Background: We developed simple methods of risk adjustment for evaluating the quality of injury care (predicting survival probabilities of the injured) by fully utilizing routinely collected data in injury surveillance and clinical practices. Widely used methods of risk adjustment require additional data that are difficult to collect in resource-constrained settings.
Methods: We developed logistic regression models that predict survival using data obtained from 9,840 victims aged 15 years or older with blunt traumatic injuries who were registered in the Japan Trauma Data Bank, Japan's national trauma registry, between January 2004 and December 2007. The models included three predictors: age, an anatomical injury severity parameter such as a simplified severity categorization (minor, moderate, and severe) described in the Injury Surveillance Guidelines, and a physiological status parameter. The models' abilities to predict survival probabilities were evaluated using the area under the receiver-operating characteristic curve (AUROCC).
Results: The simplified three-predictor models showed good performance with the AUROCC ranging from 0.86 to 0.94. In particular, the models with a consciousness level indicator as a physiological parameter showed a high AUROCC, ranging from 0.93 to 0.94, which was not much different from the performance of the widely used method that shows an AUROCC of 0.96.
Conclusions: Simplified methods of risk adjustment that require only routinely collected data will facilitate evaluation and improvement in the quality of injury care in resource-constrained low- and middle-income countries, where injuries are a growing public health concern.