UCSF

Introduction Injury is a major cause of morbidity and mortality in low- and middle-income countries. Effective trauma surveillance is imperative to guide research and quality improvement interventions, so an accurate metric for quantifying injury severity is crucial. The objectives of this study are (1) to assess the feasibility of calculating five injury scoring systems - ISS (injury severity score), RTS (revised trauma score), KTS (Kampala trauma score), MGAP (mechanism, GCS (Glasgow coma score), age, pressure) and GAP (GCS, age, pressure) - with data from a trauma registry in a lower middle-income country and (2) to determine which of these scoring systems most accurately predicts in-hospital mortality in this setting. Patients and methods This is a retrospective analysis of data from an institutional trauma registry in Mumbai, India. Values for each score were calculated when sufficient data were available. Logistic regression was used to compare the correlation between each score and in-hospital mortality. Results There were sufficient data recorded to calculate ISS in 73% of patients, RTS in 35%, KTS in 35%, MGAP in 88% and GAP in 92%. ISS was the weakest predictor of in-hospital mortality, while RTS, KTS, MGAP and GAP scores all correlated well with in-hospital mortality (area under ROC (receiver operating characteristic) curve 0.69 for ISS, 0.85 for RTS, 0.86 for KTS, 0.84 for MGAP, 0.85 for GAP). Respiratory rate measurements, missing in 63% of patients, were a major barrier to calculating RTS and KTS. Conclusions Given the realities of medical practice in low- and middle-income countries, it is reasonable to modify the approach to characterising injury severity to favour simplified injury scoring systems that accurately predict in-hospital mortality despite limitations in trauma registry datasets.