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A prediction model for virologic failure in adolescents living with HIV in Uganda: Findings from the Suubi+Adherence
Samuel Kizito1, Fred M Ssewamala1, Torsten B Neilands2
1International Center for Child Health and Development, Brown School, Washington University in St. Louis, MO, 63130, USA.
Objectives:
Adolescents living with HIV (ALHIV) have low viral suppression levels, with 1 in 3 ALHIV experiencing virologic failure, calling for more efforts to reverse these trends. We developed and validated a model that predicts the risk of virologic failure (VF) among ALHIV.
Study Design:
Cross-sectional study.
Methods:
We used baseline data from 702 ALHIV enrolled in the Suubi + Adherence cluster-randomized clinical trial. Participants were aged 10-16 years, living with HIV and aware of their HIV status, and are living with a family. We developed a risk-prediction model for VF (viral load of ≥200 copies/mL) using sociodemographic, behavioral, psychological, economic, and treatment-related factors. LASSO logistic regression using 10-fold cross-validation with bootstrapping was used to select the predictors for the final model. Model performance was assessed by determining the discrimination using the area under the curve and calibration by drawing a calibration plot.
Results:
Using a lambda value of 0.007, the final model had 24 predictors (and interaction terms). The predictors included the participants' age, sex, work status, stigma, depressive symptoms, adherence self-efficacy, HIV knowledge, duration with HIV, time spent on ART, communication with the caregiver, family cohesion, social support, orphanhood status, number of people in the household, HIV disclosure, years spent at the current residence, and household asset ownership. The model predicted VF with AUC of 73.8 (95 % CI: 68.3-78.0) and calibration slope of 0.985.
Conclusions:
We developed and validated a model to predict the risk of virologic failure among ALHIV in Uganda, demonstrating its potential utility in identifying individuals at elevated risk for VF. Future models could be refined by incorporating clinical characteristics such as CD4 count to further improve predictive accuracy.
