ArticleOpen forum infectious diseases2021
Development and Validation of a Multivariable Prediction Model for Missed HIV Health Care Provider Visits in a Large US Clinical Cohort.
Article in Open forum infectious diseases, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Federated Learning Performance Depends on Site Variation in Global HIV Data Consortia.medRxiv : the preprint server for health sciences · 2026Article
- Prediction of First and Multiple Antiretroviral Therapy Interruptions in People Living With HIV: Comparative Survival Analysis Using Cox and Explainable Machine Learning Models.JMIR medical informatics · 2026Article
- Preoperative risk stratification for pathological upgrading in colorectal polyps using explainable machine learning: implications for screening optimization and resource allocation.Frontiers in public health · 2026Article
- Health System-Level Implementation of Digital Health Support for People Living With HIV and Substance Use Disorders: Protocol for a Cluster-Randomized, Stepped-Wedge Clinical Trial.JMIR research protocols · 2025Article
- Development of a machine learning prediction model for loss to follow-up in HIV care using routine electronic medical records in a low-resource setting.BMC medical informatics and decision making · 2025Article
- Emerging from the shadows: Trends in HIV ambulatory care, viral load testing, and viral suppression in a U.S. HIV cohort, 2019-2022: Impact of COVID-19 pandemic.Journal of investigative medicine : the official publication of the American Federation for Clinical Research · 2024Article
- Sex Differences in the Risk of Stroke Associated With Traditional and Non-Traditional Factors in a US Cohort of People With HIV Infection.Neurology · 2024Observational
- Association of Neighborhood Deprivation With Healthcare Utilization Among Persons With Human Immunodeficiency Virus: A Latent Class Analysis.Open forum infectious diseases · 2023Article
- Accuracy of Provider Predictions of Viral Suppression Among Adolescents and Young Adults With HIV in an HIV Clinical Program.Journal of the International Association of Providers of AIDS CareArticle
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
backgroundIdentifying individuals at high risk of missing HIV care provider visits could support proactive intervention. Previous prediction models for missed visits have not incorporated data beyond the individual level.
methodsWe developed prediction models for missed visits among people with HIV (PWH) with ≥1 follow-up visit in the Center for AIDS Research Network of Integrated Clinical Systems from 2010 to 2016. Individual-level (medical record data and patient-reported outcomes), community-level (American Community Survey), HIV care site-level (standardized clinic leadership survey), and structural-level (HIV criminalization laws, Medicaid expansion, and state AIDS Drug Assistance Program budget) predictors were included. Models were developed using random forests with 10-fold cross-validation; candidate models with the highest area under the curve (AUC) were identified.
resultsData from 382 432 visits among 20 807 PWH followed for a median of 3.8 years were included; the median age was 44 years, 81% were male, 37% were Black, 15% reported injection drug use, and 57% reported male-to-male sexual contact. The highest AUC was 0.76, and the strongest predictors were at the individual level (prior visit adherence, age, CD4+ count) and community level (proportion living in poverty, unemployed, and of Black race). A simplified model, including readily accessible variables available in a web-based calculator, had a slightly lower AUC of .700.
conclusionsPrediction models validated using multilevel data had a similar AUC to previous models developed using only individual-level data. The strongest predictors were individual-level variables, particularly prior visit adherence, though community-level variables were also predictive. Absent additional data, PWH with previous missed visits should be prioritized by interventions to improve visit adherence.
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