Machine learning risk stratification to identify people living with HIV at high risk of delayed ART and advanced immunosuppression: a precision public health approach.

Background: Late presentation and delayed antiretroviral therapy (ART) initiation among people living with HIV (PLWH) are associated with increased morbidity, mortality, and transmission risk. Identifying individuals at high risk of delayed care or severe immunosuppression (CD4 < 200 cells/μL) could enable targeted interventions. Objective: To develop and validate an artificial intelligence (AI)-based risk stratification tool using routinely collected data to identify PLWH at high risk of delayed ART initiation or advanced immunosuppression. Methods: We conducted a retrospective cohort study using de-identified data from HIV-positive individuals (2016 onward). A composite high-risk outcome was defined as delayed ART initiation or baseline CD4 < 200 cells/μL. Multiple classifiers-including logistic regression, XGBoost, LightGBM, CatBoost, multilayer perceptron, and HistGradientBoosting-were trained with five-fold stratified cross-validation. A stacking ensemble incorporating six base models and a LightGBM meta-learner was evaluated. Discrimination (AUC-ROC, AUC-PR), calibration, and clinical utility (decision curve analysis) were assessed. Results: Among 5,436 eligible participants, 61.2% were high-risk. The stacking ensemble achieved an AUC-ROC of 0.915, AUC-PR of 0.950, accuracy of 0.815, and F1 score of 0.858. At ≥90% sensitivity, precision was 0.810. Calibration was good across deciles; decision curve analysis showed net benefit superior to treat-all or treat-none strategies. Baseline CD4 count, age, and transmission route were the strongest predictors. Conclusion: An AI-based risk stratification tool using routinely collected data can identify PLWH at high risk of delayed ART or advanced immunosuppression with strong performance and clinical utility. Prospective validation and equity assessment are needed before implementation.