Validating performance of TRISS, TARN and NORMIT survival prediction models in a Norwegian trauma population

Abstract

Introduction: Anatomic injury, physiological derangement, age, injury mechanism and pre-injury comorbidity are well-founded predictors of trauma outcome. Statistical prediction models may have poorer discrimination, calibration and accuracy when applied in new locations. We aimed to compare the TRISS, TARN and NORMIT survival prediction models in a Norwegian trauma population. Methods: Consecutive patients admitted to Oslo University Hospital Ullev al within 24 h after injury, with Injury Severity Score ≥ 10, proximal penetrating injuries, or received by trauma team, were studied. Original NORMIT coefficients were updated in a derivation dataset (NORMIT 2; n = 5923; 2005–2009). TRISS, TARN and NORMIT prediction models were evaluated in the validation dataset (n = 6348; 2010–2013) using two different AIS editions for injury coding. Exclusion due to missing data was 0.26%. Outcome was 30-day mortality. Validation included AUROC, scaled Brier statistics, and calibration plots. Results: The NORMIT models had significantly better discrimination, calibration, and overall fit than the TRISS 09, TARN 09 and TARN 12 models. The updated NORMIT 2 had higher numerical values of AUROC and scaled Brier than the original NORMIT, but with overlapping 95%CI. Overlapping 95%CI for AUROCs and Discrimination slopes indicated that the TARN and TRISS models performed similarly. Calibration plots showed tight and consistent predictions over all Ps strata for NORMIT 2 run on AIS’98 coded data, and only little deterioration when AIS’08 data was substituted. Conclusions: In a Norwegian trauma population, the updated Norwegian survival prediction model in trauma (NORMIT 2) performed better than well-established British and US alternatives. External validation of these three models in other Nordic populations is warranted.