title: Predicting in-hospital death among patients injured in traffic crashes in Saudi Arabia.
abstract: BACKGROUND
  Traffic-related injuries are a major cause of premature death in developing countries. Saudi Arabia has struggled with high rates of traffic-related deaths for decades, yet little is known about health outcomes of motor vehicle victims seeking medical care. This study aims to develop and validate a model to predict in-hospital death among patients admitted to a large-urban trauma centre in Saudi Arabia for treatment following traffic-related crashes.
  
  
  METHODS
  The analysis used data from King Abdulaziz Medical City (KAMC) in Riyadh, Saudi Arabia. During the study period 2001-2010, 5325 patients met the inclusion criteria of being injured in traffic crashes and seen in the Emergency Department (ED) and/or admitted to the hospital. Backward stepwise logistic regression, with in-hospital death as the outcome, was performed. Variables with p<0.05 were included in the final model. The Bayesian Information Criterion (BIC) was employed to identify the most parsimonious model. Model discrimination was evaluated by the C-statistic and calibration by the Hosmer-Lemeshow Goodness of Fit statistic. Bootstrapping was used to assess overestimation of model performance and obtain a corrected C-statistic.
  
  
  RESULTS
  457 (8.5%) patients died at some time during their treatment in the ED or hospital. Older age, the Triage-Revised Trauma Scale (T-RTS), and Injury Severity Score were independent risk factors for in-hospital death: T-RTS was best modelled with linear and quadratic terms to capture a flattening of the relationship to death in the more severe range. The model showed excellent discrimination (C-statistic=0.96) and calibration (H-L statistic 4.29 [p>0.05]). Internal bootstrap validation gave similar results (C-statistic=0.96).
  
  
  CONCLUSIONS
  The proposed model can predict in-hospital death accurately. It can facilitate the triage process among injured patients, and identify unexpected deaths in order to address potential pitfalls in the care process. Conversely, by identifying high-risk patients, strategies can be developed to improve trauma care for these patients and reduce case-fatality. This is the first study to develop and validate a model to predict traffic-related mortality in a developing country. Future studies from developing countries can use this study as a reference for case fatality achievable for different risk profiles at a well-equipped trauma centre.
authors.affiliation: King Abdullah International Medical Research Center, King Saud Bin Abdulaziz University for Health Sciences, KAIMRC, KSAU-HS, Riyadh, Saudi Arabia. Electronic address: Ghnams@ngha.med.sa.
authors.forename: Suliman
authors.initials: S
authors.lastname: Alghnam
authors.affiliation: Population Health Sciences, University of Wisconsin-Madison, United States.
authors.forename: Mari
authors.initials: M
authors.lastname: Palta
authors.affiliation: Emergency Medicine, University of Wisconsin-Madison, United States.
authors.forename: Azita
authors.initials: A
authors.lastname: Hamedani
authors.affiliation: King Abdullah International Medical Research Center, King Saud Bin Abdulaziz University for Health Sciences, KAIMRC, KSAU-HS, Riyadh, Saudi Arabia.
authors.forename: Mohammad
authors.initials: M
authors.lastname: Alkelya
authors.affiliation: Population Health Sciences, University of Wisconsin-Madison, United States.
authors.forename: Patrick L
authors.initials: PL
authors.lastname: Remington
authors.affiliation: Population Health Sciences, University of Wisconsin-Madison, United States.
authors.forename: Maureen S
authors.initials: MS
authors.lastname: Durkin
mesh_terms: D000063:Accidents, Traffic / Q000401:mortality* / Q000517:prevention & control; D004636:Emergency Service, Hospital; D005260:Female; D006301:Health Services Needs and Demand; D017052:Hospital Mortality / Q000639:trends; D006801:Humans; D015601:Injury Severity Score; D008297:Male; D011237:Predictive Value of Tests; D012189:Retrospective Studies; D012307:Risk Factors; D012529:Saudi Arabia / Q000453:epidemiology; D014193:Trauma Centers / Q000458:organization & administration / Q000706:statistics & numerical data*; D014218:Triage; D014947:Wounds and Injuries / Q000401:mortality* / Q000517:prevention & control
keywords: In-hospital death; Injury prevention; Motor vehicle; Prognostic models; Saudi Arabia; Severity