Akamatsu, Nobuhisa

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. We conducted a multicenter retrospective cohort study from 17 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. The development cohort included 2944 adult LDLT recipients (67.7% male; median age 55 y; median MELD 14) with a 5.5% EAF rate. External validation included 1020 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 with 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. Preoperative and intraoperative clinical factors predict EAF in LDLT. The EAGLE-LDLT model accurately identifies LDLT recipients at the highest risk for EAF postoperatively.

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.

Objective: To define benchmark values for adult-to-adult living-donor liver transplantation (LDLT). Background: LDLT utilizes living-donor hemiliver grafts to expand the donor pool and reduce waitlist mortality. Although references have been established for donor hepatectomy, no such information exists for recipients to enable conclusive quality and comparative assessments. Methods: Patients undergoing LDLT were analyzed in 15 high-volume centers (≥10 cases/year) from 3 continents over 5 years (2016–2020), with a minimum follow-up of 1 year. Benchmark criteria included a Model for End-stage Liver Disease ≤20, no portal vein thrombosis, no previous major abdominal surgery, no renal replacement therapy, no acute liver failure, and no intensive care unit admission. Benchmark cutoffs were derived from the 75th percentile of all centers’ medians. Results: Of 3636 patients, 1864 (51%) qualified as benchmark cases. Benchmark cutoffs, including posttransplant dialysis (≤4%), primary nonfunction (≤0.9%), nonanastomotic strictures (≤0.2%), graft loss (≤7.7%), and redo-liver transplantation (LT) (≤3.6%), at 1-year were below the deceased donor LT benchmarks. Bile leak (≤12.4%), hepatic artery thrombosis (≤5.1%), and Comprehensive Complication Index (CCI ® ) (≤56) were above the deceased donor LT benchmarks, whereas mortality (≤9.1%) was comparable. The right hemiliver graft, compared with the left, was associated with a lower CCI ® score (34 vs 21, P < 0.001). Preservation of the middle hepatic vein with the right hemiliver graft had no impact neither on the recipient nor on the donor outcome. Asian centers outperformed other centers with CCI ® score (21 vs 47, P < 0.001), graft loss (3.0% vs 6.5%, P = 0.002), and redo-LT rates (1.0% vs 2.5%, P = 0.029). In contrast, non-benchmark low-volume centers displayed inferior outcomes, such as bile leak (15.2%), hepatic artery thrombosis (15.2%), or redo-LT (6.5%). Conclusions: Benchmark LDLT offers a valuable alternative to reduce waitlist mortality. Exchange of expertise, public awareness, and centralization policy are, however, mandatory to achieve benchmark outcomes worldwide.