) och överlevnad 30 dagar efter sjukhusinläggning (OR 1.85; 95% CI 1.03, 3.50; p=0.049) var signifikant associerade med högre odds för OFI i multivariabla analyser.
+
+### Slutsats
+Flera patient- och processfaktorer var associerade med ökade odds för OFI. Resultaten påvisade att det var de patienter som hade högre RTS - och därmed var mer stabila vid ankomst, samt patienter som varit inlagda längre tid på IVA och som överlevde 30 dagar efter sjukhusinläggning som hade högre odds för OFI.
+
+## Opportunities for improvement in adult trauma patients admitted to the intensive care unit: A registry-based study
+### Background
+Trauma is a leading cause of mortality and morbidity worldwide. A cornerstone in trauma quality improvement programs is multidisciplinary mortality and morbidity conferences. The purpose of these conferences is to identify opportunities for improvement (OFI). Many patients with severe trauma are admitted to the intensive care unit (ICU), but little is known about OFI in this group of patients.
-
+### Aim
+Characterize OFI in adult trauma patients admitted to the ICU and assess how patient and process factors are associated with OFI in these patients.
-
+### Methods
+A registry-based study of trauma patients admitted to the ICU between 2014-2023 at the Karolinska University Hospital in Solna, Sweden, who were reviewed regarding the presence of OFI in a mortality and morbidity conference. Bivariable and multivariable logistic regression was used to determine associations between selected patient and process factors and OFI.
-
+### Results
+OFI was identified in 143 (9,9%) out of 1449 patients. RTS (OR 1.22; 95% CI 1.10, 1.36; p<0.001), > 7 days in the ICU (OR 1.83; 95% CI 1.21, 2.75; p=0.004) and survival 30 days after injury (OR 1.85; 95% CI 1.03, 3.50; p=0.049) were significantly associated with increased odds of OFI.
-
+### Conclusion
+Several patient and process factors were found to be associated with OFI, indicating that patients with higher RTS scores - who were more stable upon arrival, patients with more days spent in the ICU, and patients who were alive 30 days after injury had higher odds of OFI.
-Abstract
-========
+# Abbreviations
-
+ASA score = American Society of Anesthesiologists score
+CT = computed tomography
+GCS = Glasgow Coma Scale
+ISS = Injury Severity Score
+OFI = Opportunity For Improvement
+RR = respiratory rate
+RTS = Revised Trauma Score
+SBP = systolic blood pressure
+SweTrau = Swedish Trauma Registry
+TQIP = Trauma Quality Improvement Programs
-Background
-----------
-Methods
--------
+# Introduction
-Results
--------
+Trauma — the clinical entity of injury and the body’s associated response — is a leading cause of mortality and morbidity worldwide [@60]. According to the Global Burden of Disease Study 2019, injury-related deaths constitute 7.6% of all deaths and take the lives of 4,3 million people yearly worldwide - more than HIV/AIDS, malaria, maternal mortality, and tuberculosis combined. The top three out of five causes of death in people aged 5-29 are injury-related: road traffic injuries, homicide, and suicide. Each year, tens of millions of people suffer non-fatal injuries leading to treatment at hospitals in emergency departments and acute care visits [@52, @1]. These injuries often lead to temporary or permanent disability and the need for physical and mental rehabilitation. Disability-adjusted life-years (DALYs) is a way to measure post-discharge quality of life and overall disease burden. Trauma accounts for one-tenth of DALYs, most of the burden in low- and middle-income countries [@6]. Trauma costs approximately 3% of gross domestic product yearly globally, as it mostly affects the working population [@7]. Trauma exposure at a young age increases the risk for mental illness and suicide leading to increased risk of an unhealthy lifestyle associated with smoking, alcohol and substance abuse, chronic disease, and cancer as well as societal problems such as crime, poverty, and violence [@1]. The burden of disease from trauma varies depending on certain conditions: socioeconomic factors, sex, age, and country. Trauma is more common in men compared to women. Across all age groups worldwide, road traffic injuries and suicide rank among the top three leading causes of injury for both genders. However, homicide is more prevalent among males, while falls are more common in females [@2].
-Conclusion
-----------
+In Sweden, the ongoing advancement of quality improvement systems places significant emphasis on addressing trauma [@61]. A cornerstone of this effort is the Swedish Trauma Registry (SweTrau), to which most hospitals across the country report data on trauma patients. SweTrau serves as a vital resource for continued research on trauma patients contributing to efforts to optimize trauma care nationwide. According to SweTrau, trauma is one of the leading causes of death among the working population in Sweden. Many patients survive but with severe sequelae. The leading causes of trauma in Sweden include road traffic injuries, falls, weapons, stabs, and blows. In the university hospitals of Sweden – among patients with severe trauma, 17% were deceased, 20% suffered severe disability, 49% suffered moderate disability and 14% recovered well [@3].
-Introduction
-============
+For all injuries, quality hospital care can reduce short- and long-term disability. Therefore, improving planning, access, and organization of trauma care systems involving prevention, pre-hospital care, intra-hospital care, rehabilitation as well as research and quality improvement, all play a central role in reducing the overall impact after trauma. [@1]
-
+Trauma patients are often admitted to the ICU due to the severity of their injuries and the subsequent physiological and anatomical sequelae. Prior research has focused on classifying errors in preventable trauma deaths, with clinical performance errors, often stemming from a lack of knowledge, being identified as the most common type, particularly in the emergency department [@62]. However, reporting of errors in trauma resuscitation varies between healthcare centers [@63]. As a results, there is a notable gap in research regarding error reporting practices in trauma care — especially for trauma patients admitted to the ICU [@12]. Understanding and addressing these gaps are essential for improving patient outcomes and optimizing trauma care protocols.
