AI-based Echocardiographic Quantification in Heart Failure

NCT07010952 | Not Yet Recruiting DMC

• 1 other identifier: 25MMHIS019e
• Study Type: observational
• Target: 3,000
• Locations: 0 countries
• Sites: N/A

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

Keywords

Heart Failure, Diastolic; Echocardiography; Artificial Intelligence

Outcome Measures

Primary Outcomes (1)

• AI-driven HF phenotyping

  AI-driven HF phenotyping

  Data analysis period: June 1 to December 1, 2025

Study Arms (1)

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.

Diagnostic Test: AI-based image analysis; Other: AI-based imaging analysis

Interventions

AI-based image analysis DIAGNOSTIC_TEST

AI-based imaging analysis

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

AI-based imaging analysis OTHER

AI-based imaging analysis

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

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

We will analyze a prospective, multi-center cohort of adult patients (≥18 years) admitted for acute or chronic heart failure at three tertiary hospitals between January 1, 2017, and April 30, 2024. Participants were stratified (into two mutually exclusive groups) based on 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 will be excluded.

You may qualify if:

• Age ≥ 18 years.
• Admission for acute or chronic heart failure between January 1, 2017, and April 30, 2024.
• 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).
• 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.

You may not qualify if:

• Mid-range LVEF 40-49 %.
• Significant native or prosthetic valvular heart disease (moderate-to-severe) requiring surgery or trans-catheter therapy.
• Congenital heart disease, hypertrophic cardiomyopathy, restrictive or constrictive pericardial pathology, or prior cardiac transplantation/LVAD.
• Inadequate echocardiographic image quality (e.g., missing views, severe acoustic shadowing) precludes automated analysis.
• Hemodynamic instability preventing standardized imaging or data collection.
• Pregnancy.
• Concurrent enrollment in another interventional trial that may confound results of imaging or biomarkers.

Sponsors & Collaborators

• Mackay Memorial Hospital: lead

MeSH Terms

Conditions

Heart Failure, Diastolic

Condition Hierarchy (Ancestors)

Heart Failure; Heart Diseases; Cardiovascular Diseases

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Director of Ultrasound Imaging and Telemedicine

Study Record Dates

• First Submitted: May 30, 2025
• First Posted: June 8, 2025
• Study Start: July 1, 2025
• Primary Completion: December 1, 2025
• Study Completion: December 31, 2025
• Last Updated: June 15, 2025

Record last verified: 2025-05

Data Sharing

• IPD Sharing: Will not share