Achieving the third 95 in sub-Saharan Africa: application of machine learning approaches to predict viral failure.

OBJECTIVE

Viral failure in people with HIV (PWH) may be influenced by multiple sociobehavioral, 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 PWH at 12 sites in Uganda, Kenya, Tanzania, and Nigeria. Participants underwent physical examination, medical history-taking, medical record extraction, sociobehavioral interviews, and laboratory testing. In cross-sectional analyses of enrollment data, viral failure was defined as a viral load at least 1000 copies/ml among participants on antiretroviral therapy (ART) for at least 6 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, 2941 PWH were enrolled, 1602 had been on antiretroviral therapy (ART) for at least 6 months, and 1571 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 PWH 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.