ArticleAIDS and behavior2022
Multicenter Development and Validation of a Model for Predicting Retention in Care Among People with HIV.
Article in AIDS and behavior, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- Article
- Visit-level prediction of missed HIV appointments using machine learning and transformer-based models in Uganda.BMC medical informatics and decision making · 2026Article
- Identification of Clinical Phenotypes Among People with HIV Using Electronic Health Record Data.AIDS and behavior · 2026Article
- Identifying Patients at High Risk of Lapses in Diabetic Retinopathy Care: A Machine Learning Study.Ophthalmology science · 2026Article
- Comparison of machine learning models to predict loss to follow-up among people with Human Immunodeficiency Virus (HIV).JAMIA open · 2025Article
- Predictive Models to Identify Individuals With HIV at Risk of Unsuppressed Viral Load Using Routine Public Health Data.Journal of acquired immune deficiency syndromes (1999) · 2025Article
- Exploring the Feasibility of an Electronic Tool for Predicting Retention in HIV Care: Provider Perspectives.International journal of environmental research and public health · 2024Article
- Lightweight federated learning for STIs/HIV prediction.Scientific reports · 2024Article
- Approaching Personalized Medicine: The Use of Machine Learning to Determine Predictors of Mortality in a Population with SARS-CoV-2 Infection.Biomedicines · 2024Article
- Predictive Modeling of Lapses in Care for People Living with HIV in Chicago: Algorithm Development and Interpretation.JMIR public health and surveillance · 2023Article
- No-show Prediction Model Performance Among People With HIV: External Validation Study.Journal of medical Internet research · 2023Article
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
Predictive analytics can be used to identify people with HIV currently retained in care who are at risk for future disengagement from care, allowing for prioritization of retention interventions. We utilized machine learning methods to develop predictive models of retention in care, defined as no more than a 12 month gap between HIV care appointments in the Center for AIDS Research Network of Integrated Clinical Systems (CNICS) cohort. Data were split longitudinally into derivation and validation cohorts. We created logistic regression (LR), random forest (RF), and gradient boosted machine (XGB) models within a discrete-time survival analysis framework and compared their performance to a baseline model that included only demographics, viral suppression, and retention history. 21,267 Patients with 507,687 visits from 2007 to 2018 were included. The LR model outperformed the baseline model (AUC 0.68 [0.67-0.70] vs. 0.60 [0.59-0.62], P < 0.001). RF and XGB models had similar performance to the LR model. Top features in the LR model included retention history, age, and viral suppression.
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Registered trials
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