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Applying machine learning and predictive modeling to retention and viral suppression in South African HIV treatment cohorts
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...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9325703/ https://www.ncbi.nlm.nih.gov/pubmed/35882962 http://dx.doi.org/10.1038/s41598-022-16062-0 |
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author | Maskew, Mhairi Sharpey-Schafer, Kieran De Voux, Lucien Crompton, Thomas Bor, Jacob Rennick, Marcus Chirowodza, Admire Miot, Jacqui Molefi, Seithati Onaga, Chuka Majuba, Pappie Sanne, Ian Pisa, Pedro |
author_facet | Maskew, Mhairi Sharpey-Schafer, Kieran De Voux, Lucien Crompton, Thomas Bor, Jacob Rennick, Marcus Chirowodza, Admire Miot, Jacqui Molefi, Seithati Onaga, Chuka Majuba, Pappie Sanne, Ian Pisa, Pedro |
author_sort | Maskew, Mhairi |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9325703 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-93257032022-07-28 Applying machine learning and predictive modeling to retention and viral suppression in South African HIV treatment cohorts Maskew, Mhairi Sharpey-Schafer, Kieran De Voux, Lucien Crompton, Thomas Bor, Jacob Rennick, Marcus Chirowodza, Admire Miot, Jacqui Molefi, Seithati Onaga, Chuka Majuba, Pappie Sanne, Ian Pisa, Pedro Sci Rep Article 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. Nature Publishing Group UK 2022-07-26 /pmc/articles/PMC9325703/ /pubmed/35882962 http://dx.doi.org/10.1038/s41598-022-16062-0 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Maskew, Mhairi Sharpey-Schafer, Kieran De Voux, Lucien Crompton, Thomas Bor, Jacob Rennick, Marcus Chirowodza, Admire Miot, Jacqui Molefi, Seithati Onaga, Chuka Majuba, Pappie Sanne, Ian Pisa, Pedro Applying machine learning and predictive modeling to retention and viral suppression in South African HIV treatment cohorts |
title | Applying machine learning and predictive modeling to retention and viral suppression in South African HIV treatment cohorts |
title_full | Applying machine learning and predictive modeling to retention and viral suppression in South African HIV treatment cohorts |
title_fullStr | Applying machine learning and predictive modeling to retention and viral suppression in South African HIV treatment cohorts |
title_full_unstemmed | Applying machine learning and predictive modeling to retention and viral suppression in South African HIV treatment cohorts |
title_short | Applying machine learning and predictive modeling to retention and viral suppression in South African HIV treatment cohorts |
title_sort | applying machine learning and predictive modeling to retention and viral suppression in south african hiv treatment cohorts |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9325703/ https://www.ncbi.nlm.nih.gov/pubmed/35882962 http://dx.doi.org/10.1038/s41598-022-16062-0 |
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