-You can cite document here like this [@exampleKey9999]. Open the file
-bibliography.bib to learn more.
+## Trauma quality improvement programs
-Methods
-=======
+Trauma Quality Improvement Programs (TQIP) emerged in high-income countries to enhance the quality of in-hospital trauma care. Its implementation has contributed to significant improvements in the quality of care and outcomes [@47]. Among TQIP interventions, trauma registries along with mortality and morbidity conferences have been highlighted as key in-hospital TQIP interventions [@48]. In addition to these, audit filters is another intervention that is commonly a part of TQIP. All these interventions have become integral components of TQIP, facilitating ongoing improvements in trauma care [@49]. The purpose of multidisciplinary mortality and morbidity reviews is to discuss preventable deaths and identify OFIs in trauma care regarding structure as well as clinical processes. This way, identifying OFIs will guide us on what to focus specific efforts on. Mortality and morbidity reviews are a key method of addressing all necessary components of trauma improvement [@10]. Audit filters are descriptions of specific actions that should be taken, time frames in which tests or treatments should be provided, or outcomes that are expected to occur in injured patients. The purpose is to assess patients whose care falls outside the frames of these audit filters and provide feedback on how to correct errors and improve future trauma care on a systematic level. Audit filters are often used to select patients for mortality and morbidity conferences [@5].
-Study design
-------------
+Preventability in trauma is often defined in studies as an event that would not have occurred had the patient received ordinary standards of care, such as timely and accurate management of hypoxemia and hemorrhage during early patient management [@9]. The term “Opportunities for improvement” (OFI) is a term used by the World Health Organization (WHO) in their quality improvement guidelines, refers to preventable errors in care that have a direct adverse effect on patient outcomes or deviate from safe clinical practice. Quality improvement requires a feedback loop where patterns of the care of patients are analyzed consistently in an objective way. Utilizing OFIs serves as a means of identifying areas of improvement and further elevating the quality of care [@42].
-
+Albaaj et al. recently analyzed OFI in relation to different patient and process factors based on a selection of data from SweTrau including the patients admitted to the Karolinska University Hospital Solna. The study indicated that patients with moderate to severe trauma are at the highest odds of OFI [@12].
-Setting
--------
+The paradigm of Donabedian states that implementing proven structures and processes of care is the most effective way to improve outcomes [@8]. Mortality and morbidity reviews as well as audit filters can be used as means of improving outcomes in line with the paradigm of Donabedian. Examples of structure and process interventions in the intensive care unit (ICU) are increased staff coverage and treatment bundles to recognize and treat common conditions such as bundles for sepsis and ventilator-associated pneumonia [@9].
-
+## Preventability among trauma patients in the ICU
-Participants
-------------
+In the evaluation of trauma patients, the primary focus is on conducting a rapid initial assessment, known as the primary survey, to identify and address any life-threatening injuries. This is typically followed by a more comprehensive secondary survey to thoroughly assess for additional injuries. However, the secondary survey may be overlooked during the initial evaluation in the emergency department, resulting in ICU personnel assuming responsibility for conducting this assessment, along with initiating early patient management. Early management of trauma patients has over time increasingly shifted to the ICU [@50]. The most common challenges in trauma patient management in the ICU are airway and ventilator management, hemorrhage, transfusions, and coagulopathy [@53].
-
+Challenges of airway management in ICU-admitted trauma patients involve harmful effects of mechanical ventilation, ventilator-associated pneumonia (VAP), and severe hypoxemia due to aspiration, pneumonia, acute respiratory distress syndrome, or pulmonary contusions. Mechanical ventilation can be managed with a lung-protective strategy to reduce risks of harmful effects, there are ventilator bundles to decrease the rate of VAP, and severe hypoxemia can be treated with strategies to improve oxygenation, such as airway pressure release ventilation, pulmonary vasodilators, or extracorporeal membrane oxygenation [@54, @55, @56, @57].
-
+Hemorrhage in trauma patients involves blood loss, tissue ischemia as well as inflammatory responses. An increase in lactate and base deficit or failure to normalize them, are linked to higher mortality rates. Managing hemorrhagic shock in trauma patients typically necessitates massive transfusions [@58].
-
+Assessing preventable deaths among ICU-admitted patients poses a significant challenge due to their complex medical backgrounds as well as the complexity and duration of the care, making it difficult to establish the standard of care for each individual. Moreover, despite receiving optimal treatment, these patients remain at elevated risk for complications and increased mortality rates [@9].
-Variables and data sources/measurements
----------------------------------------
+There are many studies on OFI in relation to preventable deaths in trauma patients both regarding medical and human aspects. The primary medical causes of death after trauma are hemorrhage, sepsis/multiorgan failure, and central nervous system injury. Delayed hemostatic procedures and transfusions are common areas for improvement in the hospital stage [@13]. The most prominent OFI regarding hemorrhage was decision-making compared to errors in technical skill. The OFIs are frequently involved in the decision between radiology, surgery, and further investigation [@14].
-
+A study from The University of Pennsylvania looked at the human aspect of OFIs in the ICU. The ICU of this hospital uses telemedicine and telemonitoring to review critical patient events. The areas of improvement they identified were team dynamics and communication, use of best practices, increased and standardized access to emergency resources, and improvement of procedural technique [@15]. Another study looked at how critical information was handled in the ICU and found that laboratory values and test results were the most frequently lost items. This points to the importance of communication and handling of critical information at handoffs of care [@16].
