Kizito Samuel, Ssewamala Fred M, Neilands Torsten B, Nabunya Proscovia, Namatovu Phionah, Nabayinda Josephine, McKay Mary M, Johnson Kimberly J, Brownson Ross
International Center for Child Health and Development, Brown School, Washington University in St. Louis, MO, 63130, USA.
International Center for Child Health and Development, Brown School, Washington University in St. Louis, MO, 63130, USA.
Public Health. 2025 Jul;244:105753. doi: 10.1016/j.puhe.2025.105753. Epub 2025 May 13.
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.
Cross-sectional study.
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.
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.
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.
感染艾滋病毒的青少年（ALHIV）的病毒抑制水平较低，三分之一的ALHIV经历病毒学失败，因此需要做出更多努力来扭转这些趋势。我们开发并验证了一个预测ALHIV中病毒学失败（VF）风险的模型。
横断面研究。
我们使用了参与Suubi + 依从性整群随机临床试验的702名ALHIV的基线数据。参与者年龄在10 - 16岁之间，感染艾滋病毒且知晓自己的艾滋病毒感染状况，并且与家人同住。我们使用社会人口学、行为、心理、经济和治疗相关因素开发了一个VF（病毒载量≥200拷贝/毫升）风险预测模型。使用带有自抽样法的10倍交叉验证的套索逻辑回归来选择最终模型的预测因子。通过使用曲线下面积确定辨别力和绘制校准图来评估模型性能。
使用0.007的lambda值，最终模型有24个预测因子（和交互项）。预测因子包括参与者的年龄、性别、工作状态、耻辱感、抑郁症状、依从性自我效能、艾滋病毒知识、感染艾滋病毒的持续时间、接受抗逆转录病毒治疗的时间、与照顾者的沟通、家庭凝聚力、社会支持、孤儿身份、家庭人口数量、艾滋病毒披露情况、在当前住所居住的年数以及家庭资产所有权。该模型预测VF的AUC为73.8（95%CI：68.3 - 78.0），校准斜率为0.985。
我们开发并验证了一个模型来预测乌干达ALHIV中病毒学失败的风险，证明了其在识别VF高风险个体方面的潜在效用。未来的模型可以通过纳入如CD4计数等临床特征来进一步改进，以提高预测准确性。
