Re‐assessing prolonged cold ischemia time in kidney transplantation through machine learning consensus clustering
Caroline C. Jadlowiec, Charat Thongprayoon, Supawit Tangpanithandee, Rachana Punukollu, Napat Leeaphorn, Matthew Cooper, Wisit Cheungpasitporn- Transplantation
Abstract
Background
We aimed to cluster deceased donor kidney transplant recipients with prolonged cold ischemia time (CIT) using an unsupervised machine learning approach.
Methods
We 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.
Results
Consensus 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.
Conclusions
Unsupervised 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.