-Bias
-----
+Many patients with severe trauma are admitted to the ICU, but little is known about OFI in this group of patients. This is due to the complexity of this cohort making identification of OFIs in this group more challenging. While numerous studies have explored various facets of trauma care enhancement, research specifically centered on OFIs in ICU-admitted trauma patients remains limited.
-
+## Aim
-Study size
-----------
+This study aims to characterize OFI in adult trauma patients admitted to the ICU and assess how patient and process factors are associated with OFI in these patients.
-
+# Methods
-Quantitative variables
-----------------------
+## Study design
-
+We conducted a retrospective single-center cohort study based on data from the Karolinska University trauma registry, which is part of the Swedish Trauma registry (SweTrau), and the Karolinska trauma care quality database.
-Statistical methods
--------------------
+## Setting
-
+The continuous variables in the analysis were age, Revised Trauma Score (RTS), ISS, and time to first computed tomography (CT). RTS is a measure of severity based on prehospital or emergency department values. In instances where hospital values for systolic blood pressure (SBP), respiratory rate (RR), or Glasgow Come Scale (GCS) scores were missing from the emergency department records, prehospital values were used instead. If the patient was intubated, it was assumed that their GCS was 3 and RR was 0. Subsequently, for the RTS variable, each of the variables GCS, SBP, and RR were converted RTS codes ranging from 0 to 4 based on their respective values. RTS was then calculated using the formula for RTS: 0.9368 * GCS-code + 0.7326 SBP-code + 0.2908 RR-code to derive the RTS value.
-Results
-=======
+## Statistical methods
-
+## Main results
-You can include code in this document like this:
+```{r, include=FALSE}
+source("functions/table2.R")
+```
-```{r main, echo=FALSE}
-source("main.R") ## This "imports" the main script file of your project and run any code in it
+```{r, echo=FALSE}
+table3_merge
```
-You can also embed plots:
+Table 2 shows the unadjusted and adjusted associations of the selected patient and process factors in relation to OFI. In the unadjusted analysis, the factors that were significantly associated with the presence of OFI were: Revised Trauma Score `r inline_text(table3a, variable = RTS)`, > 7 days in the ICU `r inline_text(table3a, variable = daysinICU, level = "> 7 days")` and survival 30 days after injury `r inline_text(table3a, variable = Survival, level = "Alive")`.
+
+In the adjusted analysis the following factors demonstrated significant association with increasing odds of OFI: Revised Trauma Score `r inline_text(table3b, variable = RTS)`, > 7 days in the ICU `r inline_text(table3b, variable = daysinICU, level = "> 7 days")` and survival 30 days after injury `r inline_text(table3b, variable = Survival, level = "Alive")`.
+
+Sex, age, mechanical ventilation, ISS, time to first CT, on-call hours, and ASA preinjury were not significantly associated with OFI in the unadjusted nor the adjusted analyses.
+
+The same patient and process factors were significant in both analyses. The ORs increased for all three factors in the adjusted analysis compared to the unadjusted analysis.
-```{r plot, echo=FALSE}
-plot(pressure)
+```{r, include=FALSE}
+source("functions/table3.R")
```
-You can also mix text and code, so called inline code, like this: `r 2+5`.
+```{r, echo=FALSE}
+table3alive_merge
+```
+
+Table 3 shows the unadjusted and adjusted associations of OFI and the selected patient and process factors among the patients who were alive 30 days after injury In the unadjusted analysis, time to first CT was significantly associated with OFI. However, the effect size was minimal and thus rounded to zero None of the other patient and process factors were significantly associated with OFI in the analyses.
+
+# Discussion
+
+The aim of this study was to characterize OFI in adult trauma patients admitted to the ICU and assess how patient and process factors are associated with OFI in these patients. The key results from this study are that higher RTS, > 7 days length of stay in the ICU and survival 30 days after injury were significantly associated with higher odds of OFI. Age, sex, mechanical ventilation, ISS, time to first CT, arrival during on-call hours, and ASA preinjury were not significantly associated with OFI in either the adjusted or the unadjusted analyses. In the subgroup analysis of patients who were alive 30 days after injury, no patient or process factor was significantly associated with OFI in the adjusted analysis.
+
+Our study found a significant association between higher RTS scores and higher odds of OFI, meaning that among very severely injured patients that are admitted to the ICU, patients in a less severe condition were at higher odds of OFI. Patients with lower RTS scores are often in critical condition upon arrival — upon which emergency interventions are performed immediately. Conversely, patients with higher RTS scores, indicating relative stability upon arrival, may experience delayed interventions. This aligns with findings from previous research, where patients with relatively better vital parameter values, were significantly associated with increased odds of OFI in the unadjusted analysis, but not in the adjusted analysis [@12]. Patients with higher RTS scores among the severely injured patient may appear more stable despite trauma, potentially leading to delayed emergency procedures. In the subgroup analysis of patients who survived 30 days after injury, an association between increased RTS score and odds of OFI was not found. An explanation is that these survivors generally had better overall health conditions. Increased RTS-scores in a cohort of patients with better survival would not be associated with increased odds of OFI.
