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Relevance of Artificial Intelligence-Assisted Echocardiography for Left Ventricular Ejection Fraction Assessment in Geriatric Patients (REFERENCE-AI)
Heart failure (HF) is the leading cause of hospitalization among adults aged 80 years and older and represents a major diagnostic challenge in geriatric medicine due to frequently atypical clinical presentations and the presence of multiple comorbidities. Although transthoracic echocardiography (TTE) with measurement of left ventricular ejection fraction (LVEF) remains the gold standard for cardiac functional assessment, access to echocardiography is often limited in geriatric wards.
Recent advances in artificial intelligence (AI) have enabled the development of portable ultrasound devices and automated image analysis software capable of providing reliable and reproducible LVEF measurements. AI-assisted automated LVEF assessment (AutoEF-AI) may therefore represent a valuable alternative to conventional echocardiography for the cardiac evaluation of older patients with heart failure.
Studie Overzicht
Toestand
Conditie
Interventie / Behandeling
Gedetailleerde beschrijving
Prospective, single-center interventional study comparing two methods of left ventricular ejection fraction measurement in patients aged 75 years and older hospitalized for acute heart failure.Each hemodynamically stable participant will undergo two echocardiographic examinations performed within 24 hours:
- Standard Echocardiography (Reference Method)
- AI-assisted automated LVEF assessment (AutoEF-AI)
Studietype
Inschrijving (Geschat)
Fase
- Niet toepasbaar
Contacten en locaties
Studiecontact
- Naam: Isabelle DUFOUR
- Telefoonnummer: +33 (0) 185781011
- E-mail: isabelle.dufour@gerondif.org
Studie Contact Back-up
- Naam: Prisca LUCAS, PhD MPH
- Telefoonnummer: +33 (0)185737323
- E-mail: prisca.lucas@gerondif.org
Studie Locaties
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Paris, Frankrijk, 75013
- Werving
- Broca Hospital
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Contact:
- Olivier HANON, MD PHD
- Telefoonnummer: 01 44 08 33 81
- E-mail: olivier.hanon@aphp.fr
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Deelname Criteria
Geschiktheidscriteria
Leeftijden die in aanmerking komen voor studie
- Oudere volwassene
Accepteert gezonde vrijwilligers
Beschrijving
Inclusion Criteria:
- Age ≥75 years.
- Hospitalization in a geriatric unit for acute heart failure according to the 2021 ESC diagnostic criteria.
- Hemodynamic stability at the time of echocardiographic examination.
- Ability to understand study information, provide written informed consent, and willingness to participate.
- Affiliation with, or beneficiary of, a health insurance/social security scheme.
Exclusion Criteria:
- Hemodynamic instability preventing echocardiographic assessment.
- Contraindication to transthoracic echocardiography.
- Patients under legal protection (guardianship, curatorship, or judicial protection measures) or unable to provide informed consent.
- Refusal to participate in the study.
Studie plan
Hoe is de studie opgezet?
Ontwerpdetails
- Primair doel: Diagnostisch
- Toewijzing: Niet-gerandomiseerd
- Interventioneel model: Crossover-opdracht
- Masker: Enkel
Wapens en interventies
Deelnemersgroep / Arm |
Interventie / Behandeling |
|---|---|
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Ander: Standard Echocardiography (Reference method)
The standard echocardiography will be performed by a cardiologist as part of routine clinical care.
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The standard echocardiography will be performed by an expert cardiologist as part of routine clinical care and will serve as the gold standard
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Experimenteel: AutoEF-AI Assessment
AI-assisted automated LVEF assessment
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Performed by a geriatrician who has completed a one-day practical training session and combines:
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Wat meet het onderzoek?
Primaire uitkomstmaten
Uitkomstmaat |
Maatregel Beschrijving |
Tijdsspanne |
|---|---|---|
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Agreement between LVEF measured using AutoEF-AI and LVEF measured using standard echocardiography.
Tijdsspanne: At baseline
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Agreement will be assessed using:
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At baseline
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Secundaire uitkomstmaten
Uitkomstmaat |
Maatregel Beschrijving |
Tijdsspanne |
|---|---|---|
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Diagnostic performance of AutoEF-AI for the detection of LVEF <50%, including sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy.
Tijdsspanne: At baseline
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Calculation of the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and overall accuracy of AutoEF-AI for identifying left ventricular ejection fraction (LVEF) below 50%, compared with the standard reference method.
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At baseline
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Analysis of factors associated with agreement between the two methods
Tijdsspanne: At baseline
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Multiple linear regression model will be used to identify clinical and technical variables (age, sex, cardiovascular history, presence of a pacemaker, cardiac arrhythmias, image quality, comorbidities, etc.) associated with a significant discrepancy between LVEF measurements obtained using AutoEF-AI and standard echocardiography.
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At baseline
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Feasibility of AutoEF-AI use by a geriatrician after a short training program
Tijdsspanne: At baseline
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At baseline
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Medewerkers en onderzoekers
Sponsor
Onderzoekers
- Hoofdonderzoeker: Olivier HANON, MD PhD, Geriatric Department, Broca hospital
Studie record data
Bestudeer belangrijke data
Studie start (Werkelijk)
Primaire voltooiing (Geschat)
Studie voltooiing (Geschat)
Studieregistratiedata
Eerst ingediend
Eerst ingediend dat voldeed aan de QC-criteria
Eerst geplaatst (Werkelijk)
Updates van studierecords
Laatste update geplaatst (Werkelijk)
Laatste update ingediend die voldeed aan QC-criteria
Laatst geverifieerd
Meer informatie
Termen gerelateerd aan deze studie
Trefwoorden
Aanvullende relevante MeSH-voorwaarden
Andere studie-ID-nummers
- 2025-A02039-40
Informatie over medicijnen en apparaten, studiedocumenten
Bestudeert een door de Amerikaanse FDA gereguleerd geneesmiddel
Bestudeert een door de Amerikaanse FDA gereguleerd apparaatproduct
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