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In SSA, where healthcare resources are often sparse, there is a dire need to identify adolescents living with HIV (ALHIV) more likely to experience virologic failure, such that they can be prioritized to limit HIV-related deaths and reduce HIV spread. Guided by the socioecological model, we develop and validate a model to predict the risk of virologic failure among ALHIV in Uganda.
Methods
We used baseline data from 530 ALHIV who were recruited from thirty-nine clinics in Southern Uganda and enrolled in a cluster-randomized controlled trail (Suubi+Adherence study). At enrollment, participants were aged 10 - 16, living with HIV, taking ART, living with a family, and receiving care at the study clinics. Virologic failure was defined as viral load >200 copies/ml. The least absolute shrinkage and selection operator (LASSO) penalized regression was used to select the subset of predictors (at the individual, interpersonal, community, and structural level) that contributed the most in predicting virological failure. The best penalty factor (λ) was selected using 10-fold cross-validation with bootstrapping. The model performance was assessed by determining the area under the curve (discrimination), and model calibration.
Findings
At baseline, the mean age was 12 years, and only 31.7% (n=168) experienced virologic failure. Out of the 37 candidate predictors, 24 were retained in the final model. Variables retained in the model included 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 virologic failure with an AUC of 73.8 (95% CI: 68.3 – 78.0) and an almost perfect calibration of 0.985.
Conclusions
It is feasible to predict ALHIV at high risk for virologic failure in low-income settings thus guiding clinicians on which patients need additional intervention, thus contributing to precision medicine in HIV management.