+
+Prolonged stay in the ICU was found to be significantly associated with higher odds of OFI in both the unadjusted and adjusted analyses. There are two things to consider regarding this variable. Firstly, patients requiring extended ICU care are often those with complex medical conditions, severe injuries, or critical illness. As a result, their prolonged stay in the ICU may signify a higher baseline risk for experiencing complications, increasing odds of OFI. Additionally, prolonged ICU stay increases likelihood of medical errors and adverse events to occur. The ICU environment, while designed to provide advanced medical care, can also be fraught with complexities, including high patient acuity, numerous interventions, and a fast-paced workflow. The longer a patient remains in the ICU, the greater the cumulative exposure to potential errors. Moreover, the prolonged duration of critical illness may lead to complications further increasing the odds of OFI. Conversely, shorter ICU stays may indicate patient demise before potential mistakes could occur, particularly in cases where mortality was anticipated rather than preventable. This finding is similar to previous research demonstrating prolonged ICU stays as a risk factor for increased mortality and morbidity in trauma patients [@22, @26]. Furthermore, it also corresponds with a retrospective cohort indicating that lower initial illness severity and younger age are associated with earlier discharge, further supporting the link between higher odds of OFI and prolonged ICU stays [@25]. In the subgroup analysis of patients alive 30 days after injury, the results could not be replicated. This discrepancy might be attributed to the fact that patients who had longer ICU stays and also passed away belonged to the group with more severe conditions, thus predisposing them to more errors and consequently increasing the odds of OFI. In summary, the association between prolonged ICU stays and OFI is multifactorial, encompassing patient acuity, complexity of care, risk of medical errors, and long-term sequelae of critical illness.
+
+The reason for prolonged ICU stays being associated with increased mortality has been investigated by previous studies suggesting that factors such as comorbidities, age, and illness severity upon admission do not fully elucidate the increased long-term mortality observed in this patient cohort. As the duration of ICU stay increases, the primary reason for ICU stay becomes less linked to the patient’s original condition and increasingly tied to persistent chronic illness accompanied by subsequent organ dysfunction. The cause of the organ dysfunction comes from increased exposure to iatrogenous factors and nosocomial infections, which exacerbate pre-existing organ dysfunction or lead to new-onset organ dysfunctions. [@23, @24] Consequently, this might be a potential explanation for the nonsignificance of predictors such as ASA preinjury and ISS in the analyses. These predictors primarily reflect the patient's baseline health status and the initial injury severity, whereas the complications that arise during prolonged ICU stays are more closely associated with the ongoing healthcare interventions and exposures experienced during hospitalization which cannot be measured with ASA preinjury and ISS.
+
+Another factor associated with increased odds of OFI was survival 30 days after injury. Previous research has identified an association between increased mortality and the severity of the injury upon admission [@59], suggesting that patients at high risk of 30-day mortality are typically in poor health upon arrival. Consequently, the likelihood of OFI decreases as deaths may not be preventable. Therefore, survival to 30 days after the injury is associated with increased odds of OFI.
+
+We also found that mechanical ventilation more than seven days was significantly associated with increased odds of OFI. Previous research has shown that prolonged mechanical ventilation is associated with increased mortality. This is because mechanical ventilation is associated with severe complications and failure of weaning further increases length of mechanical ventilation as well as ICU length of stay [@65, @66]. Our finding of this association may be because of the increased rate of complications and mortality associated with prolonged mechanical ventilation.
+
+Our findings imply that patients were not subjected to differential treatment based on age or sex. This is corroborated by a study indicating that physicians’ willingness to admit patients to the ICU remains consistent regardless of whether the case describes a man or a woman [@29]. However, other research reveals nuances in the relationship between sex and patient outcomes. For instance, among severely injured patients aged 16-44 years, female sex is associated with lower in-hospital mortality rates. Female sex is also associated with a decreased likelihood of ICU admission [@30]. Conversely, male sex is associated with worse outcomes in postoperative ICU patients, with male patients consuming two-thirds of ICU resources [@31]. The higher rate of male ICU admissions and their association with adverse outcomes may be influenced by factors unrelated to the odds of OFI.
+
+The preinjury ASA score was initially designed to evaluate a patient's health status pre-surgery, offering insights into the risk of postoperative complications, and has been found to be a reliable score for predicting mortality after trauma, predicting readmission after traumatic injury, and classifying comorbidity [@32, @33, @34]. As highlighted earlier, prolonged ICU stay becomes decreasingly associated with a patient’s initial condition, supporting the lack of association between OFI and preinjury ASA score. Moreover, the ASA score paints a pre-trauma health picture contrasting with injury scores like RTS, which may offer a more precise assessment of the current condition.
+
+The lack of significance in on-call hours in both analyses suggests that patient’s arrival times did not influence their medical care, indicating that increasing medical resources outside regular working hours may not effectively reduce OFIs. This finding aligns with research on surgeons’ performance after night shifts, which found similar risks of adverse outcomes regardless of prior night work [@51].
+
+Similarly, the lack of significance of ISS in either analysis is consistent with studies suggesting that ISS has higher predictive value when combined with other parameters such as vital signs, comorbidities, and mechanism of injury. Our findings are further supported by research indicating that physiological models outperform anatomical models among ICU-admitted patients, with RTS being a physiological model and ISS being an anatomical one [@36]. Moreover, the New Injury Severity Score (NISS) has also been shown to be a superior predictor for ICU admission and prolonged ICU stay compared to ISS [@37], possibly explaining the nonsignificance of ISS in our analysis.
+
+### Strengths and limitations
+
+One of the strengths of this study is its reliance on registry-based data, ensuring a high level of data quality and reliability. The use of pre-defined variables standardized their definitions, enhancing the potential for similar studies to replicate these findings. Additionally, the study’s specific inclusion criteria facilitated the formation of a well-defined cohort, further contributing to the reliability and reproducibility of the study.
+
+However, several limitations need to be acknowledged. Firstly, the study size was limited. This was a single-center study and provided insights primarily into the context of the Karolinska Institute Hospital Solna. Consequently, the generalizability of the findings to other level 1 trauma centers and ICUs may be limited. Moreover, the small cohort size could potentially impact result accuracy.
