ArticleAIDS and behavior2019
Who Will Show? Predicting Missed Visits Among Patients in Routine HIV Primary Care in the United States.
Article in AIDS and behavior, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
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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
34 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evaluating predictive performance, validity, and applicability of machine learning models for predicting HIV treatment interruption: a systematic review.BMC global and public health · 2025Pooled it
- Retention in care: is it still a clinically useful indicator for monitoring long-term engagement in HIV care in the treat-all era?AIDS (London, England) · 2026Article
- Development and Validation of a Novel Risk Calculator to Predict Sub-optimal HIV Outcomes Among Pregnant and Postpartum Women with HIV in Kenya.AIDS and behavior · 2025Article
- Retention in HIV Care Still Matters: A Longitudinal Multisite Analysis.Journal of acquired immune deficiency syndromes (1999) · 2025Article
- Intervention With WhatsApp Messaging to Compare the Effect of Self-Designed Messages and Standardized Messages in Adherence to Antiretroviral Treatment in Young People Living With HIV in a Hospital in Lima, Peru: Protocol for a Nonblinded Randomized Controlled 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
- Article
- The heterogeneity among people re-engaging in antiretroviral therapy highlights the need for a differentiated approach: results from a cross-sectional study in Johannesburg, South Africa.Journal of the International AIDS Society · 2024Article
- Rate and predictors of loss to follow-up in HIV care in a low-resource setting: analyzing critical risk periods.BMC infectious diseases · 2024Article
- Observational
- Demographic and Clinical Characteristics Predicting Missed Clinic Visits among Patients Living with HIV on Antiretroviral Treatment in Kinshasa and Haut-Katanga Provinces of the Democratic Republic of Congo.Healthcare (Basel, Switzerland) · 2024Article
- Exploring the Feasibility of an Electronic Tool for Predicting Retention in HIV Care: Provider Perspectives.International journal of environmental research and public health · 2024Article
- The Characteristics of Youth With Missed HIV Visits in Alabama.Open forum infectious diseases · 2024Article
- Geographic Mobility and HIV Care Engagement among People Living with HIV in Rural Kenya and Uganda.Tropical medicine and infectious disease · 2023Article
- The Brief Human Immunodeficiency Virus (HIV) Index: A Rapid 3-Item Scale to Measure Engagement in HIV Care.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2023Article
- Predictive Modeling of Lapses in Care for People Living with HIV in Chicago: Algorithm Development and Interpretation.JMIR public health and surveillance · 2023Article
- Article
- The Effect of Initial Patient Experiences and Life Stressors on Predicting Lost to Follow-Up in Patients New to an HIV Clinic.AIDS and behavior · 2022Article
- Baseline and Process Factors of Anti-Retroviral Therapy That Predict Loss to Follow-up Among People Living with HIV/AIDS in China: A Retrospective Cohort Study.AIDS and behavior · 2022Article
- Missing appointments by patients on antiretroviral therapy: Professional nurses' perspective.Curationis · 2022Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Missed HIV medical visits predict poor clinical outcomes. We sought to identify patients at high risk of missing visits. We analyzed 2002-2014 data from six large US HIV clinics. At each visit, we predicted the likelihood of missing the next scheduled visit using demographic, clinical, and patient-reported psychosocial variables. Overall, 10,374 participants contributed 105,628 HIV visits. For 17% of visits, the next scheduled appointment was missed. The strongest predictor of a future missed visit was past-year missed visits. A model with only this predictor had area under the receiver operator curve = 0.65; defining "high risk" as those with any past-year missed visits had 73% sensitivity and 51% specificity in correctly identifying a future missed visit. Inclusion of other clinical and psychosocial predictors only slightly improved performance. Past visit attendance can identify those at increased risk for future missed visits, allowing for proactive allocation of resources to those at greatest risk.
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