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首页> 外文期刊>Acute and critical care. >Inclusion of lactate level measured upon emergency room arrival in trauma outcome prediction models improves mortality prediction: a retrospective, single-center study
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Inclusion of lactate level measured upon emergency room arrival in trauma outcome prediction models improves mortality prediction: a retrospective, single-center study

机译：在急诊室到达创伤成果预测模型时含有乳酸水平提高死亡率预测：回顾性，单中心研究

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BACKGROUND: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.METHODS: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.RESULTS: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.CONCLUSIONS: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.
机译：背景：本研究旨在通过在急诊室（ER）到达现有的创伤伤害严重程度评分系统中，制定一种通过添加动脉乳酸水平来预测创伤结果的模型。方法：我们在2010年期间检查了钝的创伤病例2014年。资格标准是患有伤势严重程度≥9，完整的创伤和伤害严重程度评分（Triss）可变数据的病例，并在ER到达时评估的乳酸水平。比较幸存者和非幸存者组，并使用逻辑回归产生基于乳酸的预测模型。比较了传统预测模型的预测性能（修正了创伤评分[RTS]和Triss]和基于乳酸的模型，使用了接收器操作特征曲线的曲线（AUC）下的区域。结果：我们包括829名患者，以及这些患者中的医院死亡率为21.6％。使用乳酸水平和年龄的模型提供了比RTS模型更好的AUC值。添加到Triss变量的乳液的模型提供了最高的Yende J统计，灵敏度为86.0％，截止值为0.15的截止值和70.8％，以及最高的预测值，具有比Triss的最高的Auc .conclusions：这些发现表明，ER到来时的乳酸试验可以帮助补充或取代传统的生理参数来预测韩国创伤患者的死亡率结果。添加乳酸水平也似乎改善了现有创伤结果预测模型的预测能力。

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