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Modification of the Trauma and Injury Severity Score (TRISS) method provides better survival prediction in Asian blunt trauma victims

机译:创伤和损伤严重程度评分(TRISS)方法的修改为亚洲钝伤受害者提供了更好的生存预测

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Background: The objective of the present study was to identify logistic regression models with better survival prediction than the Trauma and Injury Severity Score (TRISS) method in assessing blunt trauma (BT) victims in Japan and Thailand. An additional aim was to demonstrate the feasibility of probability of survival (Ps) estimation without respiratory rate (RR) on admission, which is often missing or unreliable in Asian countries. Methods: We used BT patient data (n = 15,524) registered in the Japan Trauma Data Bank (JTDB, 2005-2008). We also extracted data on BT patients injured in the Khon Kaen District between January 2005 and December 2008 (n = 6,411) from the Khon Kaen Hospital Trauma Registry. For logistic regression analyses, we chose the Injury Severity Score (ISS), age year (AY), Glasgow Coma Scale (GCS) score, systolic blood pressure (SBP), RR, and their coded values (c) as explanatory variables, as well as the Revised Trauma Score (RTS). We estimated parameters by the method of maximum likelihood estimation, and utilized Akaike's Information Criterion (AIC), the area under the receiver operating characteristic curve (AUROCC), and accuracy for model comparison. A model having the lower AIC is considered to be the better model. Results: The AIC of the model using AY was lower than that of the model using the coded value for AY (cAY) (used by the TRISS method). The model using ISS, AY and cGCS, cSBP, and cRR instead of the RTS demonstrated the lowest AIC in both data groups. The same trend could be observed in the AUROCCs and the accuracies. In the Khon Kaen data, we found no additional reduction of the AIC in the model using the cRR variable compared to the model without cRR. Conclusions: For better prediction of Ps, the actual number of the AY should be used as an explanatory variable instead of the coded value (used by the TRISS method). The logistic regression model using the ISS, AY, and coded values of SBP, GCS, and RR estimates the best prediction. Information about RR seems to be unimportant for survival prediction in BT victims in Asian countries.
机译:背景:本研究的目的是确定在评估日本和泰国的钝伤(BT)受害者时,生存预测比创伤和损伤严重程度评分(TRISS)方法更好的逻辑回归模型。另一个目的是证明在没有呼吸频率(RR)的情况下估计生存概率(Ps)的可行性,而在亚洲国家这通常是缺失或不可靠的。方法:我们使用在日本创伤数据库(JTDB,2005-2008年)中注册的BT患者数据(n = 15,524)。我们还从孔敬医院创伤登记处提取了2005年1月至2008年12月之间在孔敬地区受伤的BT患者的数据(n = 6,411)。为了进行逻辑回归分析,我们选择了损伤严重度评分(ISS),年龄年份(AY),格拉斯哥昏迷评分(GCS)评分,收缩压(SBP),RR及其编码值(c)作为解释变量,如下以及修订的创伤评分(RTS)。我们通过最大似然估计的方法估计参数,并利用Akaike的信息准则(AIC),接收器工作特性曲线下的面积(AUROCC)和模型比较的准确性。具有较低AIC的模型被认为是更好的模型。结果:使用AY的模型的AIC低于使用AY的编码值(cAY)(由TRISS方法使用)的模型的AIC。使用ISS,AY和cGCS,cSBP和cRR而不是RTS的模型在两个数据组中均显示出最低的AIC。在AUROCC和精度中可以观察到相同的趋势。在孔敬数据中,我们发现与没有cRR的模型相比,使用cRR变量的模型在AIC方面没有其他降低。结论:为了更好地预测Ps,应将AY的实际数量用作解释变量,而不要使用编码值(TRISS方法使用)。使用ISS,AY和SBP,GCS和RR的编码值的逻辑回归模型可估计最佳预测。关于RR的信息对于亚洲国家BT受害者的生存预测似乎并不重要。

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