Comprehensive comparative analysis of eight predictive indices for survival after deceased donor liver transplantation: a retrospective cohort study

초록

Purpose: Deceased donor liver transplantation (DDLT) remains constrained by organ shortages, emphasizing the need for accurate predictive models to optimize allocation and improve outcomes. Although multiple indices, such as Model for End-Stage Liver Disease (MELD), MELD-Na, and donor-specific models, exist to predict recipient and graft survival, their comparative performance remains uncertain. Methods: This study compared 8 predictive models (MELD, MELD-Na, Gender-Equity Model for Liver Allocation [GEMA], GEMA-Na, Donor Rejection Organ Procurement Evaluation, Donor Risk Index, Kidney Donor Profile Index [KDPI], and Korean KDPI) in predicting overall and graft survival after DDLT. Data from 328 donor-recipient pairs at Samsung Medical Center (2000-2020) were retrospectively analyzed. Calibration was assessed using the Hosmer-Lemeshow test and R-2 values, and discrimination using the Kaplan-Meier curves, concordance statistics (c-index), and receiver operating characteristic curves. Results: MELD-Na demonstrated the highest calibration and discrimination for both overall and graft survival, particularly at short-term follow-up. Recipient-based models (MELD, GEMA) showed satisfactory performance, with MELD closely following MELD-Na in predicting overall survival. In contrast, donor-based indices (KDPI, K-KDPI) exhibited weak predictive power. Integrating recipient and donor parameters improved the overall comprehensiveness of prediction. Conclusion: MELD-Na is the most reliable predictor of short-term survival after DDLT. Donor-specific indices provide valuable insights into organ quality but are limited in survival prediction. Integrating both donor and recipient factors may refine organ allocation and enhance posttransplant outcomes. Further validation across diverse populations and the incorporation of advanced technologies, including machine learning, are warranted to optimize predictive accuracy in liver transplantation.

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