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

Area of Science:

• Nephrology
• Biomarkers
• Machine Learning

Background:

• Hemodialysis patient care is complex due to clinical heterogeneity.
• Conventional management often relies on single parameters, limiting personalized treatment.
• Precision medicine offers a path to individualized care strategies.

Purpose of the Study:

• To develop a novel, data-driven phenotyping framework for hemodialysis patients.
• To integrate unsupervised and supervised machine learning for robust patient classification.
• To establish a foundation for phenotype-guided interventions in precision nephrology.

Main Methods:

• Utilized K-means clustering on 22 clinical indicators from 1,207 hemodialysis patients.
• Employed supervised classification with six key biomarkers.
• Constructed five composite indicators, including Middle-Small Molecule Clearance Index and ferritin-hemoglobin ratio.

Main Results:

• Identified three stable metabolic phenotypes: high retention-inflammatory (19.5%), optimal clearance (24.3%), and intermediate-stable (56.0%).
• The six-parameter model demonstrated high accuracy (>88%) and AUC (0.893-0.919).
• Phenotypes were characterized by distinct profiles of dialysis adequacy, inflammation, and iron status.

Conclusions:

• The developed framework provides a stable and accurate method for hemodialysis patient phenotyping.
• This approach facilitates personalized interventions, such as intensified dialysis or clearance optimization.
• The study lays the groundwork for algorithm-driven, individualized care in hemodialysis, potentially improving outcomes and resource allocation.