Machine Learning Consensus Clustering of Morbidly Obese Kidney Transplant Recipients in the United States

Abstract

Background: This study aimed to better characterize morbidly obese kidney transplant recipients, their clinical characteristics, and outcomes by using an unsupervised machine learning approach. Methods: Consensus cluster analysis was applied to OPTN/UNOS data from 2010 to 2019 based on recipient, donor, and transplant characteristics in kidney transplant recipients with a pre-transplant BMI ≥ 40 kg/m2. Key cluster characteristics were identified using the standardized mean difference. Post-transplant outcomes, including death-censored graft failure, patient death, and acute allograft rejection, were compared among the clusters. Results: Consensus clustering analysis identified 3204 kidney transplant recipients with a BMI ≥ 40 kg/m2. In this cohort, five clinically distinct clusters were identified. Cluster 1 recipients were predominantly white and non-sensitized, had a short dialysis time or were preemptive, and were more likely to receive living donor kidney transplants. Cluster 2 recipients were older and diabetic. They were likely to have been on dialysis >3 years and receive a standard KDPI deceased donor kidney. Cluster 3 recipients were young, black, and had kidney disease secondary to hypertension or glomerular disease. Cluster 3 recipients had >3 years of dialysis and received non-ECD, young, deceased donor kidney transplants with a KDPI < 85%. Cluster 4 recipients were diabetic with variable dialysis duration who either received non-ECD standard KDPI kidneys or living donor kidney transplants. Cluster 5 recipients were young retransplants that were sensitized. One-year patient survival in clusters 1, 2, 3, 4, and 5 was 98.0%, 94.4%, 98.5%, 98.7%, and 97%, and one-year death-censored graft survival was 98.1%, 93.0%, 96.1%, 98.8%, and 93.0%, respectively. Cluster 2 had the worst one-year patient survival. Clusters 2 and 5 had the worst one-year death-censored graft survival. Conclusions: With the application of unsupervised machine learning, variable post-transplant outcomes are observed among morbidly obese kidney transplant recipients. Recipients with earlier access to transplant and living donation show superior outcomes. Unexpectedly, reduced graft survival in cluster 3 recipients perhaps underscores socioeconomic access to post-transplant support and minorities being disadvantaged in access to preemptive and living donor transplants. Despite obesity-related concerns, one-year patient and graft survival were favorable in all clusters, and obesity itself should be reconsidered as a hard barrier to kidney transplantation.

Distinct phenotypes of kidney transplant recipients aged 80 years or older in the USA by machine learning consensus clustering

“…Artificial intelligence and machine learning (ML) have been applied in medicine to develop clinical decision support tools that can improve and individualise healthcare, including organ transplantation 29–36. Unsupervised consensus clustering is ML adopted to identify novel data patterns and distinct subtypes 37–39.…”

Explainable Preoperative Automated Machine Learning Prediction Model for Cardiac Surgery-Associated Acute Kidney Injury

“…Artificial intelligence (AI) and machine learning (ML) have been increasingly applied to individualized medicine [ 21 , 22 , 23 , 24 , 25 , 26 ], including the prediction of AKI in various settings [ 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ]. ML algorithms can handle nonlinear, complex, and multidimensional data [ 36 , 37 ], and recent studies have shown high predictive performance from ML algorithms that outperform traditional statistical analyses [ 38 , 39 ].…”

Clinical Phenotypes of Dual Kidney Transplant Recipients in the United States as Identified through Machine Learning Consensus Clustering

“…Machine learning is a subfield of artificial intelligence that has been applied to assist clinicians in making better clinical decisions in various areas of the medical field [41][42][43]. Generally, machine learning provides three types of algorithms: supervised, unsupervised, and reinforcement learning [41][42][43]. By identifying certain similarities and differences in various input variables, a computer system can complete a task without explicit programming.…”

“…By identifying certain similarities and differences in various input variables, a computer system can complete a task without explicit programming. This is known as unsupervised machine learning [41][42][43][44]. As a result, grouping data into clinically relevant clusters can aid clinicians [42,43,[45][46][47].…”

“…This is known as unsupervised machine learning [41][42][43][44]. As a result, grouping data into clinically relevant clusters can aid clinicians [42,43,[45][46][47]. In this study, an unsupervised machine learning clustering approach was used to identify distinct clusters of DKT recipients and their clinical outcomes using the OPTN/UNOS database from 2010 through 2019.…”