Determining Efficacy of an Artificial Intelligence-based System for Heart Failure Detection Through Interpretation of Electrocardiograms: a Pragmatic Randomized Clinical Trial (DECISION)
Trial ID: NCT07113223
Status: RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2025-07-23
Completion date: 2026-09
Sponsor: Idoven 1903 S.L.
Collaborators: Fundación para la Investigación Biomédica del Hospital 12 de Octubre; Fundación para la Investigación Biomédica del Hospital Gregorio Maranon; Fundación para la Investigación e Innovación Biosanitaria de Atención Primaria de la Comunidad de Madrid (FIIBAP); Instituto de Investigación Marqués de Valdecilla; Karolinska Institutet; Region Stockholm; AstraZeneca; Servicio Madrileno De Salud (SERMAS)
Conditions: Heart Failure; Cardiovascular Risk Factors
Interventions: Willem™ platform ECG assessment
Locations: Hospital General Universitario Gregorio Marañón; Hospital Universitario 12 de Octubre; Primary Care: Gerencia Asistencial Atención Primaria Madrid; Hospital Universitario Marqués de Valdecilla; Region Stockholm
Countries: Spain; Spain; Spain; Spain; Sweden
Enrollment: 1968
Age range: 65 Years - None
Sex: ALL

Summary:
The DECISION trial aims to evaluate the efficacy of an artificial intelligence (AI)-powered system, Willem™, for improving the detection of heart failure (HF) in primary care settings by interpreting electrocardiograms (ECGs). The study seeks to answer whether AI-assisted ECG interpretation enhances diagnostic accuracy and clinical outcomes compared to standard ECG evaluation in patients with suspected HF or those at high risk.

This multicenter, pragmatic, randomized clinical trial involves two groups: patients receiving AI-assisted ECG analysis and those undergoing standard ECG evaluation. The study's primary analysis will compare the diagnostic performance of AI-assisted ECG versus standard ECG using sensitivity, specificity, and predictive value metrics. Secondary analyses will evaluate healthcare resource utilization, clinical outcomes, and usability feedback from healthcare providers. Results will inform the potential integration of AI-assisted ECG in routine primary care workflows for earlier HF detection and better resource allocation.

Eligibility 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.

Outcomes:
Device performance in patients with suspected Heart Failure