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

Autor:	Esra RT; Institute of Global Health, University of Geneva, Geneva, Switzerland.; Imperial College of London, London, United Kingdom., Carstens J; Palindrome Data, Cape Town, South Africa., Estill J; Institute of Global Health, University of Geneva, Geneva, Switzerland., Stoch R; Studio Fundi Ltd, London, United Kingdom., Le Roux S; The Aurum Institute, Johannesburg, South Africa., Mabuto T; The Aurum Institute, Johannesburg, South Africa., Eisenstein M; The Aurum Institute, Johannesburg, South Africa., Keiser O; Institute of Global Health, University of Geneva, Geneva, Switzerland., Maskew M; Health Economics and Epidemiology Research Office, Department of Internal Medicine, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa., Fox MP; Health Economics and Epidemiology Research Office, Department of Internal Medicine, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.; Departments of Epidemiology and Global Health, Boston University School of Public Health, Boston, Massachusetts, United States of America., De Voux L; Palindrome Data, Cape Town, South Africa., Sharpey-Schafer K; Palindrome Data, Cape Town, South Africa.
Jazyk:	angličtina
Zdroj:	PLOS global public health [PLOS Glob Public Health] 2023 Jul 19; Vol. 3 (7), pp. e0002105. Date of Electronic Publication: 2023 Jul 19 (Print Publication: 2023).
DOI:	10.1371/journal.pgph.0002105
Abstrakt:	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. Competing Interests: Jacques Carstens received funding in the form of salary from the commercial company Palindrome Data. Palindrome Data is partially funded by Janssen Pharmaceutica (Pty) Ltd, part of the Janssen Pharmaceutical Companies of Johnson & Johnson. There are no patents, products in development or marketed products associated with this research to declare. This does not alter our adherence to PLOS ONE policies on sharing data and materials. (Copyright: © 2023 Esra et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze:	MEDLINE
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