Applying machine learning and predictive modeling to retention and viral suppression in South African HIV treatment cohorts

Abstract

HIV treatment programs face challenges in identifying patients at risk for loss-to-follow-up and uncontrolled viremia. We applied predictive machine learning algorithms to anonymised, patient-level HIV programmatic data from two districts in South Africa, 2016–2018. We developed patient risk scores for two outcomes: (1) visit attendance ≤ 28 days of the next scheduled clinic visit and (2) suppression of the next HIV viral load (VL). Demographic, clinical, behavioral and laboratory data were investigated in multiple models as predictor variables of attending the next scheduled visit and VL results at the next test. Three classification algorithms (logistical regression, random forest and AdaBoost) were evaluated for building predictive models. Data were randomly sampled on a 70/30 split into a training and test set. The training set included a balanced set of positive and negative examples from which the classification algorithm could learn. The predictor variable data from the unseen test set were given to the model, and each predicted outcome was scored against known outcomes. Finally, we estimated performance metrics for each model in terms of sensitivity, specificity, positive and negative predictive value and area under the curve (AUC). In total, 445,636 patients were included in the retention model and 363,977 in the VL model. The predictive metric (AUC) ranged from 0.69 for attendance at the next scheduled visit to 0.76 for VL suppression, suggesting that the model correctly classified whether a scheduled visit would be attended in 2 of 3 patients and whether the VL result at the next test would be suppressed in approximately 3 of 4 patients. Variables that were important predictors of both outcomes included prior late visits, number of prior VL tests, time since their last visit, number of visits on their current regimen, age, and treatment duration. For retention, the number of visits at the current facility and the details of the next appointment date were also predictors, while for VL suppression, other predictors included the range of the previous VL value. Machine learning can identify HIV patients at risk for disengagement and unsuppressed VL. Predictive modeling can improve the targeting of interventions through differentiated models of care before patients disengage from treatment programmes, increasing cost-effectiveness and improving patient outcomes.

Artificial Intelligence and Machine Learning Based Prediction of Viral Load and CD4 Status of People Living with HIV (PLWH) on Anti-Retroviral Treatment in Gedeo Zone Public Hospitals

“… 33 , 36 , 44 This is somewhat higher than Maskew et al, which reports that ML based predictive modeling achieves an AUC of 0.76. 39 Recently, Kamal et al conducted a study employing RF-based prediction of virologic outcomes in Switzerland, which achieved an AUC of 0.77. 38 Meanwhile, our accuracy finding is in agreement with a study of Bisaso et al, which reports a predictive modeling accuracy of 92.9%.…”

“… 38 , 39 , 52 Furthermore, duration on ART was an important feature for predicting virological failure. Participants who had a longer duration of stay on ART had a higher risk of developing virological failure; this is consistent with the previous studies, 38 , 39 which reported longer ratio of follow-up increases the chances of developing virological failure. Our finding indicated participants who had low CD4 counts had higher chance of virological failure.…”

“… 38 , 46 , 49 Similarly, opportunistic infection was found to have the most crucial impact in predicting viral load failure among individuals living with HIV/AIDS in the literature. 36 , 39 , 44 …”

“…38,46,49 Similarly, opportunistic infection was found to have the most crucial impact in predicting viral load failure among individuals living with HIV/AIDS in the literature. 36,39,44 This study determined the significant influencing factors for low or poor CD4 status using the relevance values of features for the GB classifier. Accordingly, WHO clinical stage, weight, duration of ART follow-up, age, and regimen change were predictive of CD4 status in this study.…”

Machine learning to predict virological failure among HIV patients on antiretroviral therapy in the University of Gondar Comprehensive and Specialized Hospital, in Amhara Region, Ethiopia, 2022

“…Machine learning predictive algorithms have the potential to improve the quality of care and predict the needs of HIV patients by analyzing huge amounts of data, and enhancing prediction capabilities [ 42 , 43 ]. Furthermore, machine learning is used to identify HIV patients who are at high risk of failing to adhere and being a virological failure [ 44 ] and able to learn from domain experts quickly and accurately, then apply that knowledge to identify HIV risk behaviors in a huge dataset [ 45 ]. Ethiopia has a severe HIV pandemic with increasing virological failure [ 46 ].…”