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Historical visit attendance as predictor of treatment interruption in South African HIV patients: Extension of a validated machine learning model

Retention of antiretroviral (ART) patients is a priority for achieving HIV epidemic control in South Africa. While machine-learning methods are being increasingly utilised to identify high risk populations for suboptimal HIV service utilisation, they are limited in terms of explaining relationships...

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Autores principales: Esra, Rachel T., Carstens, Jacques, Estill, Janne, Stoch, Ricky, Le Roux, Sue, Mabuto, Tonderai, Eisenstein, Michael, Keiser, Olivia, Maskew, Mhari, Fox, Matthew P., De Voux, Lucien, Sharpey-Schafer, Kieran
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10355459/
https://www.ncbi.nlm.nih.gov/pubmed/37467217
http://dx.doi.org/10.1371/journal.pgph.0002105
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author Esra, Rachel T.
Carstens, Jacques
Estill, Janne
Stoch, Ricky
Le Roux, Sue
Mabuto, Tonderai
Eisenstein, Michael
Keiser, Olivia
Maskew, Mhari
Fox, Matthew P.
De Voux, Lucien
Sharpey-Schafer, Kieran
author_facet Esra, Rachel T.
Carstens, Jacques
Estill, Janne
Stoch, Ricky
Le Roux, Sue
Mabuto, Tonderai
Eisenstein, Michael
Keiser, Olivia
Maskew, Mhari
Fox, Matthew P.
De Voux, Lucien
Sharpey-Schafer, Kieran
author_sort Esra, Rachel T.
collection PubMed
description Retention of antiretroviral (ART) patients is a priority for achieving HIV epidemic control in South Africa. While machine-learning methods are being increasingly utilised to identify high risk populations for suboptimal HIV service utilisation, they are limited in terms of explaining relationships between predictors. To further understand these relationships, we implemented machine learning methods optimised for predictive power and traditional statistical methods. We used routinely collected electronic medical record (EMR) data to evaluate longitudinal predictors of lost-to-follow up (LTFU) and temporal interruptions in treatment (IIT) in the first two years of treatment for ART patients in the Gauteng and North West provinces of South Africa. Of the 191,162 ART patients and 1,833,248 visits analysed, 49% experienced at least one IIT and 85% of those returned for a subsequent clinical visit. Patients iteratively transition in and out of treatment indicating that ART retention in South Africa is likely underestimated. Historical visit attendance is shown to be predictive of IIT using machine learning, log binomial regression and survival analyses. Using a previously developed categorical boosting (CatBoost) algorithm, we demonstrate that historical visit attendance alone is able to predict almost half of next missed visits. With the addition of baseline demographic and clinical features, this model is able to predict up to 60% of next missed ART visits with a sensitivity of 61.9% (95% CI: 61.5–62.3%), specificity of 66.5% (95% CI: 66.4–66.7%), and positive predictive value of 19.7% (95% CI: 19.5–19.9%). While the full usage of this model is relevant for settings where infrastructure exists to extract EMR data and run computations in real-time, historical visits attendance alone can be used to identify those at risk of disengaging from HIV care in the absence of other behavioural or observable risk factors.
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spelling pubmed-103554592023-07-20 Historical visit attendance as predictor of treatment interruption in South African HIV patients: Extension of a validated machine learning model Esra, Rachel T. Carstens, Jacques Estill, Janne Stoch, Ricky Le Roux, Sue Mabuto, Tonderai Eisenstein, Michael Keiser, Olivia Maskew, Mhari Fox, Matthew P. De Voux, Lucien Sharpey-Schafer, Kieran PLOS Glob Public Health Research Article Retention of antiretroviral (ART) patients is a priority for achieving HIV epidemic control in South Africa. While machine-learning methods are being increasingly utilised to identify high risk populations for suboptimal HIV service utilisation, they are limited in terms of explaining relationships between predictors. To further understand these relationships, we implemented machine learning methods optimised for predictive power and traditional statistical methods. We used routinely collected electronic medical record (EMR) data to evaluate longitudinal predictors of lost-to-follow up (LTFU) and temporal interruptions in treatment (IIT) in the first two years of treatment for ART patients in the Gauteng and North West provinces of South Africa. Of the 191,162 ART patients and 1,833,248 visits analysed, 49% experienced at least one IIT and 85% of those returned for a subsequent clinical visit. Patients iteratively transition in and out of treatment indicating that ART retention in South Africa is likely underestimated. Historical visit attendance is shown to be predictive of IIT using machine learning, log binomial regression and survival analyses. Using a previously developed categorical boosting (CatBoost) algorithm, we demonstrate that historical visit attendance alone is able to predict almost half of next missed visits. With the addition of baseline demographic and clinical features, this model is able to predict up to 60% of next missed ART visits with a sensitivity of 61.9% (95% CI: 61.5–62.3%), specificity of 66.5% (95% CI: 66.4–66.7%), and positive predictive value of 19.7% (95% CI: 19.5–19.9%). While the full usage of this model is relevant for settings where infrastructure exists to extract EMR data and run computations in real-time, historical visits attendance alone can be used to identify those at risk of disengaging from HIV care in the absence of other behavioural or observable risk factors. Public Library of Science 2023-07-19 /pmc/articles/PMC10355459/ /pubmed/37467217 http://dx.doi.org/10.1371/journal.pgph.0002105 Text en © 2023 Esra et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Esra, Rachel T.
Carstens, Jacques
Estill, Janne
Stoch, Ricky
Le Roux, Sue
Mabuto, Tonderai
Eisenstein, Michael
Keiser, Olivia
Maskew, Mhari
Fox, Matthew P.
De Voux, Lucien
Sharpey-Schafer, Kieran
Historical visit attendance as predictor of treatment interruption in South African HIV patients: Extension of a validated machine learning model
title Historical visit attendance as predictor of treatment interruption in South African HIV patients: Extension of a validated machine learning model
title_full Historical visit attendance as predictor of treatment interruption in South African HIV patients: Extension of a validated machine learning model
title_fullStr Historical visit attendance as predictor of treatment interruption in South African HIV patients: Extension of a validated machine learning model
title_full_unstemmed Historical visit attendance as predictor of treatment interruption in South African HIV patients: Extension of a validated machine learning model
title_short Historical visit attendance as predictor of treatment interruption in South African HIV patients: Extension of a validated machine learning model
title_sort historical visit attendance as predictor of treatment interruption in south african hiv patients: extension of a validated machine learning model
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10355459/
https://www.ncbi.nlm.nih.gov/pubmed/37467217
http://dx.doi.org/10.1371/journal.pgph.0002105
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