Gwendolyn B. Henry

Background: Normothermic machine perfusion (NMP) is an emerging strategy for pediatric liver transplantation, but pediatric‐specific viability criteria remain undefined. Lactate clearance, extrapolated from adult protocols, is a central marker of allograft function. In our pediatric NMP (pNMP) experience, we observed a novel pattern: initial clearance followed by secondary rise, termed rebound lactic acidosis. Methods: We retrospectively reviewed pediatric liver allografts preserved on the TransMedics Organ Care System at two centers (January 2025 through June 2026). After excluding donors > 35 kg, 18 perfusions (17 transplants) were classified as rebound lactic acidosis (lactate rise > 0.5 mmol/L after initial clearance) or sustained clearance. Rebound cases were characterized by lactate kinetics, perfusion parameters, acid–base status, bile production, histology, and recipient outcomes. Results: Six allografts demonstrated rebound, with onset 1.0–6.6 h into perfusion. All sustained bile production throughout perfusion. Circuit alkalosis temporally correlated with rebound in three cases and improved with gas flow reduction. The first allograft was discarded; the remaining five were transplanted, including one perfused for 35.5 h. Recipients had elevated POD1 transaminases and INR that normalized by POD7; three are discharged home and two remain hospitalized, all with functioning allografts (follow‐up up to 312 days). Conclusions: Rebound lactic acidosis during pNMP may not indicate allograft nonviability and may be ameliorated by modifying circuit perfusion or pH. Lactate kinetics should be interpreted within a multiparametric viability framework incorporating perfusion adequacy, acid–base status, bile production, liver function, and histology. Pediatric‐specific criteria are needed to safely expand the donor pool.

Objective: To determine whether machine learning (ML) models, specifically Extreme Gradient Boosting (XGBoost), improve the prediction of graft and patient survival compared with Scientific Registry of Transplant Recipients (SRTR) regression models across heart, lung, liver, and kidney transplants. Background: Accurate prediction of post-transplant outcomes is essential for organ allocation and regulatory oversight. Current SRTR models rely on regression techniques that may not capture complex donor-recipient interactions. ML offers the potential for improved predictive accuracy. Methods: A retrospective cohort study analyzed United Network for Organ Sharing data from 1987 to 2023 for heart, lung, liver, and kidney transplants. Cox proportional hazards models (SRTR) were reconstructed and compared with XGBoost models. Outcomes included graft and patient survival at 1 and 3 years. Model performance was assessed using area under the curve and DeLong’s test. Results: XGBoost models outperformed SRTR models for 1-year graft survival: heart (area under the curve 0.698 vs 0.576), kidney (0.736 vs 0.649), liver (0.706 vs 0.616), and lung (0.612 vs 0.572). Similar improvements were observed for patient survival and 3-year outcomes, with statistical significance for most organs except lung graft survival at 1 year. Conclusions and Relevance: ML models provide superior predictive accuracy compared with traditional regression-based models for transplant outcomes. While current clinical utility remains limited, these findings support further development and integration of ML techniques to enhance regulatory evaluations and optimize patient care.

Background/Objectives: There is a critical disparity between donor organs and recipients awaiting liver transplantation. Normothermic machine perfusion (NMP) has emerged as a promising strategy with which to address this shortage. This study aimed to evaluate the association between NMP and short-term outcomes following liver transplantation using a large, multivariable-adjusted national dataset. Methods: A retrospective analysis of the de-identified United Network for Organ Sharing (UNOS) database was conducted for adult liver transplant recipients between January 2020 and July 2024. Standard and deceased donor data were merged according to donor identification number. Multivariable logistic and Cox regression models were used to evaluate patient mortality, graft failure, and hospital length of stay (LOS). Results: Among 34,115 patients, adjusted regression demonstrated lower one-year patient mortality (OR 0.68, CI 0.54–0.86, p = 0.001) and graft failure (OR 0.72, CI 0.60–0.87, p = 0.001) with NMP compared to static cold storage. NMP was also associated with reduced 30-day (OR 0.67, CI 0.47–0.95, p = 0.03) and 90-day mortality (OR 0.72, CI 0.54–0.94, p = 0.02) and shorter LOS (HR 1.06, CI 1.01–1.12, p = 0.02). Kaplan–Meier and Cox analyses showed no significant differences in overall patient mortality or graft failure. Conclusions: NMP was associated with improved short-term survival in time-independent analysis; however, it failed to reach significance in time-dependent Cox regression. These findings suggest that NMP could play a role in improving short-term outcomes and expanding the donor pool for liver transplant candidates. Additional studies are needed to fully elucidate the impact of NMP on short-term survival outcomes.

Background.: Consideration of both donor and recipient factors at the time of an organ offer is crucial in achieving the most optimal outcome given the shortage of available donor organs and waitlist mortality risk. Our aim was to achieve a comprehensive risk assessment for liver transplant patients by creating a series of independent risk index equations for 6 distinct posttransplant endpoints in the transplant continuum of care. Methods.: The Survival Outcomes Following Liver Transplantation (SOFT) Cluster score includes risk indices for patient mortality at 90 d, 1 y, 3 y, and 5 y posttransplant, graft failure at 1 y after transplantation, and prolonged length of stay (>30 d). Using a total sample of 61 231 patients, significant risk and protective factors were identified through univariable and multivariable analysis from a training set of 40 821 patients from the Organ Procurement and Transplantation Network database. Results.: When applied to a validation set of 20 410 patients, the SOFT Cluster score has a C -statistic of 0.66 for 90-d mortality, 0.64 for 1-y mortality, 0.63 for 3-y mortality, 0.63 for 5-y mortality, 0.76 for prolonged length of stay, and 0.60 for 1-y graft failure. Conclusions.: The SOFT Cluster score represents a novel proof-of-concept model for posttransplant risk assessment and provides physicians with a more holistic understanding of patient risk by examining a spectrum of outcomes after liver transplantation up to 5 y posttransplant using a modern cohort of recipients.