Heart Failure With Preserved Ejection FractionNCT07010952
AI-based Echocardiographic Quantification in Heart Failure
An observational study of Heart Failure With Preserved Ejection Fraction, run by Mackay Memorial Hospital.
Trial 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). Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Heart Failure With Preserved Ejection Fraction
- What is being tested?
- AI-based image analysis and AI-based imaging analysis
- How many people
- 3,000 participants
- Where
- No sites listed yet
- Study dates
- 1 Jul 2025to 31 Dec 2025
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- 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.
Who cannot join
- 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.
About this study
Official title: Artificial Intelligence-based Automatic Echocardiographic Quantification in Advanced Heart Failure (AIED Study)
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.
- Being tested
- AI-based image analysis, AI-based imaging analysis
- Keywords
- Heart Failure, Diastolic, Echocardiography, Artificial Intelligence
- Medical subject headings
- Heart Failure, Diastolic
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-based image analysisDiagnostic test
AI-based imaging analysis
AI-based imaging analysisOther
AI-based imaging analysis
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Diagnostic Test: AI-based image analysis; Other: AI-based imaging analysis |
What is being measured
The primary outcome is the main question the study is designed to answer.
AI-driven HF phenotyping
AI-driven HF phenotyping
Measured: Data analysis period: June 1 to December 1, 2025
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted8 Jun 2025
- Study start1 Jul 2025 (estimated)
- Primary completion1 Dec 2025 (estimated)
- Study completion31 Dec 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Heart Failure With Preserved Ejection FractionStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 3,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Mackay Memorial Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Chung-Lieh Hung, Mackay Memorial Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07010952
- Sponsor's protocol ID
- 25MMHIS019e
- Status last verified
- May 2025
- First submitted
- 30 May 2025
- Last updated
- 15 Jun 2025
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 15 Jun 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence-based Automatic Echocardiographic Quantification in Advanced Heart Failure (AIED Study)
- ClinicalTrials.gov ID
- NCT07010952
- Acronym / sponsor ID
- 25MMHIS019e
- Keywords
- Heart Failure, Diastolic, Echocardiography, Artificial Intelligence
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 3,000 participants (estimated)
- Lead sponsor
- Mackay Memorial HospitalUniversity, hospital or other
- Study start
- 1 Jul 2025 (estimated)
- Primary completion
- 1 Dec 2025 (estimated)
- Study completion
- 31 Dec 2025 (estimated)
- First posted
- 8 Jun 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Adult heart failure cohort with comprehensive echocardiographic imaging for AI-driven HF phenotyping | – | Diagnostic Test: AI-based image analysis, Other: AI-based imaging analysisWe 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (6 listed)
- 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:
Key exclusion criteria (7 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI-based image analysisAI-based imaging analysis | Diagnostic test | Adult heart failure cohort with comprehensive echocardiographic imaging for AI-driven HF phenotyping |
| AI-based imaging analysisAI-based imaging analysis | Other | Adult heart failure cohort with comprehensive echocardiographic imaging for AI-driven HF phenotyping |
Primary outcome
AI-driven HF phenotyping
Time frame: Data analysis period: June 1 to December 1, 2025
AI-driven HF phenotyping
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 15 Jun 2025.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
The record lists no contact. Use the official record to reach the sponsor.
Questions about this trial
What is this clinical trial studying?
An observational study of Heart Failure With Preserved Ejection Fraction, run by Mackay Memorial Hospital.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 6 inclusion and 7 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 15 Jun 2025. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
AI-driven HF phenotyping (time frame: Data analysis period: June 1 to December 1, 2025). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 15 Jun 2025.