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.