Early graft failure after adult living donor liver transplantation: A multicenter risk analysis and development of the early allograft failure in living donor liver transplantation (EAGLE-LDLT) model.
Academic Article
Overview
abstract
BACKGROUND: Early allograft failure (EAF) after living donor liver transplantation (LDLT) remains a clinical challenge. Existing prediction models developed for deceased donor transplantation poorly apply to LDLT due to distinct surgical and physiological factors. This study identifies clinical determinants of EAF and develops an LDLT-specific prediction model. METHODS: We conducted a multicenter retrospective cohort study from seventeen high-volume LDLT centers (January 2016-December 2020) with external validation at a tertiary center in Saudi Arabia (January 2015-December 2022). The primary outcome was EAF (graft loss or patient death ≤90 d). Multivariable mixed-effects logistic regression identified preoperative/intraoperative risk factors. The EAGLE-LDLT model was constructed using postoperative laboratory values. Performance was compared against established models (EAD, MEAF, A2ALL) using ROC analysis and decision curve analysis. RESULTS: The development cohort included 2,944 adult LDLT recipients (67.7% male; median age 55 y; median MELD 14) with 5.5% EAF rate. External validation included 1,020 recipients (median MELD 21, 6.7% EAF). Independent risk factors for EAF were MELD (OR 1.06, 95%CI 1.04-1.08), donor BMI (OR 1.05, 95%CI 1.00-1.10), portal vein thrombosis (OR 1.73, 95%CI 1.13-2.63), and hepaticojejunostomy (OR 1.58, 95%CI 1.06-2.36). The EAGLE-LDLT model incorporating peak ALT (>468 U/L), peak INR (>1.9), and bilirubin (>3.5 mg/dL) and INR (>1.3) at POD7, demonstrated superior discrimination (AUC=0.81) compared to MEAF (AUC=0.77, p=0.004), EAD (AUC=0.67, p<0.001), and A2ALL (AUC=0.65, p<0.001). EAGLE-LDLT achieved balanced sensitivity (75.0%) and specificity (73.7%), effectively stratifying patients into high-risk (15% of patients; 40.4% EAF incidence) and low-risk groups. CONCLUSION: Preoperative and intraoperative clinical factors predict EAF in LDLT. The EAGLE-LDLT model accurately identifies LDLT recipients at highest risk for EAF postoperatively.
