Longitudinal Viral Load Clustering for People With HIV Using Functional Principal Component Analysis.

ABSTRACT: Background: Longitudinal measures of viral load (VL) are critical in monitoring the HIV status. While multiple lab indicators exist for monitoring measures of VL, research on clustering historical/longitudinal VL measures is limited. Analyzing longitudinal VL patterns rather than aggregated measures offers deeper insights into HIV status. This study uses functional data clustering to classify longitudinal VL patterns and characterize each cluster by demographics, comorbidities, social behaviors, and CD4 count. Methods: Adult PWH diagnosed from 2005 to 2015 in South Carolina with a 5-year minimum follow-up were included. We compared functional principal component analysis (FPCA), K-means, hierarchical clustering, and Gaussian mixture models for classification and found FPCA yi

SUBMITTER: Ma Y 

PROVIDER: S-EPMC11824709 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature