NCT06978998

Validation of the Prognostic Impact of a Retinal Photograph-based Cardiovascular Disease Risk Stratification System in de Novo HFrEF

Recruiting now Last updated 18 May 2025

What this trial tests

trial in Heart Failure in 100 participants. Currently enrolling.

Timeline

10 December 2024

Primary endpoint
10 December 2030

10 December 2030

Quick facts

Lead sponsor	Yonsei University
Status	Recruiting now
Study type	OBSERVATIONAL
Enrollment	100
Start date	10 December 2024
Primary completion	10 December 2030
Estimated completion	10 December 2030
Sites	1 location across South Korea

Conditions studied

• Heart Failure — all drugs for Heart Failure →
• Cardiomyopathies — all drugs for Cardiomyopathies →
• Retinal Photograph — all drugs for Retinal Photograph →
• Deep Learning — all drugs for Deep Learning →

Sponsor

Yonsei University

Who can join

20 and older, any sex, with Heart Failure or Cardiomyopathies. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

"Despite significant advances in pharmacologic and device-based therapies, heart failure (HF) remains a major public health burden, with persistently high rates of hospitalization, impaired quality of life, and excess mortality-often exceeding those of leading malignancies. Prognosis in HF is shaped by its underlying etiology: ischemic HF often responds to revascularization strategies, whereas non-ischemic HF, particularly due to idiopathic or genetic cardiomyopathies, demonstrates highly variable outcomes and limited responsiveness to guideline-directed medical therapy (GDMT). Although left ventricular reverse remodeling (LVRR) is associated with favorable outcomes, only 40-50% of non-ischemic HF patients achieve meaningful LVRR with GDMT alone.

In this context of therapeutic uncertainty and prognostic heterogeneity, there is a critical need for novel, non-invasive risk stratification tools. Retinal imaging offers a unique advantage, enabling direct, in vivo visualization of systemic microvascular and neurovascular integrity. Prior work from our group has demonstrated that deep learning algorithms applied to retinal fundus photographs can estimate physiologic and metabolic markers-including CAC scores-and predict future cardiovascular events. The Reti-CVD scoring system, derived from these models, has been externally validated in independent populations.

In the present study, we aim to evaluate the prognostic utility of the Reti-CVD model in a cohort of patients with newly diagnosed HF and reduced ejection fraction. Specifically, we will assess whether retinal-derived risk scores at baseline are associated with adverse clinical outcomes, including cardiovascular events and all-cause mortality, and whether prognostic performance varies according to HF etiology."

Publications & conference data

No peer-reviewed publications indexed yet for this trial.