+
+Another limitation is the selection of patients for the morbidity and mortality review, which heavily relied on the local audit filters used at the Karolinska Institute Hospital Solna. The use of audit filters for patient selection for the mortality and morbidity review is associated with high false positive rates, ranging from 24% to 80% [@38]. Even though the use of audit filters is associated with high false positive rates, there is still a risk of misplacement where some patients with OFI may have been overlooked.
+
+Additionally, the handling of the variables to the RTS score – namely, RR, SBP, and GCS – introduced potential sources of variability. The use of prehospital, emergency department, or intubation-based values for these variables may have affected data accuracy and reproducibility. However, the composite nature of the RTS score mitigates some of these concerns, as it provides a comprehensive assessment of the patient’s physiological status.
+
+### Clinical implications
+
+This study may hold potential for clinical implications, particularly considering trauma as a leading cause of death globally. While further research is needed to fully understand the clinical implications of our findings, our study contributes to the ongoing efforts to improve trauma care quality.
+
+By identifying OFIs among trauma patients admitted to the ICU, our study offers valuable insights into areas where interventions can be targeted to improve patient outcomes. Specifically, our findings suggest that patients with higher RTS scores, prolonged stays in the ICU, and who were alive 30 days after injury are at increased odds of OFIs. This highlights the importance of prioritizing these patients for closer monitoring and implementing targeted interventions to prevent errors in care.
+
+Furthermore, our research provides valuable information about the patient demographics of the trauma center at Karolinska University Hospital in Solna. This understanding of patient characteristics can inform the development of tailored care plans and quality improvement initiatives aimed at optimizing trauma care delivery.
+
+In summary, our study underscores the importance of continuous efforts to refine trauma care practices and prioritize patient safety. By addressing OFIs and tailoring interventions to high-risk patients, healthcare providers can strive to improve outcomes and enhance the quality of trauma care on a global scale.
+
+
+#### Health equity
+
+In the context of trauma studies, much of the comparative research on OFI among trauma patients is registry-based and encompass all trauma admissions to a hospital regardless of age, sex, or sociodemographic factors.
+
+Our study focused specifically on trauma patients admitted to the ICU at the Karolinska University Hospital Solna, including all patients with severe trauma in the Stockholm region. However, it’s important to note that the population of Stockholm may not be representative of Sweden as a whole. Karolinska University Hospital Solna is a specialized hospital, and access to similar high-quality care may not be uniform across other regions in the country. Therefore, the findings of our study may be more applicable to the population of Stockholm, rather than ICU settings in other parts of Sweden. This could potentially limit the generalizability of our results.
+
+Furthermore, our study observed a predominance of males among the included patients. However, it’s essential to clarify that this imbalance is not a result of biased demographic selection. Instead, it likely reflects the higher incidence of males being admitted to the ICU and experiencing trauma, as indicated by the registry-based nature of our study.
+
+### Generalizability
+
+The study was conducted on 143 outcomes of OFI. The limited number of outcomes can be attributed to several inclusion criteria: the study focused on a single-center cohort, specifically targeting patients admitted to the ICU who were over 14 years old, not deceased upon arrival, and had recorded data on the presence or absence of OFI. While this approach ensured high internal validity by defining a clear and specific cohort of ICU-admitted trauma patients at Karolinska University Hospital Solna, it also led to compromised external validity due to the study’s restricted scope and sample size.
+
+Despite the limitations in external validity, the findings of this study hold significant relevance for directing future efforts in trauma improvement within the ICU of this hospital. Additionally, by concentrating on a well-defined patient cohort, this research provides valuable insights into areas where targeted interventions may be most beneficial for enhancing trauma care outcomes.
+
-Discussion
-==========
+### Future studies
-
+This study has been conducted with continuous guidance and feedback from Martin Gerdin Wärnberg, Jonatan Attergrim, and Kelvin Szolnoky.
-Conclusion
-==========
+# Acknowledgements
-
+I want to extend my greatest gratitude to my supervisor Martin Gerdin Wärnberg and co-supervisors Jonatan Attergrim and Kelvin Szolnoky who have provided me with guidance and support throughout this semester. Their extensive knowledge, deep insight into the subject, and availability and support have greatly contributed to the development of this project. Additionally, I want to express my gratitude to my friends and family who have been there contributing with encouragement and positive energy.
-References
-==========
+# References
diff --git a/manuscript2.Rmd b/manuscript2.Rmd
new file mode 100644
index 0000000..93a0dcc
--- /dev/null
+++ b/manuscript2.Rmd
@@ -0,0 +1,157 @@
+---
+title: "Opportunities for improvement in adult trauma patients admitted to the intensive care unit: A registry-based cohort study"
+output: "html_document"
+author: "Elin Sun Cao"
+bibliography: bibliography.bib
+csl: vancouver.csl
+editor_options:
+ markdown:
+ wrap: 72
+---
+
+## Opportunities for improvement in adult trauma patients admitted to the intensive care unit: A registry-based cohort study
+## Abstract
+### Background
+Trauma is a leading cause of mortality and morbidity worldwide. A cornerstone in trauma quality improvement programs is multidisciplinary mortality and morbidity reviews. The purpose of these reviews is to identify opportunities for improvement and corresponding corrective actions. Many patients with severe trauma are admitted to the intensive care unit (ICU), but little is known about opportunities for improvement in this cohort. The aim of this study was to characterize opportunities for improvement in adult trauma patients admitted to the ICU and assess how patient and process factors are associated with opportunities for improvement in these patients.
