Objective: We evaluated the transferability of prediction models between trauma care contexts in India and the United States and explored updating methods to adjust such models for new contexts. Study Design and Settings: Using a combination of prospective cohort and registry data from 3,728 patients of Towards Improved Trauma Care Outcomes in India (TITCO) and from 18,756 patients of the US National Trauma Data Bank (NTDB), we derived models in one context and validated them in the other, assessing them for discrimination and calibration using systolic blood pressure, heart rate, and Glasgow coma scale as candidate predictors. Results: Early mortality was 8% in the TITCO and 1e2% in the NTDB samples. Both models discriminated well, but the TITCO model overestimated the risk of mortality in NTDB patients, and the NTDB model underestimated the risk in TITCO patients. Conclusion: Transferability was good in terms of discrimination but poor in terms of calibration. It was possible to improve this miscalibration by updating the models’ intercept. This updating method could be used in samples with as few as 25 events.