AI-based Prediction of Cardiac Function Using Echocardiography and Body Composition Data (ECHO-FIT Study)

NCT ID: NCT06811519

Last Updated: 2025-03-04

Basic Information

• Recruitment Status: RECRUITING
• Total Enrollment: 2000 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2025-02-24
• Study Completion Date: 2028-12-31

Brief Summary

This prospective observational study (ECHO-FIT Study) aims to develop and validate a predictive model for cardiac function, particularly left ventricular ejection fraction (LVEF), by integrating echocardiographic measurements with body composition data obtained from the QCCUNIQ BC 720 device.

The study plans to enroll 2,000 adult participants, comprising 1,000 individuals with normal LVEF (≥50%) and 1,000 with heart failure (LVEF <50%), all of whom will undergo standard-of-care echocardiography and body composition analysis.

By analyzing the relationships between key echocardiographic parameters (such as LVEF and diastolic function) and body composition measures (including fat mass, skeletal muscle mass, and total body water), we will develop a non-invasive prediction model capable of identifying individuals at higher risk of cardiac dysfunction.

This innovative approach has the potential to enhance early detection and personalized management of heart failure, reduce dependence on resource-intensive diagnostic procedures, and ultimately improve patient outcomes.

Detailed Description

Background and Rationale:

Heart failure represents a significant global health burden, characterized by high morbidity and mortality rates. While echocardiography remains the gold standard for heart failure diagnosis and monitoring, providing crucial measurements like left ventricular ejection fraction (LVEF) and diastolic function assessment, its widespread implementation is limited by resource constraints and operator dependency. Bioelectrical impedance analysis (BIA) offers a promising complementary approach, providing rapid and non-invasive assessment of body composition parameters that have shown correlations with cardiovascular outcomes. This study seeks to leverage the potential synergy between echocardiographic findings and body composition data to develop a more accessible screening tool for cardiac dysfunction.

Study Objectives:

• Primary: To develop and validate a predictive model for left ventricular function by integrating body composition data from the QCCUNIQ BC 720 device with standard echocardiographic parameters.
• Secondary:

• To investigate correlations between body composition indices and echocardiographic measurements
• To evaluate the utility of body composition analysis in identifying high-risk cardiovascular patients
• To assess the model's potential as a screening tool in resource-limited settings

Methodology:

This single-center, prospective observational study will enroll 2,000 adults (≥20 years) undergoing routine echocardiography, equally divided between those with normal cardiac function (LVEF ≥50%) and heart failure (LVEF <50%). Participants will undergo body composition analysis using the QCCUNIQ BC 720 device within one week of their echocardiogram.

Data Collection and Analysis:

Comprehensive data collection will include standard echocardiographic parameters (LVEF, diastolic function, structural measurements) and detailed body composition analysis (fat mass, skeletal muscle mass, total body water). Statistical analysis will employ both traditional regression methods and advanced machine learning algorithms to develop the predictive model. Model validation will utilize k-fold cross-validation, with performance assessed through standard metrics including sensitivity, specificity, and area under the curve (AUC).

Conditions

Heart Failure; Left Ventricular (LV) Systolic Dysfunction; Body Composition Measurement; Artificial Intelligence (AI)

Study Design

• Observational Model Type: CASE_CROSSOVER
• Study Time Perspective: PROSPECTIVE

Study Groups

Diagnostic Test: Scanning body composition analyzer and performing AI algorithms

Diagnostic Test: Scanning body composition analyzer and performing AI algorithms

Body Composition Analyzer (ACCUNIQ BC720)

Intervention Type: DIAGNOSTIC_TEST

Body Composition Analyzer (ACCUNIQ BC720)

Interventions

Body Composition Analyzer (ACCUNIQ BC720)

Body Composition Analyzer (ACCUNIQ BC720)

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

Inclusion Criteria

• Aged 20 years or older.
• Undergoing a standard echocardiographic examination.
• Providing consent to undergo body composition analysis.
• Signing the informed consent form to voluntarily participate in the study.

Exclusion Criteria

• Having a physical or mental condition that makes it impossible to conduct an echocardiogram or perform body composition analysis.
• Deemed inappropriate for study participation by the researcher (e.g., unable to cooperate).

• Minimum Eligible Age: 20 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Yonsei University

OTHER

Sponsor Role: lead

Responsible Party

In Hyun Jung

Professor

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

In Hyun Jung, MD., PhD.

Role: PRINCIPAL_INVESTIGATOR

Severance Hospital

Locations

Yongin Severance Hospital

Yongin, Gyeonggi-do, South Korea

Site Status: RECRUITING

Countries

South Korea

Central Contacts

SungA Bae, MD., PhD.

Role: CONTACT

cardiobsa@yuhs.ac

01023273578

In Hyun Jung, MD., PhD.

Role: CONTACT

saveheart@yuhs.ac

Facility Contacts

SungA Bae

Role: primary

cardiobsa@yuhs.ac

01023273578

Other Identifiers

ECHO-FIT

Identifier Type: -

Identifier Source: org_study_id