Beyond Mortality: Does Trauma-related Injury Severity Score Predict Complications or Lengths of Stay Using a Large Administrative Dataset
Journal of emergencies, trauma, and shock
INTRODUCTION: Despite its shortcomings, trauma-related injury severity score (TRISS) correlates well with mortality in large trauma datasets. The aim of this study was to determine if TRISS correlates with morbidity and hospital lengths of stay using data from an institutional registry at a Level I Trauma Center. We hypothesized that higher TRISS correlates with increased complications and longer hospital stays. METHODS: A retrospective review of our institutional registry was performed, examining all trauma admissions between January 1999 and June 30, 2015. Out of a total of 32,026 patient records, TRISS data were available in 23,205 cases. Abstracted data included patient age, gender, ISS, TRISS, presence of complication, Glasgow Coma Scale (GCS), hospital length of stay, intensive care unit LOS, step-down unit LOS, functional independence measure, and 30-day mortality. RESULTS: TRISS was highly predictive of mortality, with the AUC value of 0.95 (95% confidence interval 0.936-0.954, < 0.01) compared to ISS (AUC 0.794), GCS (AUC 0.827), and age (AUC 0.650). TRISS also performed better than the other variables in terms of the ability to predict morbidity events (AUC 0.813). TRISS was comparable to ISS in terms of prediction of ICU admission (AUC 0.801 versus 0.811, respectively). After correcting for patient age and gender, higher TRISS significantly correlated with longer hospital stays . CONCLUSIONS: Despite previous criticisms, we found that TRISS is superior to ISS for mortality and morbidity prediction. TRISS correlated significantly with a hospital, step down, and ICU lengths of stay using a large administrative dataset.
Stewart, Nakosi; MacConchie, James G.; Castillo, Roberto; Thomas, Peter G.; Cipolla, James; and Stawicki, Stanislaw P., "Beyond Mortality: Does Trauma-related Injury Severity Score Predict Complications or Lengths of Stay Using a Large Administrative Dataset" (2021). Regional Trauma Network @SLUHN Articles & Publications. 11.