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Nevin Manimala Statistics

Depression improves the predictive accuracy of the VACS Index 2.0 for all-cause mortality among sexual minority men living with HIV in the Multicenter AIDS Cohort Study

J Acquir Immune Defic Syndr. 2025 Aug 26. doi: 10.1097/QAI.0000000000003752. Online ahead of print.

ABSTRACT

BACKGROUND: The Veterans Aging Cohort Study (VACS) Index 2.0 accurately predicts mortality using age and clinical biomarkers, but adding behavioral and psychosocial factors that are common among sexual minority men (SMM) may improve its predictive accuracy. We examined whether adding these factors would improve mortality prediction among SMM living with HIV.

METHODS: We included 1,438 SMM in the Multicenter AIDS Cohort Study (MACS) who initiated highly active antiretroviral therapy (HAART) for at least one year between January 1996 and September 2022. We divided the sample into development (70%) and validation (30%) sets. We used Cox proportional hazards models to develop new indices in the development set by adding binary behavioral and psychosocial factors (depression, cigarette smoking, heavy alcohol use, polydrug use) or the total number of these factors in the VACS Index 2.0 and estimated mortality using Weibull survival models. We compared accuracy using C-statistics and calibration curves in the validation set and within subgroups (age, race, CD4 count, and viral suppression).

RESULTS: Among the 1,438 SMM, 83 (5.8%) died within 5 years of follow-up. Depression significantly predicted 5-year mortality after adjusting for the VACS Index 2.0 and resulted in a 70% increased risk of death (aHR=1.70, 95% CI=1.10-2.63) compared to men without depression. The addition of depression improved C-statistics from 0.818 to 0.851 in the development set. Results were robust in all subgroups.

CONCLUSIONS: Including depression improved the VACS Index 2.0 in predicting mortality. Screening and treating depression could improve health and reduce mortality among SMM living with HIV.

PMID:40857055 | DOI:10.1097/QAI.0000000000003752

By Nevin Manimala

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