• Suliman Alghnam, Mari Palta, Azita Hamedani, Mohammad Alkelya, Patrick L Remington, and Maureen S Durkin.
• 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.
• Injury. 2014 Nov 1;45(11):1693-9.

IntroductionTraffic-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.MethodsThe 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.Results457 (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).ConclusionsThe 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.Copyright © 2014 Elsevier Ltd. All rights reserved.

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