@article{ref1,
title="Predicting in-hospital death among patients injured in traffic crashes in Saudi Arabia",
journal="Injury",
year="2014",
author="Alghnam, Suliman and Palta, Mari and Hamedani, Azita G. and Alkelya, Mohammad and Remington, Patrick L. and Durkin, Maureen S.",
volume="45",
number="11",
pages="1693-1699",
abstract="INTRODUCTION: 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. <br><br>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. <br><br>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). <br><br>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.<p /> <p>Language: en</p>",
language="en",
issn="0020-1383",
doi="10.1016/j.injury.2014.05.029",
url="http://dx.doi.org/10.1016/j.injury.2014.05.029"
}