Department of Emergency Medicine, Affiliated Hospital of Hangzhou Normal University, Hangzhou, 310015, Zhejiang, People's Republic of China.
Department of Emergency Intensive Care Unit, Affiliated Hospital of Hangzhou Normal University, 126 Wenzhou Road, Gongshu District, Hangzhou, 310015, Zhejiang, People's Republic of China.
Sci Rep. 2021 Nov 5;11(1):21757. doi: 10.1038/s41598-021-98558-9.
Abbreviated Injury Scale (AIS)-based systems such as injury severity score (ISS), exponential injury severity score (EISS), trauma mortality prediction model (TMPM), and injury mortality prediction (IMP), classify anatomical injuries with limited accuracy. The widely accepted alternative, trauma and injury severity score (TRISS), improves the prediction rate by combining an anatomical index of ISS, physiological index (the Revised Trauma Score, RTS), and the age of patients. The study introduced the traumatic injury mortality prediction (TRIMP) with the inclusion of extra clinical information and aimed to compare the ability against the TRISS as predictors of survival. The hypothesis was that TRIMP would outperform TRISS in prediction power by incorporating clinically available data. This was a retrospective cohort study where a total of 1,198,885 injured patients hospitalized between 2012 and 2014 were subset from the National Trauma Data Bank (NTDB) in the United States. A TRIMP model was computed that uses AIS 2005 (AIS_05), physiological reserve and physiological response indicators. The results were analysed by examining the area under the receiver operating characteristic curve (AUC), the Hosmer-Lemeshow (HL) statistic, and the Akaike information criterion. TRIMP gave both significantly better discrimination (AUC, 0.964; 95% confidence interval (CI), 0.962 to 0.966 and AUC, 0.923; 95% CI, 0.919 to 0.926) and calibration (HL, 14.0; 95% CI, 7.7 to 18.8 and HL, 411; 95% CI, 332 to 492) than TRISS. Similar results were found in statistical comparisons among different body regions. TRIMP was superior to TRISS in terms of accurate of mortality prediction, TRIMP is a new and feasible scoring method in trauma research and should replace the TRISS.
基于损伤严重度评分（ISS）的损伤严重度评分（ISS）、指数损伤严重度评分（EISS）、创伤死亡率预测模型（TMPM）和损伤死亡率预测（IMP）等损伤严重度评分（AIS）系统对解剖损伤的分类准确性有限。广泛接受的替代方案创伤和损伤严重度评分（TRISS）通过结合解剖学指数 ISS、生理学指数（修订后的创伤评分，RTS）和患者年龄来提高预测率。本研究引入了创伤性损伤死亡率预测（TRIMP），纳入了额外的临床信息，并旨在比较其作为生存预测因子的能力与 TRISS。假设 TRIMP 通过纳入临床可用数据在预测能力上优于 TRISS。这是一项回顾性队列研究，其中共有 1198885 名 2012 年至 2014 年期间住院的受伤患者从美国国家创伤数据库（NTDB）中分为亚组。计算了一种使用 AIS 2005（AIS_05）、生理储备和生理反应指标的 TRIMP 模型。通过检查接受者操作特征曲线下面积（AUC）、Hosmer-Lemeshow（HL）统计量和 Akaike 信息准则来分析结果。TRIMP 具有显著更好的区分度（AUC，0.964；95%置信区间（CI），0.962 至 0.966 和 AUC，0.923；95%CI，0.919 至 0.926）和校准度（HL，14.0；95%CI，7.7 至 18.8 和 HL，411；95%CI，332 至 492）优于 TRISS。在不同身体部位的统计比较中也发现了类似的结果。TRIMP 在死亡率预测的准确性方面优于 TRISS，TRIMP 是创伤研究中的一种新的可行评分方法，应该取代 TRISS。