+
+### Methods
+A registry-based study of trauma patients admitted to the ICU between 2014-2023 at the Karolinska University Hospital in Solna, Sweden, who were reviewed regarding the presence of opportunities for improvement in a mortality and morbidity review. Univariate and multivariable logistic regression was used to determine associations between selected patient and process factors and opportunities for improvement.
+
+### Results
+Opportunities for improvement was identified in 143 (9,9%) out of 1449 patients.Revised Trauma Score , > 7 days in the ICU and survival 30 days after injury were significantly associated with increased opportunities for improvement.
+
+### Conclusion
+The care of patients who are physiologically more stable on admission to the ICU is more likely to be associated with opportunities for improvement. This association is particularly pronounced in patients who ultimately die. Additionally, higher odds of opportunities for improvement in survivors and those with prolonged ICU stays suggest that extended care in complex cases may both heighten error risk and lead to longer hospitalisations.
+
+# Introduction
+Trauma — the clinical entity of external injury and the body’s associated response — is a leading cause of mortality and morbidity worldwide [@60; @6]. Patients with major trauma often require intensive care unit (ICU) admission, where managing these patients can be challenging and result in complications or death [@64; @69]. Although these complications are often expected, the complexity of ICU care makes the setting prone to mistakes and errors.
+
+Peer review is a cornerstone of trauma quality improvement programs aimed at identifying preventable causes of mortality and morbidity in trauma patients [@70], referred to as opportunities for improvement [@42]. Previous research on opportunities for improvement was recently classified in a systematic review that identified areas for improvement such as inadequate monitoring, team communication, assessment of injury and procedure-related issues [@75].
+
+In contrast, little is known about opportunities for improvement in the care of trauma patients in the ICU [@64; @12]. Assessing opportunities for improvement in these patients is challenging because it is difficult to establish the standard of care for each individual and despite optimal treatment, these patients are at risk for complications and death [@9]. This study aims to characterize opportunities for improvement in adult trauma patients admitted to the ICU and assess how patient and process factors are associated with opportunities for improvement in these patients.
+
+# Methods
+
+## Study design and data sources
+
+We conducted a retrospective single-center cohort study based on data from the Karolinska University Hospital trauma registry, and the hospital’s local trauma care quality database. The Karolinska University Hospital trauma registry is part of the Swedish national trauma registry SweTrau which follows the Utstein template [@82]. The registry includes all patients admitted with trauma team activation, regardless of Injury Severity Score, as well as patients admitted without trauma team activation but found to have an Injury Severity Score of more than 9 [@76]. The trauma care quality database includes data collected as part of the local peer review process. Screening for opportunities for improvement in selected patient cases was initiated in 2013. Since 2017 all patients have been included in the screening process.
+
+
+## Setting
+
+The Karolinska University Hospital in Solna is the trauma center designated to receive all severely injured patients in the wider metropolitan area of Stockholm [@17]. The hospital has direct access to radiology, surgery, intensive care, and consultants from relevant specialities [@18]. The care of adult trauma patients is screened for opportunities for improvement using a combination of audit filters and individual review by specialized nurses. All deceased patients, as well as those deemed as having a higher probability of opportunities for improvement, are reviewed in a multidisciplinary peer-review conducted every 6–8 weeks. The presence or absence of opportunities for improvement, including any corrective actions, is reached through consensus among the peer-review participants and subsequently recorded in the trauma care quality database. Decisions regarding the presence of preventable deaths are also made during the peer-review process.. The different ICU:s at the Karolinska University Hospital are all Level 3 as defined by the Intensive Care Society [@83].
+
+
+## Participants
+
+We included all patients who had been included in the opportunities for improvement screening process between January 1, 2013, and February 28, 2023, and who were also admitted to the ICU. We excluded patients who were younger than 15 years and patients who were dead on arrival. Patients under 15 years of age were not included in the study as their clinical and review pathway differ compared to adults. Patients who were dead on arrival were excluded as opportunities for improvement among these patients is not relevant for the aim of this study.
+
+## Variables
+
+### Outcome
+
+The outcome was the presence of any opportunity for improvement during the hospital stay, as decided by consensus during a multidisciplinary peer-review meeting. An opportunity for improvement represents preventable events in the care of patients with adverse effects on their outcomes, or recurrent deviations from safe clinical practice, which are classified into the categories missed diagnosis, delay in treatment, clinical judgment errors, inadequate protocols, inadequate resources, other errors, and preventable deaths.
+
+### Patient and process factors
+
+The variables selected for analysis were patient and process factors chosen from the trauma registry, based on the locally used audit filters, previous literature and expert opinion. The categorical factors included sex [@30; @31], mechanical ventilation [@66; @78], on-call hours [@79], ICU length of stay [@22; @26], American Society of Anesthesiologists preinjury score [@33], and 30-day mortality. Sex was categorized as male sex or female sex. Mechanical ventilation was stratified into “Not intubated”, “Mechanical ventilation 1-7 days”, “Mechanical ventilation > 7 days” and “Unknown”. On-call hours was defined as arrival to the hospital before 8.00 a.m., after 5 p.m., or during weekends – Saturday or Sunday. ICU length of stay was categorized as “≤ 7 days” or “> 7 days”. American Society of Anesthesiologists preinjury score was divided into “ASA 1-2” and “ASA 3-5”. We categorized variables to limit the number of independent variables in the multivariable analysis to adhere to the rule of thumb of ten patients with the outcome per variable [@45].
