Leeaphorn, Napat

ABSTRACTBackground and AimsOne strategy to minimize the risk of posttransplant lymphoproliferative disorder (PTLD) is to avoid an Epstein–Barr Virus (EBV) mismatch through kidney paired donation. We aimed to estimate the incidence of PTLD in EBV‐negative kidney transplant recipients with EBV‐positive donors (D+/R−) and evaluate the excess risk of death following the occurrence of PTLD.MethodsWe included adult patients in OPTN database who underwent kidney transplants between 2012 and 2022. Cox regression analysis was employed to evaluate the impact of EBV serostatus on the development of PTLD, and to assess mortality following PTLD diagnosis in D+/R− individuals.ResultsA total of 179 068 patients were included, with 92.8% in the R+, 6.4% in the D+/R−, and 0.8% in the D−/R− group. D+/R− exhibited a significantly higher risk of PTLD compared to R+ and D−/R− groups. D+/R− had a greater risk of PTLD when compared to D−/R− recipients (aHR: 3.82; p < 0.001). Among D+/R− recipients, those who developed PTLD had a significantly higher risk of mortality (aHR: 2.28; p < 0.001 in deceased donor kidney transplant [DDKT] and aHR: 5.22; p < 0.001 in living donor kidney transplant [LDKT]).ConclusionsD+/R− recipients have nearly a fourfold higher risk of PTLD compared to D−/R− recipients, suggesting that choosing an EBV D−/R− transplant could reduce the PTLD risk by about 73%. This data is crucial for counseling EBV‐negative patients considering kidney paired donation to avoid an EBV serostatus mismatch.

ABSTRACTBackgroundDespite many people awaiting kidney transplant, kidney allografts from acute kidney injury (AKI) donors continue to be underutilized. We aimed to cluster kidney transplant recipients of AKI kidney allografts using an unsupervised machine learning (ML) approach.MethodsUsing Organ Procurement and Transplantation Network–United Network for Organ Sharing (OPTN/UNOS) data, a consensus clustering cohort analysis was performed in 12 356 deceased donor kidney transplant recipients between 2015 and 2019 in whom donors had a terminal serum creatinine ≥1.5 mg/dL. Significant cluster characteristics were determined, and outcomes were compared.ResultsThe median donor terminal creatinine was 2.2 (interquartile range [IQR] 1.7–3.3) mg/dL. Cluster analysis was performed on 12 356 AKI kidney recipients, and three clinically distinct clusters were identified. Young, sensitized kidney re‐transplant patients characterized Cluster 1. Cluster 2 was characterized by first‐time kidney transplant patients with hypertensive and diabetic kidney diseases. Older diabetic recipients characterized Cluster 3. Clusters 1 and 2 donors were young and met standard kidney donor profile index (KDPI) criteria; Cluster 3 donors were older, more likely to have hypertension or diabetes, and meet high KDPI criteria. Cluster 1 had a higher risk of acute rejection, 3‐year patient death, and graft failure. Cluster 3 had a higher risk of death‐censored graft failure, patient death, and graft failure at 1 and 3 years. Cluster 2 had the best patient‐, graft‐, and death‐censored graft survival at 1 and 3 years. Compared to non‐AKI kidney recipients, the AKI clusters showed a higher incidence of delayed graft function (DGF, AKI: 43.2%, 41.7%, 45.3% vs. non‐AKI: 25.5%); however, there were comparable long‐term outcomes specific to death‐censored graft survival (AKI: 93.6%, 93.4%, 90.4% vs. non‐AKI: 92.3%), patient survival (AKI: 89.1%, 93.2%, 84.2% vs. non‐AKI: 91.2%), and overall graft survival (AKI: 84.7%, 88.2%, 79.0% vs. non‐AKI: 86.0%).ConclusionsIn this unsupervised ML approach study, AKI recipient clusters demonstrated differing, but good clinical outcomes, suggesting opportunities for transplant centers to incrementally increase kidney utilization from AKI donors.

AbstractBackgroundWe aimed to cluster deceased donor kidney transplant recipients with prolonged cold ischemia time (CIT) using an unsupervised machine learning approach.MethodsWe performed consensus cluster analysis on 11 615 deceased donor kidney transplant patients with CIT exceeding 24 h using OPTN/UNOS data from 2015 to 2019. Cluster characteristics of clinical significance were identified, and post‐transplant outcomes were compared.ResultsConsensus cluster analysis identified two clinically distinct clusters. Cluster 1 was characterized by young, non‐diabetic patients who received kidney transplants from young, non‐hypertensive, non‐ECD deceased donors with lower KDPI scores. In contrast, the patients in cluster 2 were older and more likely to have diabetes. Cluster 2 recipients were more likely to receive transplants from older donors with a higher KDPI. There was lower use of machine perfusion in Cluster 1 and incrementally longer CIT in Cluster 2. Cluster 2 had a higher incidence of delayed graft function (42% vs. 29%), and lower 1‐year patient (95% vs. 98%) and death‐censored (95% vs. 97%) graft survival compared to Cluster 1.ConclusionsUnsupervised machine learning characterized deceased donor kidney transplant recipients with prolonged CIT into two clusters with differing outcomes. Although Cluster 1 had more favorable recipient and donor characteristics and better survival, the outcomes observed in Cluster 2 were also satisfactory. Overall, both clusters demonstrated good survival suggesting opportunities for transplant centers to incrementally increase CIT.