Data-driven cluster analysis identifies three clinical phenotypes in hemodialysis patients.

ABSTRACT: Clinical heterogeneity among hemodialysis patients necessitates precision medicine approaches transcending conventional single-parameter management. Through machine learning analysis of 1,207 maintenance hemodialysis patients, we developed a novel two-tier phenotyping framework integrating unsupervised K-means clustering across 22 clinical indicators with supervised classification using six universally available biomarkers. Five mechanistically informed composite indicators were constructed, including the Middle-Small Molecule Clearance Index (β₂-microglobulin reduction ratio × Kt/V) and ferritin-hemoglobin ratio, achieving superior discriminatory capacity over traditional approaches. Three distinct metabolic phenotypes emerged with exceptional stability (Adjusted

SUBMITTER: Chen C 

PROVIDER: S-EPMC12636659 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature