Association between Open-Angle Glaucoma Progression and Cardiovascular Risk Predicted by a Deep Learning-Based Fundus...

This study investigated the association between the RetiCAC score, a deep learning-derived estimate of systemic atherosclerotic burden from retinal photographs and glaucomatous visual field (VF) progression.

A retrospective cohort study.

A total of 192 eyes from 192 patients diagnosed with open-angle glaucoma at Hanyang University Seoul and Guri Hospitals.

RetiCAC scores were estimated from fundus photographs using a deep learning model, and patients were stratified into low-to-moderate vs. high-risk groups using a cutoff of 41 based on the RetiCAC-derived 5-year cardiovascular disease risk classification. Generalized estimating equations (GEEs) were applied to identify factors associated with VF progression. Progression-free survival was compared between risk groups using Kaplan-Meier analysis. Relationships between RetiCAC and VF change rates were examined using scatter plots with locally weighted scatterplot smoothing curves.

The primary outcome was the association between the RetiCAC score and glaucomatous VF progression.

Higher RetiCAC scores (P = 0.023) and longer follow-up duration (P = 0.018) were independently associated with VF progression in the final multivariable GEE model. Kaplan-Meier analysis demonstrated a significantly higher probability of VF progression in the high-risk group compared to that of the low-to-moderate risk group (P = 0.030). Locally weighted scatterplot smoothing analyses identified an inflection point consistent with accelerated VF deterioration at a RetiCAC score of 51.1.

The RetiCAC score, derived from retinal photographs, showed a significant association with glaucoma progression, independent of traditional risk factors. This noninvasive marker of systemic vascular health may be useful for predicting glaucoma progression and may reflect vascular dysfunction contributing to disease progression.

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