Development and Validation of a Multivariable Prediction Model for Missed HIV Health Care Provider Visits in a Large US Clinical Cohort. (8th April 2021)

• Record Type: Journal Article
• Authors: Pettit, April C; Bian, Aihua; Schember, Cassandra O; Rebeiro, Peter F; Keruly, Jeanne C; Mayer, Kenneth H; Mathews, W Christopher; Moore, Richard D; Crane, Heidi M; Geng, Elvin; Napravnik, Sonia; Shepherd, Bryan E; Mugavero, Michael J
• Is Part Of: Open forum infectious diseases. Volume 8:Number 7(2021)
• Publication Date: 2021-04-08
• Subjects: HIV -- missed visits -- prediction model -- random forests -- retention in care; Communicable diseases -- Periodicals; Medical microbiology -- Periodicals; Infection -- Periodicals; 616.9
• DOI: 10.1093/ofid/ofab130
• Languages: English
• ISSNs: 2328-8957

Abstract

Abstract: Background: Identifying 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. Methods: We 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. Results: Data 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. Conclusions: Prediction 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. Abstract : We developed a simple, point-of-care model, available via a web-based calculator with strong discriminatory power for predicting missed HIV care visits. Strongest predictors were individual-level variables, particularly prior visit adherence, though community-level variables were also predictive.