Maskew, MhairiParrott, ShantelleDe Voux, LucienSharpey-Schafer, KieranCrompton, ThomasGovender, Ashley ChristopherPisa, Pedro TerrenceRosen, Sydney2025-07-032025-07-032025-05Maskew, M., Parrott, S., De Voux, L. et al. 2025, 'Triaging clients at risk of disengagement from HIV care : application of a predictive model to clinical trial data in South Africa', Risk Management and Healthcare Policy, vol. 18, pp. 1601—1619, doi : 10.2147/RMHP.S510666.1179-1594 (online)10.2147/RMHP.S510666http://hdl.handle.net/2263/103156DATA SHARING STATEMENT : All data results produced in the present study are contained in the manuscript and Supplementary Material. Source data for the SLATE model are available online at Boston University’s data repository. Source data for the PREDICT models are owned by the South African Government and were used under license for the current study. Access to these data was provided by the South African National Department of Health through an agreement with Right to care and is subject to restrictions owing to privacy and ethics policies set by the South African Government, so they are not publicly available. Requests to access these should be directed to pedro.pisa@righttocare.org.PURPOSE : To reach South Africa's targets for HIV treatment and viral suppression, retention on antiretroviral therapy (ART) must increase. Here, we aim to successfully identify ART clients at risk of loss from care prior to disengagement. PATIENTS AND METHODS : We applied a previously developed machine learning and predictive modelling algorithm (PREDICT) to ART client data from SLATE I and II trials. The primary outcome was interruption in treatment (IIT), defined as missing the next scheduled clinic visit by >28 days. We tested two risk triaging approaches: 1) threshold approach classifying individuals into low, moderate, or high risk of IIT; and 2) archetype approach identifying subgroups with characteristics associated with risk of ITT. We report associations between risk category groups and subsequent IIT at the next scheduled visit using crude risk differences and relative risks with 95% confidence intervals. RESULTS : SLATE datasets included 7199 client visits for 1193 clients over ≤14 months of follow-up. The threshold approach consistently and accurately assigned levels of IIT risk for multiple stages of the care cascade. The archetype approach identified several subgroups at increased risk of IIT, including those late to previous appointments, returning after a period of disengagement, living alone or without a treatment supporter. Behavioural elements of the archetypes tended to drive the risk of treatment interruption more consistently than demographics; eg adolescent boys/young men who attended visits on time experienced the lowest rates of treatment interruption (10% PREDICT datasets; 7% SLATE datasets), while adolescent boys/young men returning after previously disengaging had the highest rates of subsequent treatment interruption (31% PREDICT datasets; 40% SLATE datasets). CONCLUSION : Routinely collected medical record data can be combined with basic demographic and socioeconomic data to assess individual risk of future treatment disengagement. This approach offers an opportunity to prevent disengagement from HIV care, rather than responding only after it has occurred. TRIAL REGISTRATION : SLATE I trial: Clinicaltrials.gov NCT02891135, registered September 1, 2016. First participant enrolled March 6, 2017, in South Africa and July 13, 2017, in Kenya. SLATE II trial: Clinicaltrials.gov NCT03315013, registered 19 October 2017. First participant enrolled 14 March 2018.en© 2025 Maskew et al. This work is published by Dove Medical Press Limited, and licensed under a Creative Commons Attribution License. The full terms of the License are available at https://creativecommons.org/licenses/by/4.0/.HIV service deliveryMachine learningPredictive modellingRetentionRisk triagingHuman immunodeficiency virus (HIV)Antiretroviral therapy (ART)Article