Validation of a novel prediction model for early mortality in adult trauma patients in three public university hospitals in urban India

Background: Trauma is one of the top threats to population health globally. Several prediction models have been developed to supplement clinical judgment in trauma care. Whereas most models have been developed in high-income countries the majority of trauma deaths occur in low- and middle-income countries. Almost 20 % of all global trauma deaths occur in India alone. The aim of this study was to validate a basic clinical prediction model for use in urban Indian university hospitals, and to compare it with existing models for use in early trauma care. Methods: We conducted a prospective cohort study in three hospitals across urban India. The model we aimed to validate included systolic blood pressure and Glasgow coma scale. We compared this model with three additional models, which all have been designed for use in bedside trauma care, and two single variable models based on systolic blood pressure and Glasgow coma scale respectively. The outcome was early mortality, defined as death within 24 h from the time when vital signs were first measured. We compared the models in terms of discrimination, calibration, and potential clinical consequences using decision curve analysis. Multiple imputation was used to handle missing data. Performance measures are reported using their median and inter-quartile range (IQR) across imputed datasets. Results: We analysed 4440 patients, out of which 1629 were used as an updating sample and 2811 as a validation sample. We found no evidence that the basic model that included only systolic blood pressure and Glasgow coma scale had worse discrimination or potential clinical consequences compared to the other models. A model that also included heart had better calibration. For the model with systolic blood pressure and Glasgow coma scale the discrimination in terms of area under the receiver operating characteristics curve was 0.846 (IQR 0.841-0.849). Calibration measured by estimating a calibration slope was 1.183 (IQR 1.168-1.202). Decision curve analysis revealed that using this model could potentially result in 45 fewer unnecessary surveys per 100 patients. Conclusions: A basic clinical prediction model with only two parameters may prove to be a feasible alternative to more complex models in contexts such as the Indian public university hospitals studied here. We present a colour-coded chart to further simplify the decision making in early trauma care.