Development and External Validation of a Treatment-Adjusted Machine Learning Model for Precision Allocation of Group-Based Depression Care Among People Living with HIV in Uganda

<ns3:p>Background Group-based depression care is widely used in HIV services in Uganda, yet some patients remain symptomatic. Uniform intervention allocation may not match individual needs and can strain scarce mental health resources. We developed and externally validated a treatment-adjusted machine learning model to predict six-month depression non-remission and support precision allocation of care. Methods We analyzed data from 1,140 adults living with HIV and significant depression symptoms enrolled across 30 HIV clinics in the SEEK-GSP trial (PACTR201608001738234). Participants were assigned to Group Support Psychotherapy (GSP) or Group HIV Education (GHE). The primary outcome was six-month depression non-remission, defined as Self-Reporting Questionnaire (SRQ) score ≥ 6 and a functional impairment score &lt; 9. Treatment assignment was included to enable risk estimation adjusted for treatment. Three machine learning models (Elastic Net, Random Forest, and XGBoost) were trained on baseline data from Gulu and Kitgum and externally validated in Pader district, with the best-performing parsimonious model selected to inform precision allocation. Calibration was evaluated on the external set using slope, intercept, Brier score, and the area under the receiver operating characteristic curve [AUC], with isotonic regression and Platt scaling applied for recalibration. Results All models demonstrated excellent discrimination in geographically distinct external validation (AUC 0.974–0.980). XGBoost achieved strong overall classification performance in Pader district (AUC 0.979; accuracy 0.920; sensitivity 0.865; specificity 0.970), supporting reliable identification of individuals at risk of non-remission. Post-calibration isotonic regression improved probability alignment (slope 1.703 to 1.00; Brier 0.070 to 0.015; AUC 0.986). Across modeling approaches, consistent predictors of non-remission included treatment assignment, older age, economic vulnerability (lower income, savings, and employment instability), stigma, low perceived social support, and maladaptive coping patterns. Conclusion Treatment-adjusted machine learning models can accurately predict six-month depression non-remission and provide a foundation for precision allocation of group-based depression care within HIV services in low-resource settings.</ns3:p>