ECG Low Ejection Fraction Detection and Guiding in AI Navigated Treatment Era

Summary

Asymptomatic left ventricular systolic dysfunction (ALVSD), identified as a key component of stage B heart failure (HF) by AHA/ACC guidelines, is a common precursor to clinically overt HF. This progressive chronic disease affects over 23 million people worldwide and leads to significant morbidity, mortality, and healthcare costs. Although ALVSD presents a relatively lower risk compared to symptomatic reduced ejection fraction HF, it remains associated with a 1.6-fold increase in the risk of incident HF, a 2.13-fold increase in cardiovascular mortality, and a 1.46-fold increase in all-cause mortality. The prevalence of ALVSD ranges from 3% to 6%, at least twice that of symptomatic HF. To prevent progression to symptomatic heart failure and associated morbidities and mortalities, guideline-directed medical therapy, including ACEIs/ARBs or beta-blockers, is essential for patients with ALVSD. However, distinguishing individuals with ALVSD from the general population is challenging due to the lack of symptoms. Effective screening methods are crucial to identify individuals with ALVSD. Traditionally, diagnosing ALVSD involves screening asymptomatic populations using transthoracic echocardiography (TTE), which is costly, time-consuming, and inconvenient for patients. Other screening methods, such as laboratory tests for brain natriuretic peptide (BNP) or N- terminal pro-atrial natriuretic peptide (NT-proBNP), have insufficient diagnostic performance.

Previous research proposed an AI-based alarm system (AI-S) to screen patients for ALVSD, demonstrating greater accuracy than BNP screening and improved accessibility compared to widespread echocardiography. AI-S demonstrated a sensitivity of 92.6% (standard error \[SE\] 0.042) for detecting medium-risk ALVSD patients and 63% (SE 0.154) for high-risk ALVSD patients, with a specificity of 92.7% (SE 0.003) for medium-risk patients and 98.7% (SE 0.002) for high-risk patients. AI-S is accuracy, noninvasive, highly accessible in local medical clinics, less time-consuming, and cost-effective, making it a valuable screening tool for identifying ALVSD prior to echocardiography or other confirmatory diagnostic methods.

To date, no randomized controlled trial has assessed the cost-effectiveness and impact of AI-assisted screening tools for heart failure prevention in Asians. The ECG AI-Guided Screening for Low Ejection Fraction (EAGLE) trial reported a 32% increase in diagnosing of low left ventricular ejection fraction (defined as LVEF ≤50%) within 90 days of the ECG. However, this population was not Asian, and randomization involved primary care teams rather than participants. Therefore, this randomized controlled trial is designed to evaluate the impact of AI-S on diagnosing low ejection fraction in Asians, its cost-effectiveness, and the incidence of worsening HF (defined as admission for HF or HF-related emergency department visits).

Conditions

• Heart Failure
• Ventricular Dysfunction, Left
• Artificial Intelligence
• Early Diagnosis
• Asymptomatic Diseases
• Cost-Benefit Analysis

Interventions

DIAGNOSTIC_TEST

AI-ECG guided diagnosis

Participants undergo screening using the AI-ECG system. Participants identified as medium- to high-risk for LV dysfunction (LVEF \<50%) are recommended for echocardiography to confirm the diagnosis and guide subsequent management.

Sponsors & Collaborators

• National Defense Medical Center, Taiwan

  lead OTHER

Study Design

Allocation

RANDOMIZED

Purpose

SCREENING

Masking

NONE

Model

PARALLEL

Eligibility

Min Age

60 Years

Max Age

85 Years

Sex

ALL

Healthy Volunteers

Yes

Timeline & Regulatory

Start

2024-06-01

Primary Completion

2027-06-01

Completion

2027-06-01

Countries

• Taiwan

Study Locations