Moon Jonghwan, Hwang Kyungjin, Yoon Dukyong, Jung Kyoungwon
Division of Trauma Surgery, Department of Surgery, Ajou University School of Medicine and Graduate School of Medicine, Suwon, Korea.
Department of Biomedical Informatics, Ajou University School of Medicine and Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Korea.
Acute Crit Care. 2020 May;35(2):102-109. doi: 10.4266/acc.2019.00780. Epub 2020 May 31.
This study aimed to develop a model for predicting trauma outcomes by adding arterial lactate levels measured upon emergency room (ER) arrival to existing trauma injury severity scoring systems.
We examined blunt trauma cases that were admitted to our hospital during 2010- 2014. Eligibility criteria were cases with an Injury Severity Score of ≥9, complete Trauma and Injury Severity Score (TRISS) variable data, and lactate levels that were assessed upon ER arrival. Survivor and non-survivor groups were compared and lactate-based prediction models were generated using logistic regression. We compared the predictive performances of traditional prediction models (Revised Trauma Score [RTS] and TRISS) and lactate-based models using the area under the curve (AUC) of receiver operating characteristic curves.
We included 829 patients, and the in-hospital mortality rate among these patients was 21.6%. The model that used lactate levels and age provided a significantly better AUC value than the RTS model. The model with lactate added to the TRISS variables provided the highest Youden J statistic, with 86.0% sensitivity and 70.8% specificity at a cutoff value of 0.15, as well as the highest predictive value, with a significantly higher AUC than the TRISS.
These findings indicate that lactate testing upon ER arrival may help supplement or replace traditional physiological parameters to predict mortality outcomes among Korean trauma patients. Adding lactate levels also appears to improve the predictive abilities of existing trauma outcome prediction models.
本研究旨在通过将急诊室（ER）就诊时测得的动脉血乳酸水平添加到现有的创伤损伤严重程度评分系统中，开发一种预测创伤结局的模型。
我们检查了2010年至2014年期间入住我院的钝性创伤病例。纳入标准为损伤严重程度评分≥9分、创伤和损伤严重程度评分（TRISS）变量数据完整且在ER就诊时评估了乳酸水平的病例。比较存活组和非存活组，并使用逻辑回归生成基于乳酸的预测模型。我们使用受试者操作特征曲线的曲线下面积（AUC）比较了传统预测模型（修订创伤评分[RTS]和TRISS）与基于乳酸的模型的预测性能。
我们纳入了829例患者，这些患者的院内死亡率为21.6%。使用乳酸水平和年龄的模型提供的AUC值明显优于RTS模型。在TRISS变量中添加乳酸的模型提供了最高的约登J统计量，在临界值为0.15时，灵敏度为86.0%，特异度为70.8%，以及最高的预测价值，其AUC明显高于TRISS。
这些发现表明，ER就诊时进行乳酸检测可能有助于补充或替代传统生理参数，以预测韩国创伤患者的死亡率结局。添加乳酸水平似乎也能提高现有创伤结局预测模型的预测能力。