+
+The continuous variables in the analysis were age in years [@81], Revised Trauma Score [@11], Injury Severity Score [@77], and time to first computed tomography in minutes [@80]. The Revised Trauma Score is a measure of severity based on prehospital or emergency department values. In instances where hospital values for systolic blood pressure, respiratory rate, or Glasgow Coma Scale scores were missing from the emergency department records, prehospital values were used instead. If it was known that the patient was intubated, it was assumed that their Glasgow Coma Scale was three and respiratory rate was zero when calculating the Revised Trauma Score variable. Subsequently, each of the variables Glasgow Coma Scale, systolic blood pressure, and respiratory rate were converted to Revised Trauma Score codes ranging from 0 to 4 based on their respective values, and the final score was calculated using published coefficients [@71].
+
+
+## Statistical analysis
+
+We conducted a complete case analysis after handling missing values for systolic blood pressure, respiratory rate and Glasgow Coma Scale as described above. Continuous variables are presented using mean values and standard deviations, and categorical variables are presented with the number of patients in each subgroup and their respective percentages.
+
+We used logistic regression to determine unadjusted and adjusted associations between patient and process factors and opportunities for improvement. Odds ratios (OR) with associated 95% confidence intervals were calculated. A p-value of less than 0.05 were considered indicative of a significant difference. We considered 30-day mortality as a potential effect modifier and therefore conducted a stratified analysis in which the associations between patient and process factors and opportunities for improvement were estimated separately in patients who survived and patients who died within 30 days. All statistical analyses were conducted using R version 4.4 [@19].
+
+# Results
+
+## Participants
+Figure 1. Flowchart describing the inclusion and exclusion criteria of the study
+
+```{r, include=FALSE}
+source("functions/flowchart.R")
+```
+
+```{r, echo=FALSE}
+for (i in 1:(length(vert) - 1)) {
+ connectGrob(vert[[i]], vert[[i + 1]], type = "vert", arrow = small_arrow) %>%
+ print
+}
+
+connectGrob(vert$eligible, excluded, type = "L", arrow = small_arrow)
+connectGrob(vert$included, excluded1, type = "L", arrow = small_arrow)
+
+vert
+excluded
+excluded1
+```
+
+A total of 14,022 patients were included in the trauma registry and trauma care quality database between 2014 and 2023, out of which 12,278 patients were excluded, as shown in Figure 1. Out of all patients, 11,343 were excluded because they were not treated in the ICU, three patients were excluded because they were under 15 years old, six patients were excluded because they were dead on arrival, and 928 patients were excluded because they had not been screened regarding the presence of opportunities for improvement. This left us with a total of 1,742 patients eligible for the study. Out of these, 293 patients were excluded because there was missing data in one or several of the patient and process factors, leaving a total of 1,449 patients included in the study. The variable with the highest number of missing values was Revised Trauma Score, with 207 instances of missing data.
+
+## Descriptive data
+
+```{r include_scripts, include=FALSE}
+source("functions/table1.R")
+```
+
+```{r, echo=FALSE}
+table2
+```
+
+Table 1 presents sample characteristics. Most patients were male 1,096 (76%) vs 353 (24%). Of the 354 (42%) patients who were intubated, 241 (17%) patients received mechanical ventilation for 1-7 days, while 113 (7,8%) patients received mechanical ventilation for more than 7 days. The number of patients arriving outside working hours was 1,042 (72%). A total of 1,064 (73%) patients were equal to or less than 7 days in the ICU vs 385 (27%) patients whose ICU-stay exceeded 7 days. A total of 1,080 (75%) patients had an ASA score of 1-2 before arrival to the hospital, and 369 (25%) patients had an ASA score of 3-6 before arrival to the hospital. Amongst our patients, 30-day mortality was at 286 (20%). Of these, 105 patients died within 24 hours, and a total of 148 patients died within 48 hours of hospital arrival. When comparing patients with opportunities for improvement to those without, patients with opportunities for improvement were less likely to require intubation, had higher Revised Trauma Score, experienced shorter time to initial computed tomography scans, spent more days in the ICU, and demonstrated a greater likelihood of surviving 30 days post-injury.
+
+```{r, include=FALSE}
+source("functions/ofi_categories.R")
+```
+```{r, echo=FALSE}
+table_ofic
+```
+
+Table 2 illustrates opportunities for improvement divided into categories based on the definitions of opportunity for improvement types during mortality and morbidity reviews at the Karolinska University Hospital in Solna. The most frequently identified opportunity for improvement in the patient cohort of this study was clinical judgement errors, accounting for 42 cases (29%), followed by delays in treatment, observed in 33 cases (23%).
+
+## Main results
+
+```{r, include=FALSE}
+source("functions/table2.R")
+```
+
+```{r, echo=FALSE}
+table3_merge
+```
+
+Table 3 shows the unadjusted and adjusted associations between patient and process factors and opportunities for improvement. In the unadjusted analysis, Revised Trauma Score `r inline_text(table3a, variable = RTS)`, > 7 days in the ICU `r inline_text(table3a, variable = daysinICU, level = "> 7 days")` and survival 30 days after injury `r inline_text(table3a, variable = Survival, level = "Alive")`, were all significantly associated with opportunities for improvement. These variables remained significantly associated with opportunities for improvement in the adjusted analysis, with similar effect magnitudes: Revised Trauma Score `r inline_text(table3b, variable = RTS)`, > 7 days in the ICU `r inline_text(table3b, variable = daysinICU, level = "> 7 days")` and survival 30 days after injury `r inline_text(table3b, variable = Survival, level = "Alive")`. Sex, age, mechanical ventilation, Injury Severity Score, time to first computed tomography, on-call hours, and ASA preinjury were not significantly associated with opportunities for improvement in the unadjusted nor the adjusted analyses.
