Clinical trial NCT07010952: AI-based Echocardiographic Quantification in Heart Failure
Official title: Artificial Intelligence-based Automatic Echocardiographic Quantification in Advanced Heart Failure (AIED Study)
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Heart Failure With Preserved Ejection Fraction
Keywords: Heart Failure, Diastolic
Echocardiography
Artificial Intelligence
Lead sponsor: Mackay Memorial Hospital
Sponsor class: OTHER
First posted: 2025-06-08
Enrollment: 3000
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
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.

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.


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.

Primary outcomes:
measure: AI-driven HF phenotyping
timeFrame: Data analysis period: June 1 to December 1, 2025
Secondary outcomes:
Other outcomes:
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Study officials:
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