Improving the TRISS Methodology by Restructuring Age Categories and Adding Comorbidities

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Abstract
The Trauma and Injury Severity Score (TRISS) methodology was developed to predict the probability of survival after trauma. Despite many criticisms, this methodology remains in common use. The purpose of this study was to show that improving the stratification for age and adding an adjustment for comorbidity significantly increases the predictive accuracy of the TRISS model. The trauma registry and the hospital administrative database of a regional trauma center were used to identify all blunt trauma patients older than 14 years of age admitted with International Classification of Diseases, Ninth Revision codes 800 to 959 from April 1993 to March 2001. Each individual medical record was then reviewed to ascertain the Revised Trauma Score, the Injury Severity Score, the age of the patients, and the presence of eight comorbidities. The outcome variable was the status at discharge: alive or dead. The study population was divided into two subsamples of equal size using a random sampling method. Logistic regression was used to develop models on the first subsample; a second subsample was used for cross-validation of the models. The original TRISS and three TRISS-derived models were created using different categorizations of Revised Trauma Score, Injury Severity Score, and age. A new model labeled TRISSCOM was created that included an additional term for the presence of comorbidity. There were 5,672 blunt trauma patients, 2,836 in each group. For original TRISS, the Hosmer-Lemeshow statistic (HL) was 179.1 and the area under the receiver operating characteristic (AUROC) curve was 0.873. Sensitivity and specificity were 99.0% and 27.8%, respectively. For the best modified TRISS model, the HL statistic was 20.35, the AUROC curve was 0.902, the sensitivity was 99.0%, and the specificity was 27.8%. For TRISSCOM, the HL statistic was 14.95 and the AUROC curve was 0.918. Sensitivity and specificity were 99.0% and 29.7%, respectively. The difference between the two models almost reached statistical significance (p = 0.086). When TRISSCOM was applied to the cross-validation group, the HL statistic was 10.48 and the AUROC curve was 0.914. The sensitivity was 98.6% and the specificity was 34.9%. TRISSCOM can predict survival more accurately than models that do not include comorbidity. A better categorization of age and the inclusion of comorbid conditions in the logistic model significantly improves the predictive performance of TRISS.