Lab tests can be used to predict phosphatidylethanol-measured high-risk alcohol use among people with HIV: a proof-of-concept using machine learning.

ABSTRACT:

Background

Unhealthy alcohol use is prevalent among persons with HIV (PWH) and is associated with adverse outcomes, but is underestimated partly due to use of self-reported measures prone to underreporting. Phosphatidylethanol (PEth) is a direct measure of past month alcohol consumption but is costly. We assessed whether lab and health data representing alcohol-associated physiologic changes could be leveraged with machine learning to predict PEth-measured high-risk alcohol use among PWH.

Methods

We pooled baseline data from two studies among PWH in Uganda that measured PEth (N = 988), and classified PEth as no/low/moderate (PEth <200 ng/ml) or high-risk (PEth ≥200 ng/ml). We split the data into training (n = 790) and testing (n = 198) sets, imputing missing data separately

SUBMITTER: Espinosa da Silva C 

PROVIDER: S-EPMC12704430 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature