Reexamining Transplant Outcomes in Acute Kidney Injury Kidneys Through Machine Learning
Issued Date
2024-10-01
Resource Type
ISSN
09020063
eISSN
13990012
Scopus ID
2-s2.0-85206003870
Journal Title
Clinical Transplantation
Volume
38
Issue
10
Rights Holder(s)
SCOPUS
Bibliographic Citation
Clinical Transplantation Vol.38 No.10 (2024)
Suggested Citation
Jadlowiec C.C., Thongprayoon C., Suppadungsuk S., Tangpanithandee S., Leeaphorn N., Heilman R., Cooper M., Cheungpasitporn W. Reexamining Transplant Outcomes in Acute Kidney Injury Kidneys Through Machine Learning. Clinical Transplantation Vol.38 No.10 (2024). doi:10.1111/ctr.15470 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/101635
Title
Reexamining Transplant Outcomes in Acute Kidney Injury Kidneys Through Machine Learning
Corresponding Author(s)
Other Contributor(s)
Abstract
Background: Despite 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. Methods: Using 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. Results: The 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%). Conclusions: In 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.