OCTA ‐Derived Retinal Biomarkers and Infarct Topography Improve Etiologic Classification of Recent Single Subcortical Infarction: A Nomogram Model

BACKGROUND: Recent single subcortical infarction (RSSI) in lenticulostriate artery territories exhibits etiological heterogeneity. Misclassification risks persist due to overlapping neuroimaging features between cerebral small-vessel disease-related lacunar infarction (CSVD-related LI) and branch atheromatous disease (BAD). We developed a nomogram that integrates retinal optical coherence tomography angiography (OCTA) metrics with infarct topography to improve etiological classification. METHODS: Patients with RSSI were prospectively enrolled between December 2021 and December 2023. LASSO regression identified predictors for a logistic regression-based nomogram. Performance was evaluated via concordance index (C-index), calibration curves, and decision-curve analysis. RESULTS: A total of 127 RSSI patients (86 CSVD-related LI, 41 BAD) were included. Three variables-superficial vascular complex density, number of lesion slices, and proximal lesion location-were retained in the final model. The nomogram achieved a C-index of 0.84 (95% CI, 0.80-0.89) versus 0.68 for conventional imaging, with superior net benefit across clinical thresholds (AUC 0.84 vs. 0.68, p < 0.001). CONCLUSION: The novel nomogram combining OCTA-derived retinal biomarkers with infarct topography improves differentiation of BAD from CSVD-related LI in RSSI patients and may facilitate etiology-driven clinical decision-making. External validation is needed for clinical implementation.