The Trauma and Injury Severity Score (TRISS) revised
The Trauma and Injury Severity Score (TRISS) revised
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DOI：
10.1016/j.injury.2010.08.040
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发表时间：
2011-01-01
影响因子：
2.5
通讯作者：
Schluter, Philip J.
中科院分区：
文献类型：
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作者：
Schluter, Philip J.
背景：创伤和伤害严重程度评分 (TRISS) 仍然是衡量创伤死亡结果最常用的工具。最近，事实证明，通过对组成变量重新分类并名义上处理变量类别，可以显着提高 TRISS 的预测能力。本研究旨在使用重新分类的变量开发修订的 TRISS 模型，评估这些模型相对于现有 TRISS 模型的预测性能，并确定和推荐首选 TRISS 模型。 材料和方法：针对钝性和穿透性损伤机制的修订 TRISS 模型是在国家创伤数据库国家样本项目 (NSP) 的成人（年龄 >= 15 岁）样本上开发的，使用 5 类变量分类和加权逻辑回归。然后，使用接受者操作特征曲线 (AUC) 和贝叶斯信息标准 (BIC) 统计数据，根据未加权 NSP、国家创伤数据库 (NTDB) 和新西兰数据库 (NZDB) 样本上的现有 TRISS 模型评估他们的预测性能。结果：加权 NSP 样本包括 1,124,001 名患有钝性或穿透性损伤机制事件且已知出院状态的成年人，其中 1,061,709 名成人(94.5%) 存活并出院。 896,212 人（79.7%）可获得所有 TRISS 变量的完整信息。对于 NSP、NTDB 和 NZDB 样本中具有完整数据的患者，包含主效应和双因素交互作用项的修订 TRISS 模型具有优于主效应模型和现有 TRISS 模型的 AUC 和 BIC 统计数据。随着修订后的 TRISS 模型中包含的缺失值变量数量的增加，预测性能下降，但模型性能总体上仍优于现有 TRISS 模型。讨论：修订后的 TRISS 模型比现有 TRISS 模型显着提高了预测能力。此外，它们很容易计算，仅利用为现有 TRISS 模型收集的那些变量，并且当一个或多个预测变量包含缺失值时可以应用并产生有意义的生存概率。首选的修订 TRISS 模型包括主效应和双因素交互作用项，并允许所有预测变量中存在缺失值。有充分的理由用这种首选的修订版 TRISS 模型替换创伤评分系统基准测试软件中的现有 TRISS 模型。 (C) 2010 Elsevier Ltd. 保留所有权利。
Background: The Trauma and Injury Severity Score (TRISS) remains the most commonly used tool for benchmarking trauma fatality outcome. Recently, it was demonstrated that the predictive power of TRISS could be substantially improved by re-classifying the component variables and treating the variable categories nominally. This study aims to develop revised TRISS models using re-classified variables, to assess these models' predictive performances against existing TRISS models, and to identify and recommend a preferred TRISS model.Materials and methods: Revised TRISS models for blunt and penetrating injury mechanism were developed on an adult (aged >= 15 years) sample from the National Trauma Data Bank National Sample Project (NSP), using 5-category variable classifications and weighted logistic regression. Their predictive performances were then assessed against existing TRISS models on the unweighted NSP, National Trauma Data Bank (NTDB), and New Zealand Database (NZDB) samples using area under the Receiver Operating Characteristic curve (AUC) and Bayesian Information Criterion (BIC) statistics.Results: The weighted NSP sample included 1,124,001 adults with blunt or penetrating injury mechanism events and known discharge status, of whom 1,061,709 (94.5%) survived to discharge. Complete information for all TRISS variables was available for 896,212 (79.7%). Revised TRISS models that included main-effects and two-factor interaction terms had superior AUC and BIC statistics to main-effects models and existing TRISS models for patients with complete data in NSP, NTDB and NZDB samples. Predictive performance decreased as the number of variables with missing values included within revised TRISS models increased, but model performances generally remained superior to existing TRISS models.Discussion: Revised TRISS models had importantly improved predictive capacities over existing TRISS models. Additionally, they were easily computed, utilised only those variables already collected for existing TRISS models, and could be applied and produce meaningful survival probabilities when one or more of the predictor variables contained missing values. The preferred revised TRISS model included main-effects and two-factor interaction terms and allowed for missing values in all predictor variables. A strong case exists for replacing existing TRISS models in trauma scoring systems benchmarking software with this preferred revised TRISS model. (C) 2010 Elsevier Ltd. All rights reserved.