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

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dc.contributor.author Maskew, Mhairi
dc.contributor.author Sharpey‑Schafer, Kieran
dc.contributor.author DeVoux, Lucien
dc.contributor.author Crompton, Thomas
dc.contributor.author Bor, Jacob
dc.contributor.author Rennick, Marcus
dc.contributor.author Chirowodza, Admire
dc.contributor.author Miot, Jacqui
dc.contributor.author Molefi, Seithati
dc.contributor.author Onaga, Chuka
dc.contributor.author Majuba, Pappie
dc.contributor.author Sanne, Ian
dc.contributor.author Pisa, Pedro Terrence
dc.date.accessioned 2022-11-23T05:27:05Z
dc.date.available 2022-11-23T05:27:05Z
dc.date.issued 2022-07-26
dc.description.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, patientlevel 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 classifcation 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 classifcation 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, specifcity, 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 classifed 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 diferentiated models of care before patients disengage from treatment programmes, increasing costefectiveness and improving patient outcomes. en_US
dc.description.department Human Nutrition en_US
dc.description.librarian dm2022 en_US
dc.description.sponsorship The American People and the President’s Emergency Plan for AIDS Relief (PEPFAR) through the United States Agency for International Development (USAID), including bilateral support through USAID South Africa’s Accelerating Program Achievements to Control the Epidemic; the NIH National Institute of Allergies and Infectious Diseases Award and Eunice Kennedy Shriver National Institute of Child Health & Human Development. en_US
dc.description.uri https://www.nature.com/srep en_US
dc.identifier.citation Maskew, M., Sharpey-Schafer, K., De Voux, L. et al. Applying machine learning and predictive modeling to retention and viral suppression in South African HIV treatment cohorts. Scientific Reports 12, 12715 (2022). https://doi.org/10.1038/s41598-022-16062-0. en_US
dc.identifier.issn 2045-2322 (online)
dc.identifier.other 10.1038/s41598-022-16062-0
dc.identifier.uri https://repository.up.ac.za/handle/2263/88448
dc.language.iso en en_US
dc.publisher Nature Research en_US
dc.rights © The Author(s) 2021. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License. en_US
dc.subject Machine learning en_US
dc.subject Predictive modeling en_US
dc.subject South African en_US
dc.subject HIV treatment en_US
dc.subject Human immunodeficiency virus (HIV) en_US
dc.title Applying machine learning and predictive modeling to retention and viral suppression in South African HIV treatment cohorts en_US
dc.type Article en_US


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