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The End of the Injury Severity Score (ISS) and the Trauma and Injury Severity Score (TRISS)

损伤严重程度评分 修正创伤评分 医学 急诊医学 伤害预防 毒物控制
作者
Robert Rutledge,Turner Osler,Sherry Emery,Sharon Kromhout-Schiro
出处
期刊:Journal of Trauma-injury Infection and Critical Care [Lippincott Williams & Wilkins]
卷期号:44 (1): 41-49 被引量:222
标识
DOI:10.1097/00005373-199801000-00003
摘要

Introduction Since their inception, the Injury Severity Score (ISS) and the Trauma and Injury Severity Score (TRISS) have been suggested as measures of the quality of trauma care. In concept, they are designed to accurately assess injury severity and predict expected outcomes. ICISS, an injury severity methodology based on International Classification of Diseases, Ninth Revision, codes, has been demonstrated to be superior to ISS and TRISS. The purpose of the present study was to compare the ability of TRISS to ICISS as predictors of survival and other outcomes of injury (hospital length of stay and hospital charges). It was our hypothesis that ICISS would outperform ISS and TRISS in each of these outcome predictions. Methods "Training" data for creation of ICISS predictions were obtained from a state hospital discharge data base. "Test" data were obtained from a state trauma registry. ISS, TRISS, and ICISS were compared as predictors of patient survival. They were also compared as indicators of resource utilization by assessing their ability to predict patient hospital length of stay and hospital charges. Finally, a neural network was trained on the ICISS values and applied to the test data set in an effort to further improve predictive power. The techniques were compared by comparing each patient's outcome as predicted by the model to the actual outcome. Results Seven thousand seven hundred five patients had complete data available for analysis. The ICISS was far more likely than ISS or TRISS to accurately predict every measure of outcome of injured patients tested, and the neural network further improved predictive power. Conclusion In addition to predicting mortality, quality tools that can accurately predict resource utilization are necessary for effective trauma center quality-improvement programs. ICISS-derived predictions of survival, hospital charges, and hospital length of stay consistently outperformed those of ISS and TRISS. The neural network-augmented ICISS was even better. This and previous studies demonstrate that TRISS is a limited technique in predicting survival resource utilization. Because of the limitations of TRISS, it should be superseded by ICISS.

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