Alghnam Suliman, Palta Mari, Hamedani Azita, Alkelya Mohammad, Remington Patrick L, Durkin Maureen S
King Abdullah International Medical Research Center, King Saud Bin Abdulaziz University for Health Sciences, KAIMRC, KSAU-HS, Riyadh, Saudi Arabia.
Population Health Sciences, University of Wisconsin-Madison, United States.
Injury. 2014 Nov;45(11):1693-9. doi: 10.1016/j.injury.2014.05.029. Epub 2014 Jun 2.
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.
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.
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).
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.
在发展中国家，与交通相关的伤害是过早死亡的主要原因。几十年来，沙特阿拉伯一直面临着与交通相关的高死亡率问题，但对于寻求医疗救治的机动车受害者的健康结局却知之甚少。本研究旨在开发并验证一个模型，以预测在沙特阿拉伯一家大型城市创伤中心因交通相关碰撞事故入院治疗的患者的院内死亡情况。
分析使用了沙特阿拉伯利雅得阿卜杜勒阿齐兹国王医疗城（KAMC）的数据。在2001年至2010年的研究期间，5325名患者符合在交通事故中受伤并在急诊科（ED）就诊和/或入院的纳入标准。以院内死亡为结局进行了向后逐步逻辑回归分析。p<0.05的变量被纳入最终模型。采用贝叶斯信息准则（BIC）来确定最简约的模型。通过C统计量评估模型的区分度，通过Hosmer-Lemeshow拟合优度统计量评估校准度。使用自助法评估模型性能的高估情况并获得校正后的C统计量。
457名（8.5%）患者在急诊科或医院治疗期间的某个时间死亡。年龄较大、分诊修订创伤量表（T-RTS）和损伤严重程度评分是院内死亡的独立危险因素：T-RTS最好用线性和二次项建模，以捕捉在更严重范围内与死亡关系的平缓情况。该模型显示出出色的区分度（C统计量=0.96）和校准度（H-L统计量4.29 [p>0.05]）。内部自助验证给出了类似的结果（C统计量=0.96）。
所提出的模型能够准确预测院内死亡情况。它可以促进受伤患者的分诊过程，并识别意外死亡情况，以便解决护理过程中的潜在问题。相反，通过识别高危患者，可以制定策略来改善对这些患者的创伤护理并降低病死率。这是第一项在发展中国家开发并验证预测交通相关死亡率模型的研究。发展中国家的未来研究可以将本研究作为参考，了解在设备完善的创伤中心不同风险特征所能达到的病死率情况。
