Work

Identifying Patients at High Risk of Lapses in Diabetic Retinopathy Care: A Machine Learning Study

Year: 2025

Type: article

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: < 0.001 for each comparison). 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.

Source: Ophthalmology Science

DOI: 10.1016/j.xops.2025.100967