Achieving the 3rd 95 in sub-saharan Africa: application of machine learning approaches to predict viral failure.

OBJECTIVE: Viral failure in people living with HIV (PLWH) may be influenced by multiple socio-behavioral, clinical, and context-specific factors, and supervised learning approaches may identify novel predictors. We compared the performance of two supervised learning algorithms to predict viral failure in four African countries.

DESIGN: Cohort study.

METHODS: The African Cohort Study is an ongoing, longitudinal cohort enrolling PLWH at 12 sites in Uganda, Kenya, Tanzania, and Nigeria. Participants underwent physical examination, medical history-taking, medical record extraction, socio-behavioral interviews, and laboratory testing. In cross-sectional analyses of enrollment data, viral failure was defined as a viral load ≥1000 copies/mL among participants on antiretroviral therapy (ART) for at least six months. We compared the performance of lasso-type regularized regression and random forests by calculating area under the curve (AUC) and used each to identify factors associated with viral failure; 94 explanatory variables were considered.

RESULTS: Between January 2013 and December 2020, 2,941 PLWH were enrolled, 1,602 had been on ART for at least 6 months, and 1,571 participants with complete case data were included. At enrollment, 190 (12.0%) had viral failure. The lasso regression model was slightly superior to the random forest in its ability to identify PLWH with viral failure (AUC: 0.82 vs 0.75). Both models identified CD4 count, ART regimen, age, self-reported ART adherence and duration on ART as important factors associated with viral failure.

CONCLUSION: These findings corroborate existing literature primarily based on hypothesis-testing statistical approaches and help to generate questions for future investigations that may impact viral failure.