AUTHOR=Liu Hui , Ding Haibo , Zheng Yue , Li Yue , Yu Yang , Geng Zhaodi , Zhou Jie , Huo Huaibi , Li Han , Peng Xin , Tian Zhaoxin , Li Xiaolin , Shang Hong , Liu Ting 

TITLE=Prediction of obstructive coronary artery disease in people living with HIV: value of machine learning incorporating HIV-specific factors

JOURNAL=Frontiers in Medicine

VOLUME=Volume 12 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1661990

DOI=10.3389/fmed.2025.1661990

ISSN=2296-858X

ABSTRACT=ObjectivesTo explore the value of machine learning (ML) model in conjunction with HIV-specific risk factors to predict obstructive coronary artery disease (CAD) (≥50% stenosis) on coronary CT angiography (CTA) in the asymptomatic people living with HIV (PLWH).MethodsIn this cross-sectional study, we prospectively analyzed 304 PLWH without chest pain (age 48 ± 11 years, 91% males). The dataset was randomly divided into training and held-out test sets in an 8:2 ratio. The ML model established by random forest was compared with traditional models, including CAD consortium clinical score, CONFIRM score, and Genders clinical model, as well as logistic regression model. The coronary artery calcium score (CACS) was added to the above five models to establish new models. Predictive performance of the models was evaluated according to Delong test.ResultsObstructive CAD occurred in 64 of 304 PLWH (21%). The ML model (AUC of 0.946) had the highest discrimination for obstructive CAD compared with above models (AUC of 0.734, 0.736, 0.737, and 0.782, respectively; p < 0.05 for all comparisons). ML model showed the best calibration and clinical decision-making capability. Moreover, the ML model showed the best predictive performance compared with models after adding the CACS (AUC of 0.772, 0.740, 0.742, 0.750, and 0.798, respectively; p < 0.05 for all comparisons).ConclusionThe ML model incorporating cardiovascular risk factors and HIV-specific factors can more accurately estimate the pretest likelihood of obstructive CAD in PLWH than traditional models. ML improves risk stratification in HIV populations and may help guide management.