Fundus Photograph-Derived Computational Features Predict Risk of Cardiovascular Events in the Chronic Renal Insufficiency Cohort Clinical Observational Study.

Abstract

Background: Patients with CKD face an elevated but variable risk of cardiovascular (CV) disease. Retinal imaging in CKD provides a non-invasive opportunity for CV risk stratification through microvascular analysis. The objective of this study was to evaluate retinal vascular features extracted via computer vision and machine learning approaches for CV risk and their added value over established risk calculators in CKD patients.

Methods: Retinal scans from 1333 participants of the multi-center clinical observational study, Chronic Renal Insufficiency Cohort (NCT00304148), were analyzed. A deep-learning pipeline segmented vessels and then identified arterioles and venules from them. Segmented vessel, arteriole and venule masks were used to extract 384 vascular features. An elastic-net model-Cardiovascular Assessment through Retinal Evaluation in CKD (CARE-CKD)(MCARE)- was trained, using the top eight features, on 567 participants (101 major adverse cardiovascular events [MACE]: composite of myocardial infarction, stroke, heart failure) and validated on 244 participants (44 MACE). A Nomogram integrating MCARE with clinical markers (age, sex, blood pressure, smoking, eGFR, albuminuria, cholesterol, BMI and diabetes status) was developed.

Results: MCARE demonstrated strong prognostic performance for predicting MACE, (C-index=0.70, HR=3.95, above vs. below median; 95%CI: 2.36-6.63; p<0.001), outperforming the Framingham Risk Score (FRS) (C-index=0.66; HR=1.06) (Likelihood Ratio Test (LRT) p<0.01) and Predicting Risk of cardiovascular disease EVENTs (PREVENT) (C-index=0.65; HR=1.84, LRT p<0.001) calculators. MCARE improved risk stratification within FRS-based high-risk (HR=3.73, p<0.001) and PREVENT-based high-risk (HR=4.73, p<0.001) categories. Nomogram enhanced risk stratification (C-index=0.77, HR=3.81, p<0.0001) compared to clinical markers (LRT p<0.01).

Conclusions: CARE-CKD provides a novel, opportunistic approach to CV risk assessment in CKD, outperforming the established risk calculators and refining stratification within high-risk categories. By enabling earlier identification, close monitoring, and targeted management of high-risk patients, CARE-CKD addresses gaps left by traditional calculators, maximizing the benefits of emerging therapies and potentially improving long-term outcomes.