The counterintuitive effect of multiple injuries in severity scoring: a simple variable improves the predictive ability of NISS

BACKGROUND: Injury scoring is important to formulate prognoses for trauma patients. Although scores based on empirical estimation allow for better prediction, those based on expert consensus, e.g. the New Injury Severity Score (NISS) are widely used. We describe how the addition of a variable quantifying the number of injuries improves the ability of NISS to predict mortality. METHODS: We analyzed 2488 injury cases included into the trauma registry of the Italian region Emilia-Romagna in 2006-2008 and assessed the ability of NISS alone, NISS plus number of injuries, and the maximum Abbreviated Injury Scale (AIS) to predict in-hospital mortality. Hierarchical logistic regression was used. We measured discrimination through the C statistics, and calibration through Hosmer-Lemeshow statistics, Akaike's information criterion (AIC) and calibration curves. RESULTS: The best discrimination and calibration resulted from the model with NISS plus number of injuries, followed by NISS alone and then by the maximum AIS (C statistics 0.775, 0.755, and 0.729, respectively; AIC 1602, 1635, and 1712, respectively). The predictive ability of all the models improved after inclusion of age, gender, mechanism of injury, and the motor component of Glasgow Coma Scale (C statistics 0.889, 0.898, and 0.901; AIC 1234, 1174, and 1167). The model with NISS plus number of injuries still showed the best performances, this time with borderline statistical significance. CONCLUSIONS: In NISS, the same weight is assigned to the three worst injuries, although the contribution of the second and third to the probability of death is smaller than that of the worst one. An improvement of the predictive ability of NISS can be obtained adjusting for the number of injuries.