AI-powered ECG interpretation to detect heart failure in older primary care patients
Determining Efficacy of an Artificial Intelligence-based System for Heart Failure Detection Through Interpretation of Electrocardiograms: a Pragmatic Randomized Clinical Trial (DECISION)
This trial tests whether the AI tool Willem™ helps primary care doctors detect heart failure from ECGs in people aged 65 and older who have symptoms or are at high cardiovascular risk.
Quick facts
| Phase | Not applicable |
|---|---|
| Study type | Interventional |
| Enrollment | 1968 (estimated) |
| Ages | 65 Years and up |
| Sex | All |
| Sponsor | Idoven 1903 S.L. Industry-sponsored |
| Locations | 5 sites (Madrid and 4 other locations) |
| Trial ID | NCT07113223 on ClinicalTrials.gov |
What this trial studies
This multicenter, pragmatic, randomized trial compares AI-assisted ECG interpretation using the Willem™ platform to standard ECG evaluation in primary care settings. Primary care centers are randomized to provide either the AI output to clinicians or usual ECG interpretation, with diagnostic confirmation by echocardiography and clinical follow-up. The primary analysis will compare sensitivity, specificity, and predictive values for heart failure detection, and secondary analyses will examine healthcare resource use, clinical outcomes, and provider usability feedback. The trial enrolls patients aged 65 and older who present with suspected heart failure or who are at elevated cardiovascular risk.
Who should consider this trial
Good fit: Ideal candidates are patients aged 65 or older presenting to participating primary care centers who either have symptoms suggestive of heart failure or have high cardiovascular risk and can provide informed consent.
Not a fit: Patients younger than 65, those with an existing confirmed diagnosis of heart failure, or those unable to attend participating centers or provide consent are unlikely to benefit from participation.
Why it matters
Potential benefit: If successful, it could enable earlier and more accurate heart failure diagnosis in primary care, leading to timelier treatment and fewer hospitalizations.
How similar studies have performed: Prior studies have shown AI can detect reduced ejection fraction and other cardiac abnormalities from ECGs with good accuracy, but randomized evidence showing improved clinical outcomes in routine primary care remains limited.
Eligibility criteria
Show full inclusion / exclusion criteria
Inclusion Criteria: * Patients with Suspected HF (Group S): * Able to understand and accept the study constraints and to provide informed consent (either themselves or a legal representative). * Age over 65 years (i.e., 65 included). * Presence of symptoms and/or signs typical of Heart Failure (defined by the European Society of Cardiology, ESC), including breathlessness (during activity or at rest, lying down, waking up at night needing to catch their breath), fatigue, swollen ankles/legs, and/or palpitations. * Patients at Risk of Heart Failure due to the presence of cardiovascular (Group R): * Able to understand and accept the study constraints and to provide informed consent (either themselves or a legal representative). * Age over 65 years (i.e., 65 included). * Absence of symptoms and/or signs typical of Heart Failure (defined by the ESC), including breathlessness (during activity or at rest, lying down, waking up at night needing to catch their breath), fatigue, swollen ankles/legs, and/or palpitations. * Presence of at least 1 ACC/AHA Heart Failure risk factor, including hypertension, cardiovascular disease (atrial fibrillation, coronary heart disease or stroke), diabetes, obesity, exposure to cardiotoxic agents, genetic variant for cardiomyopathy, or family history of cardiomyopathy that requires an ECG test for any reason in a primary care center or with an indication of a regular health examination where an ECG is included. Exclusion Criteria: * Unwillingness or inability to sign the written informed consent. * Previous Heart Failure diagnosis. * Unavailability or suboptimal quality ECG.
Where this trial is running
Madrid and 4 other locations
- Hospital General Universitario Gregorio Marañón — Madrid, Spain (Recruiting)
- Hospital Universitario 12 de Octubre — Madrid, Spain (Recruiting)
- Primary Care: Gerencia Asistencial Atención Primaria Madrid — Madrid, Spain (Recruiting)
- Hospital Universitario Marqués de Valdecilla — Santander, Spain (Recruiting)
- Region Stockholm — Stockholm, Sweden (Recruiting)
Study contacts
- Study coordinator: Juan Francisco Delgado Jiménez, MD, PhD
- Email: juan.delgado@salud.madrid.org
- Phone: +34917792640
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.