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

Abstract

Background: Early allograft dysfunction (EAD) affects outcomes in liver transplantation (LT). Existing risk models developed for deceased-donor LT depend on posttransplant factors and fall short in living-donor LT (LDLT), where pretransplant evaluations are crucial for preventing EAD and justifying the donor's risks.

Methods: This retrospective study analyzed data from 2944 adult patients who underwent LDLT at 17 centers between 2016 and 2020. We developed a logistic regression model to predict EAD based on this development cohort. We used data from 1020 patients at the King Faisal Transplant Center for external validation.

Results: In the development cohort, 321 patients (10.9%) experienced EAD. These patients had poorer health status, more liver decompensation, and higher requirements of hospitalization than those without EAD. Multivariable logistic regression identified independent pretransplant predictors of EAD: laboratory Model for End-Stage Liver Disease score (odds ratio [OR], 1.08; 95% confidence interval [CI], 1.06-1.09), the necessity for hospitalization at the time of transplant (OR, 2.58; 95% CI, 2.00-3.30), and graft weight in kilogram (OR, 0.27; 95% CI, 0.17-0.45). Using these predictors, we developed the model for EAD after LDLT, which demonstrated strong discriminative ability in the development cohort with an area under the curve (AUC) of 0.71 (95% CI, 0.68-0.74). The model maintained high discrimination during internal validation (AUC, 0.70; 95% CI, 0.67-0.73) and showed a modest reduction in discriminative power in external validation (AUC, 0.65; 95% CI, 0.61-0.68).

Conclusions: EAD post-LDLT is influenced by the recipient's pretransplant health condition and the graft weight. Integrating the model for EAD after LDLT into the pretransplant process of pairing donors and recipients can enhance the safety and efficacy of LDLT.