ArticleOphthalmology science2026
Identifying Patients at High Risk of Lapses in Diabetic Retinopathy Care: A Machine Learning Study.
Article in Ophthalmology science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Pilot Community Health Worker-Based Intervention to Address Social Determinants of Health Among Patients with Diabetes in Ophthalmology.Clinical ophthalmology (Auckland, N.Z.) · 2026Article
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Authors and funding
10 authors.
Funding
Abstract
Objective: To predict lapses in diabetic retinopathy (DR) care. Design: Retrospective cohort study. Subjects: Adults ≥18 years with diabetes seen at the Wilmer Eye Institute for DR screening or treatment between 2012 and 2022. Main Outcome Measures: Whether an office visit for DR screening or treatment was followed by a lapse in care. Methods: Three versions of prediction algorithms were constructed using random forests (RFs). XGBoost (XGB) was used as a confirmatory analysis. Random forest-A and XGB-A included electronic health record (EHR) variables alone (e.g., sociodemographic, insurance, ophthalmic diagnoses, lead time, and recommended follow-up time). Random forest-B and XGB-B added location-based social determinants of health (SDoH) variables (e.g., Area Deprivation Index). Random forest-C and XGB-C added history of lapses in care (e.g., whether the patient has ever had lapses in care before). The area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC) were calculated for each algorithm. Results: A total of 36 995 patients (mean age 62 years, 53% female, 47% non-Hispanic White, 38% non-Hispanic Black, and 4% Hispanic) and 141 930 office visits were included. The best performing model was RF-C with an AUROC of 0.774 (0.772-0.776) and AUPRC of 0.707 (0.704-0.711), outperforming RF-A and RF-B in AUROC and AUPRC ( Conclusions: We developed RF algorithms, as well as XGB confirmatory models, to predict whether patients with diabetes will experience a lapse in DR care. The best prediction was achieved using EHR variables, location-based SDoH variables, and history of lapses in care. These models offer the opportunity to identify high-risk patients and offer additional resources to reduce lapses in care and potentially vision loss from DR. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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