Validation of a base deficit-based trauma prediction model and comparison with TRISS and ASCOT

Base deficit provides a more objective indicator of physiological stress following injury as compared with vital signs constituting the revised trauma score (RTS). We have previously developed a base deficit-based trauma survival prediction model [base deficit and injury severity score model (BISS)], in which RTS was replaced by base deficit as a measurement of physiological imbalance.

To externally validate BISS in a large cohort of trauma patients and to compare its performance with established trauma survival prediction models including trauma and injury severity score (TRISS) and a severity characterization of trauma (ASCOT). Moreover, we examined whether the predictive accuracy of BISS model could be improved by replacement of injury severity score (ISS) by new injury severity score (NISS) in the BISS model (BNISS).

In this retrospective, observational study, clinical data of 3737 trauma patients (age ≥15 years) admitted consecutively from 2003 to 2007 were obtained from a prospective trauma registry to calculate BISS, TRISS, and ASCOT models. The models were evaluated in terms of discrimination [area under curve (AUC)] and calibration.

The in-hospital mortality rate was 8.1 %. The discriminative performance of BISS to predict survival was similar to that of TRISS and ASCOT [AUCs of 0.883, 95 % confidence interval (CI) 0.865-0.901 for BISS, 0.902, 95 % CI 0.858-0.946 for TRISS and 0.864, 95 % CI 0.816-0.913 for ASCOT]. Calibration tended to be optimistic in all three models. The updated BNISS had an AUC of 0.918 indicating that substitution of ISS with NISS improved model performance.

The BISS model, a base deficit-based trauma model for survival prediction, showed equivalent performance as compared with that of TRISS and ASCOT and may offer a more simplified calculation method and a more objective assessment. Calibration of BISS model was, however, less good than that of other models. Replacing ISS by NISS can considerably improve model accuracy, but further confirmation is needed.