The objective of this study was to identify biomarkers that predict a future need for anti-VEGF therapy in diabetic retinopathy (DR). Eyes with DR that underwent ultra-widefield angiography (UWFA) and had at least a 1 year follow-up were grouped based on future anti-VEGF treatment requirements: (1) not requiring treatment, (2) immediate treatment (within 3 months of UWFA), and (3) delayed treatment (after 3 months of UWFA). Quantitative UWFA features and clinical factors were evaluated. Random forest models were built to differentiate eyes requiring immediate and delayed treatment from the eyes not requiring treatment. A total of 173 eyes were included. The mean follow-up was 22 (range: 11-43) months. The macular leakage index, panretinal leakage index, presence of DME, and visual acuity were significantly different in eyes requiring immediate (n = 38) and delayed (n = 34) treatment compared to eyes not requiring treatment (n = 101). Random forest model differentiating eyes requiring immediate treatment from eyes not requiring treatment demonstrated an AUC of 0.91 ± 0.07. Quantitative angiographic features have potential as important predictive biomarkers of a future need for anti-VEGF therapy in DR and may serve to guide the frequency of a follow-up.
Keywords: anti-VEGF; artificial intelligence; diabetic retinopathy; fluorescein angiography; personalized treatment; predicting anti-VEGF; predictive modeling; quantitative image analysis.