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 yielded the best results. ANOVA was used to compare VL characteristics, demographics, comorbidities, substance uses, and longitudinal CD4 count across clusters. Results: Results obtained from FPCA could best distinguish the characteristics and patterns into four clusters. A total of 5916 PWH were grouped into long-term VS group (Cluster 1, 17.3%), short-term VS group (Cluster 2, 29.8%), suboptimal VS group (Cluster 3, 28.3%), and viral failure group (Cluster 4, 24.9%). In the long-term VS group with an average of 11-year follow-up, PWH displayed sustained VS (95.3%) and lower mean CD4 count (95.3%) than other clusters. The short-term VS group had shorter follow-up (6 years), more comorbidities (31.4%), and lower percentage of time with low CD4 count (79.9%). In suboptimal VS group, PWH were mostly under 30 years old (44.8%) and Black (77.2%), with relatively lower mean VL (92.9%) and lower VR history (18.4%). In the viral failure group, PWH had higher mean VL (40.6%) and lower mean CD4 count (34.7%). Discussion: The findings highlight the impact of continuous clustering in understanding the distinct viral profiles of PWH and emphasize the importance of tailored treatment and insights to target interventions for all PWH.