AI-based Echocardiographic Quantification in Heart Failure

NCT ID: NCT07010952

Last Updated: 2025-06-15

Basic Information

• Recruitment Status: NOT_YET_RECRUITING
• Total Enrollment: 3000 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2025-07-01
• Study Completion Date: 2025-12-31

Brief Summary

Heart failure (HF) is a clinical complication. About half of HF patients have heart failure with normal systolic fraction (HFpEF), and most of them are elderly women. The other type is systolic heart failure, characterized by a left ventricular ejection fraction of less than 40 (LVEF<40). The clinical symptoms of HFpEF are very similar to those of low systolic fraction heart failure (HFrEF) with abnormal left ventricular ejection fraction. Generally speaking, the morbidity and severity of HFrEF are higher, and the survival rate is lower. HFpEF is generally difficult to diagnose, so it is critical to find a method to accurately diagnose HFpEF. HFpEF is most commonly diagnosed by echocardiography and biomarkers. In a cardiac ultrasound examination, it is impossible to diagnose HFpEF based on a single parameter of the results. We need multiple examination parameters to gather enough evidence to confirm the existence of HFpEF. These parameters include the mitral inflow velocity pattern, the pulmonary vein flow pattern, changes in flow velocity from the left atrium to the left ventricle, tissue Doppler measurements, and M-mode ultrasound measurements. We train artificial intelligence to distinguish between normal and abnormal cardiac ultrasound images, measure or evaluate all the above parameters, and analyze all the data. We hope that, with the help of artificial intelligence, we can improve the prediction and diagnosis rate of HFpEF.

Simply diagnosing HFrEF requires an LVEF of less than 40%. Diagnosing HFpEF poses significant clinical challenges because no single tool or method can reliably confirm the condition or predict associated hospitalizations. Consequently, diagnosis depends heavily on physician judgment, requiring the synthesis of considerable clinical data and information. Recognizing the heterogeneity of the HFpEF phenotype, phenomapping integrates comprehensive data (clinical history, physiological measurements, biomarkers, ECG, echocardiographic parameters) to stratify patients into distinct subtypes, thereby optimizing classification for improved prognostic prediction. It can be seen from this that HF will rely heavily on artificial intelligence in the future to assist in patient data management and classification diagnosis and further develop clinical prediction models. This research project will implement a multi-center design to collect ultrasound images from patients with heart failure and perform relevant analyses using artificial intelligence.

Conditions

Heart Failure With Preserved Ejection Fraction

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: RETROSPECTIVE

Study Groups

Adult heart failure cohort with comprehensive echocardiographic imaging for AI-driven HF phenotyping

We will analyze a prospective, multicenter cohort of adult patients (≥18 years) admitted for acute or chronic heart failure at three tertiary hospitals between January 2021 and December 2023. Participants were stratified by index-echocardiographic left ventricular ejection fraction (LVEF): 1. HFpEF group: LVEF ≥ 50%, typical HF signs/symptoms, and objective evidence of diastolic dysfunction. 2. HFrEF group: LVEF \< 40%, consistent with guideline-defined systolic HF. Patients with mid-range LVEF (40-49%), significant valvular disease, congenital heart disease, or inadequate image quality were excluded. For every enrollee, complete transthoracic echocardiography was performed within 48 h of admission. Raw DICOM cine loops (parasternal long/short axis, apical 2-/3-/4-chamber, Doppler, and tissue Doppler views) were archived. Standardized hemodynamic and biomarker profiles, 12-lead ECGs, and comprehensive clinical data will be collected.

AI-based image analysis

Intervention Type: DIAGNOSTIC_TEST

AI-based imaging analysis

AI-based imaging analysis

Intervention Type: OTHER

AI-based imaging analysis

Interventions

AI-based image analysis

AI-based imaging analysis

Intervention Type: DIAGNOSTIC_TEST

AI-based imaging analysis

AI-based imaging analysis

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

1. Age ≥ 18 years.

2. Admission for acute or chronic heart failure between January 1, 2017, and April 30, 2024.

3. Transthoracic echocardiography completed ≤ 48 h after admission with diagnostic-quality DICOM cine loops (parasternal long/short axis and apical 2-/3-/4-chamber views plus Doppler and tissue Doppler).

4. Meets one of the two predefined phenotypes:

• HFpEF: LVEF ≥ 50 % + typical HF signs/symptoms + objective diastolic dysfunction.
• HFrEF: LVEF < 40 % in keeping with guideline-defined systolic HF.

Exclusion Criteria

1. Mid-range LVEF 40-49 %.

2. Significant native or prosthetic valvular heart disease (moderate-to-severe) requiring surgery or trans-catheter therapy.

3. Congenital heart disease, hypertrophic cardiomyopathy, restrictive or constrictive pericardial pathology, or prior cardiac transplantation/LVAD.

4. Inadequate echocardiographic image quality (e.g., missing views, severe acoustic shadowing) precludes automated analysis.

5. Hemodynamic instability preventing standardized imaging or data collection.

6. Pregnancy.

7. Concurrent enrollment in another interventional trial that may confound results of imaging or biomarkers.

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Mackay Memorial Hospital

OTHER

Sponsor Role: lead

Responsible Party

Chung-Lieh Hung

Director of Ultrasound Imaging and Telemedicine

Responsibility Role: PRINCIPAL_INVESTIGATOR

Other Identifiers

25MMHIS019e

Identifier Type: -

Identifier Source: org_study_id