Dynamic Lactate Clearance Patterns During Normothermic Machine Perfusion Predict Posttransplant Biliary Complications in Donation After Circulatory Death Liver Transplantation.

Background: Normothermic machine perfusion (NMP) enables real-time assessment of liver graft viability through various biomarkers. Although lactate clearance during NMP has been associated with graft outcomes, its dynamic patterns and relationship with posttransplant biliary complications (PTBCs) remain poorly understood. We sought to evaluate whether dynamic lactate clearance patterns during NMP could predict PTBCs in both donations after circulatory death (DCD) and brain death (DBD) liver transplantation (LT).

Methods: We conducted a single-center retrospective study of 140 LT recipients (88 DCDs, 52 DBDs) undergoing NMP (2022-2024). K-means clustering analysis was applied to lactate clearance patterns during the first 3 h of NMP. The primary outcome was PTBCs. Multivariate logistic regression analysis was used to identify independent predictors for PTBC.

Results: K-means clustering revealed 3 distinct patterns: consistently low (cluster A), rapid clearance (cluster B), and prolonged elevation (cluster C). DCD grafts demonstrated more pronounced differences in lactate clearance and higher rates of persistent elevation than DBD grafts. For DCD grafts, the distribution was cluster A (26.1%, n = 23), cluster B (37.5%, n = 33), and cluster C (36.4%, n = 32), whereas DBD grafts showed cluster A (55.8%, n = 29), cluster B (17.3%, n = 9), and cluster C (26.9%, n = 14). In DCD grafts, PTBC rates were significantly higher in cluster C (37.5%) than in clusters A (13.0%,  = 0.045) and B (15.2%,  = 0.04). This association remained significant in multivariate analysis (adjusted odds ratio, 4.76; 95% confidence interval, 1.03-21.9;  = 0.045). No differences in recipient or donor variables were observed across clusters. Among DBD grafts, no intercluster differences in PTBC rates were found.

Conclusions: Dynamic lactate clearance patterns during NMP predict PTBCs in DCD LT. This novel approach may enable more precise risk stratification and guide posttransplant surveillance strategies for DCD grafts.