Machine Learning Analysis Reveals Novel Neuroimaging and Clinical Signatures of Frailty in HIV.

ABSTRACT:

Background

Frailty is an important clinical concern for the aging population of people living with HIV (PLWH). The objective of this study was to identify the combination of risk features that distinguish frail from nonfrail individuals.

Setting

Machine learning analysis of highly dimensional risk features was performed on a clinical cohort of PLWH.

Methods

Participants included 105 older (average age = 55.6) PLWH, with at least a 3-month history of combination antiretroviral therapy (median CD4 = 546). Predictors included demographics, HIV clinical markers, comorbid health conditions, cognition, and neuroimaging (ie, volumetrics, resting-state functional connectivity, and cerebral blood flow). Gradient-boosted multivariate regressions were implemented to establish li

SUBMITTER: Paul RH 

PROVIDER: S-EPMC7903919 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature