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Войти в исследования AIDS (London, England) · 2026
Predictors of detectable viral load among adolescents with HIV in low resource settings who are receiving care in youth-friendly clinics.
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Kay Alexander, Seeger Abigail, Dlamini Phumzile, Steffy Teresa, Dube Mbongeni, Mothobi Buoang, Sekhantso Mphanya, Mohlabane Ntaoleng, Mthethwa Nobuhle, Masheane-Moseneke Anna, Chiao Elizabeth, Skinner Donald, Maldonado Yvonne, Debrah Vambe, Kirchner H Lester, Mandalakas Anna
Аннотация
OBJECTIVE: Adolescents with HIV (AWH) have higher rates of detectable viral loads (DVL) than adults. We sought to identify clinical and psychosocial predictors of DVL among AWH in optimized care environments.
METHODS: We prospectively recruited 1250 AWH (aged 10-19) from four youth-friendly HIV clinics in Eswatini and Lesotho (May 2023-October 2024). Participant clinical data was abstracted from an electronic medical record, and participants completed validated surveys assessing depression, anxiety, stigma, self-efficacy, adverse childhood experiences, and substance use. We developed predictive models from clinical and survey data using logistic regression, elastic net regression, and random forest algorithms, with 70/30 train-test splits.
RESULTS: Of 1222 participants with viral loads, 165 (13.5%) had DVL (≥50 copies/ml). The cohort was evenly distributed by gender and predominantly included older adolescents (72.3% aged 15-19) with perinatally-acquired HIV (median age at diagnosis 2 years). Most (95.1%) received dolutegravir-based therapy. Key features associated with DVL from the models included: prior DVL within 2 years [odds ratio (OR) 5.4, 95% confidence interval (CI) 3.34-8.74], nondolutegravir regimens (OR 4.11, 95% CI 1.36-12.42), and survey responses related to low self-efficacy, stigma fears, size of the household, and adherence behaviors. Model performance for prediction of DVL was moderate, with AUROCs for all models ranging between 0.729 and 0.748.
CONCLUSIONS: While clinical factors, particularly a prior DVL and non-DTG regimen, were associated with DVL, few psychosocial features consistently emerged across predictive models. The high viral suppression rates (86.5%) in these youth-friendly environments demonstrate effective comprehensive care. Results suggest individualized rather than algorithmic approaches may better support adolescents with persistent adherence challenges.
METHODS: We prospectively recruited 1250 AWH (aged 10-19) from four youth-friendly HIV clinics in Eswatini and Lesotho (May 2023-October 2024). Participant clinical data was abstracted from an electronic medical record, and participants completed validated surveys assessing depression, anxiety, stigma, self-efficacy, adverse childhood experiences, and substance use. We developed predictive models from clinical and survey data using logistic regression, elastic net regression, and random forest algorithms, with 70/30 train-test splits.
RESULTS: Of 1222 participants with viral loads, 165 (13.5%) had DVL (≥50 copies/ml). The cohort was evenly distributed by gender and predominantly included older adolescents (72.3% aged 15-19) with perinatally-acquired HIV (median age at diagnosis 2 years). Most (95.1%) received dolutegravir-based therapy. Key features associated with DVL from the models included: prior DVL within 2 years [odds ratio (OR) 5.4, 95% confidence interval (CI) 3.34-8.74], nondolutegravir regimens (OR 4.11, 95% CI 1.36-12.42), and survey responses related to low self-efficacy, stigma fears, size of the household, and adherence behaviors. Model performance for prediction of DVL was moderate, with AUROCs for all models ranging between 0.729 and 0.748.
CONCLUSIONS: While clinical factors, particularly a prior DVL and non-DTG regimen, were associated with DVL, few psychosocial features consistently emerged across predictive models. The high viral suppression rates (86.5%) in these youth-friendly environments demonstrate effective comprehensive care. Results suggest individualized rather than algorithmic approaches may better support adolescents with persistent adherence challenges.