Validation of a Pretransplant Risk Prediction Model for Early Allograft Dysfunction After Living-donor Liver...

Area of Science:

• Hepatology
• Transplant Surgery
• Medical Informatics

Background:

• Early allograft dysfunction (EAD) impacts liver transplantation (LT) outcomes.
• Existing risk models for deceased-donor LT are inadequate for living-donor LT (LDLT).
• Pretransplant evaluation is critical for preventing EAD and justifying donor risks in LDLT.

Purpose of the Study:

• To develop and validate a predictive model for EAD specifically for LDLT.
• To identify key pretransplant predictors of EAD in LDLT recipients.
• To enhance the safety and efficacy of LDLT through improved donor-recipient pairing.

Main Methods:

• Retrospective analysis of 2944 adult LDLT patients across 17 centers (2016-2020).
• Development of a logistic regression model to predict EAD using a development cohort.
• External validation of the model using data from 1020 patients at the King Faisal Transplant Center.

Main Results:

• 10.9% of patients in the development cohort experienced EAD.
• Independent pretransplant predictors of EAD included MELD score, hospitalization necessity, and graft weight.
• The developed EAD model showed strong discriminative ability (AUC 0.71) in the development cohort, with good performance in internal (AUC 0.70) and external validation (AUC 0.65).

Conclusions:

• Recipient pretransplant health status and graft weight are significant factors influencing EAD post-LDLT.
• The developed model for EAD after LDLT can optimize donor-recipient matching.
• Integrating this model can improve the safety and effectiveness of LDLT procedures.