Discrimination and Calibration of the Estimated Post-Transplant Survival Model by Race

Background: The Estimated Post-Transplant Survival (EPTS) score is widely used to predict survival in kidney transplant recipients and guide high-longevity kidney allocation. Prior studies identified racial disparities in access to high-longevity kidneys under this system, but EPTS performance across racial groups has not been directly evaluated. We evaluated EPTS model performance across racial groups. Methods: This retrospective cohort study included first-time adult deceased donor kidney transplant recipients from the U.S. Scientific Registry of Transplant Recipients (2013–2023). Cox proportional hazards models estimated mortality using Raw EPTS, with an interaction term for race (White, Black, Other). Hazard ratios (HRs) per unit Raw EPTS were calculated for each group. Discrimination was assessed using overall and time-dependent Harrell’s C-statistic, compared using DeLong’s test. Calibration was evaluated at 1, 3, and 5 years using calibration plots. Results: Among 123,952 recipients, 67,027 (54%) were White, 44,471 (36%) were Black, and 12,454 (10%) were Other. A significant interaction between Raw EPTS and race was detected (p<0.001). The HR per unit Raw EPTS was higher for White recipients (HR 3.66; 95% CI: 3.53, 3.80) than Black recipients (HR 2.91; 95% CI: 2.80, 3.02). Discrimination was slightly lower for Black recipients (C-statistic 0.678) than White recipients (0.712, p<0.001). Re-fitting the EPTS coefficients within each race group did not improve discrimination. Calibration was best at 1 year, declining at 3 and 5 years, but remained similar across groups. Conclusion: The association between Raw EPTS and mortality differed by race, with a stronger impact in White recipients. Discrimination was modestly lower for Black recipients, reducing EPTS’s ability to rank risk. Despite this, calibration was similar, providing accurate 1-year survival predictions across groups. These findings support continued EPTS use but highlight the need to monitor model performance, ensure fairness, and explore ways to improve baseline risk-stratification for Black recipients.