Post-Liver Transplant Outcomes: A Comparative Study of 6 Predictive Models

Area of Science:

• Transplantation Medicine
• Medical Informatics
• Biostatistics

Background:

• Liver transplantation (LT) outcomes rely on accurate risk prediction.
• Several risk scores exist, but their comparative performance needs evaluation.

Purpose of the Study:

• To compare the predictive performance of the Liver Transplant Risk Score (LTRS) against other established scores for post-LT outcomes.
• To assess the accuracy of LTRS and pretransplant SOFT (P-SOFT) using only preoperative data.

Main Methods:

• Retrospective analysis of 82,696 adult LT recipients from the Scientific Registry of Transplant Recipients (2002-2021).
• Comparison of LTRS, SOFT, P-SOFT, BAR, D-MELD, and ORPS for predicting 90-day mortality, 1-year mortality, and 5-year survival.
• Analysis of Area Under the Curve (AUC) and correlation coefficients.

Main Results:

• LTRS and P-SOFT demonstrated comparable predictive performance for 90-day and 1-year mortality (AUCs ranging from 0.60-0.66).
• All models showed strong perioperative risk stratification capabilities.
• LTRS and P-SOFT exhibited high correlation (0.90-0.91) for 90-day and 1-year mortality prediction.

Conclusions:

• No single model perfectly predicted 90-day or 1-year mortality after LT.
• LTRS and P-SOFT are valuable tools for preoperative risk stratification in LT candidates.
• These preoperative models aid in identifying patients at significant risk for adverse outcomes.