Characterizing Cluster-Based Frailty Phenotypes in a Multicenter Prospective Cohort of Kidney Transplant Candidates

Abstract

Frailty is associated with a higher risk of death among kidney transplant candidates. Currently available frailty indices are often based on clinical impression, physical exam or an accumulation of deficits across domains of health. In this paper we investigate a clustering based approach that partitions the data based on similarities between individuals to generate phenotypes of kidney transplant candidates. We analyzed a multicenter cohort that included several features typically used to determine an individual’s level of frailty. We present a clustering based phenotyping approach, where we investigated two clustering approaches—i.e. neural network based Self-Organizing Maps (SOM) with hierarchical clustering, and KAMILA (KAy-means for MIxed LArge data sets). Our clustering results partition the individuals across 3 distinct clusters. Clusters were used to generate and study feature-level phenotypes of each group.