Related Experiment Video
Updated: Aug 5, 2026
An Affordable HIV-1 Drug Resistance Monitoring Method for Resource Limited Settings
Published on: March 30, 2014
Predicting HIV viral non-suppression in Uganda: development and validation of machine learning and risk
Maria Magdalene Namaganda1,2,3,4, Stathis Gennatas2, Laura Merson3
1Department of Immunology and Molecular Biology, School of Biomedical Sciences, College of Health Sciences, Makerere University, Kampala, Uganda.
Background:
Viral non-suppression is the primary actionable risk state in routine HIV care, yet most individuals are identified after virological failure and/or drug resistance, rather than proactively. In Uganda and similar resource-limited settings, routine electronic medical records (EMR) are collected at scale but remain underused for targeted, data-enabled risk stratification. We aimed to develop and internally validate machine learning and regularized regression models for predicting viral non-suppression using routine monitoring data.
Methods:
We developed and internally validated prediction models for viral non-suppression (viral load ≥1,000 copies/mL) using routinely recorded EMR variables from the TASO Uganda open cohort (2014-2024; n = 33,384). Twenty variables were used across four models: logistic regression (LR), elastic net regularized logistic regression (ENET), random forest (RF), and extreme gradient boosting (XGB), evaluated on a stratified 80:20 test set. Precision-recall AUC (PR-AUC) was the primary metric; ROC-AUC, Brier score, and decision curve analysis were assessed; bootstrap 95% CIs (2,000 replicates) were computed for discrimination metrics.
Results:
On the test set (n = 6,677), RF achieved PR-AUC 0.248 (95% CI 0.207-0.291) and ROC-AUC 0.758 (0.732-0.780); ENET achieved PR-AUC 0.237 (0.198-0.279) and ROC-AUC 0.750 (0.726-0.772); confidence intervals overlapped across all four models. RF and ENET achieved Brier scores of 0.055 (8% below the null of 0.060) and maximum net benefit of 0.055 in decision curve analysis. At the capacity-first threshold (top 5% predicted risk), RF flagged 325 individuals (4.9%; PPV 0.338, NPV 0.949) and ENET flagged 334 (5.0%; PPV 0.305, NPV 0.948). Current ART class (PI-based: OR 11.07; NNRTI-based: OR 4.80), poor adherence (OR 7.71), TB history (OR 2.08), and male sex (OR 1.62) were the strongest predictors.
Conclusions:
Routine EMR data support meaningful, calibrated viral non-suppression risk prediction across a large, multi-site Ugandan HIV program. At a capacity-first threshold, both models achieved approximately five-fold enrichment over background prevalence, with clinical utility confirmed by decision curve analysis. Prospective external validation and workflow integration are required before deployment.