+
+```{r, include=FALSE}
+source("functions/table3.R")
+```
+
+```{r, echo=FALSE}
+table3b_merge
+```
+
+Table 4 shows the results of the stratified analysis with adjusted associations between patient and process factors and opportunities for improvement among the patients who were alive and dead 30 days after injury respectively. The Revised Trauma Score was significantly associated with increased odds of OFI for both patients who were alive `r inline_text(table3aalive, variable = RTS)` and dead `r inline_text(table3bdead, variable = RTS)` 30 days after injury respectively. Time to first computed tomography was significantly associated with opportunities for improvement in patients who were deceased 30 days after injury `r inline_text(table3bdead, variable = TimeFCT)`.
+
+# Discussion
+
+The key results from this study are that higher Revised Trauma Score, > 7 days length of stay in the ICU, and survival 30 days after injury were significantly associated with higher odds of opportunities for improvement. In the stratified analysis based on survival until 30 days after injury, only the Revised Trauma Score was significantly associated with higher odds of opportunities for improvement in both patient groups. Among the patients who were dead 30 days after injury, > 7 days in the ICU was significantly associated with higher odds of opportunities for improvement in addition to RTS. While these findings cannot be used to proactively identify patients at risk of medical errors or adverse events, they indicate that patients who are physiologically more stable on arrival, particularly those who ultimately die, and those who stay more than one week in the ICU are potential target groups for a focused review process, aiming to identify interventions to improve the quality of their care.
+
+The positive association between the Revised Trauma Score and opportunities for improvement, means that among the very severely injured patients admitted to the ICU, patients who are more physiologically stable upon admission were at higher odds of opportunities for improvement. One hypothetical explanation is that patients with higher Revised Trauma Score, indicating relative stability upon arrival, may experience delays in interventions overall. Previous research on all trauma patients at the Karolinska University Hospital Solna has demonstrated differences in opportunities for improvement between all trauma patients and those treated in the ICU [@12].
+
+Prolonged ICU stay was also found to be significantly associated with opportunities for improvement. This variable warrants careful consideration for two primary reasons. Firstly, patients requiring extended ICU care are often those with complex medical conditions or severe injuries. As a result, their prolonged ICU stay may signify a higher baseline risk for experiencing complications, increasing odds of opportunities for improvement. The ICU environment, while designed to provide advanced medical care, can also be fraught with complexities, including high patient acuity, numerous interventions, and a fast-paced workflow, all contributing to a higher cumulative exposure to potential errors the longer a patient remains in the ICU.
+
+Additionally, complications or opportunities for improvement might themselves lead to longer ICU stays. Conversely, shorter ICU stays may indicate patient demise before potential mistakes could occur, particularly in cases where mortality was anticipated. This is one potential explanation for why death was negatively associated with opportunities for improvement in our study. This finding is similar to previous research demonstrating prolonged ICU stays as a risk factor for increased mortality and morbidity in trauma patients [@22, @26]. Furthermore, it also corresponds with a retrospective cohort indicating that lower initial illness severity and younger age are associated with earlier discharge, further supporting the link between higher odds of opportunities for improvement and prolonged ICU stays [@25].
+
+The reason for prolonged ICU stays being associated with increased mortality has been investigated by previous studies suggesting that factors such as comorbidities, age, and illness severity upon admission do not fully explain the increased long-term mortality observed in this patient cohort. As the duration of ICU stay increases, the primary reason for ICU stay becomes less linked to the patient’s original condition and increasingly tied to persistent chronic illness accompanied by subsequent organ dysfunction. The cause of the organ dysfunction comes from increased exposure to iatrogenic factors and nosocomial infections, which exacerbate pre-existing organ dysfunction or lead to new-onset organ dysfunctions [@23, @24]. Consequently, this might be a potential explanation for the nonsignificance of predictors such as ASA preinjury and Injury Severity Score in our study. These factors primarily reflect the patient’s baseline health status and the initial injury severity, whereas the complications that arise during prolonged ICU stays are more closely associated with the ongoing healthcare interventions and exposures experienced during hospitalization which cannot be measured with ASA preinjury and Injury Severity Score.
+
+One of the strengths of this study is its reliance on registry-based data, ensuring a high level of data quality and reliability. The use of pre-defined variables standardized their definitions, enhancing the potential for similar studies to replicate these findings. Additionally, the study’s specific inclusion criteria facilitated the formation of a well-defined cohort, further contributing to the reliability and reproducibility of the study.
+
+However, several limitations need to be acknowledged in our study. This was a single-center study and primarily provide insights into the context of the Karolinska University Hospital in Solna. Another limitation is the selection of patients for the mortality and mortbidity review, which heavily relied on the local audit filters. The use of audit filters for patient selection for the mortality and morbidity review is associated with high false positive rates, ranging from 24% to 80% [@38]. Even though the use of audit filters is associated with high false positive rates, there is still a risk of misplacement where some patients with opportunities for improvement may have been overlooked. Furthermore, the handling of the variables to the Revised Trauma Score – namely respiratory rate, systolic blood pressure, and Glasgow Coma Scale – introduced potential sources of variability. The use of prehospital, emergency department, or intubation-based values for these variables may have affected data accuracy and reproducibility. Finally, the outcome opportunities for improvement is a consensus decision made in the local trauma quality improvement process. As such, these decisions have not been externally reviewed.
+
+# Conclusion
+
+The findings revealed a significant association between increased odds of opportunities for improvement and higher Revised Trauma Score, ICU length of stay longer than 7 days, and survival 30 days after injury. These findings indicate that patients who are physiologically more stable on arrival but stay more than one week in the ICU are a potential target group for a focused review process, aiming to identify interventions to improve the quality of their care.
+
+
+# References