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.
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.
Able to understand and accept the study constraints and to provide informed consent (either themselves or a legal representative).
Unwillingness or inability to sign the written informed consent.
Previous Heart Failure diagnosis.
Unavailability or suboptimal quality ECG.
Parallel
Willem™ platform ECG assessment
DeviceAI-assisted ECG analysis via the Willem™ platform
Device performance in patients with suspected Heart Failure
To compare the diagnostic performance of clinicians using a decision-aid system based on AI-assisted ECG vs. standard ECG in patients with suspected Heart Failure (symptoms and/or signs of Heart Failure) in the primary care setting analyzing the frequency of patients without cardiac pattern alterations in the ECG, ending up with diagnosis of absence of HF (using the NT-proBNP for stratification and eventually echocardiography when needed) 7 days after screening with the cardiology service.
Device performance in patients at cardiovascular risk but without Heart Failure symptoms
To compare the diagnostic performance of clinicians using a decision-aid system based on AI-assisted ECG vs. standard ECG in patients at risk but without Heart Failure symptoms in the primary care setting analyzing the frequency of patients with cardiac pattern alterations in the ECG, ending up with a diagnosis of HF (using the NT-proBNP for stratification and eventually echocardiography when needed) 7 days after screening with the cardiology service.
Device performance at 6 months after ECG
To compare the clinical performance of a decision-aid system based on AI-assisted ECG vs standard ECG, in patients with symptoms and patients at risk of Heart Failure in the primary care setting analyzing the frequency of patients with cardiac pattern alterations in the ECG, ending up with diagnosis of HF (using the NT-proBNP for stratification and eventually echocardiography when needed) 6 months after screening with the cardiology service.
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Idoven 1903 S.L.
Lead sponsor
Fundación para la Investigación Biomédica del Hospital 12 de Octubre
Collaborator
Fundación para la Investigación Biomédica del Hospital Gregorio Maranon
Collaborator
Fundación para la Investigación e Innovación Biosanitaria de Atención Primaria de la Comunidad de Madrid (FIIBAP)
Collaborator
Instituto de Investigación Marqués de Valdecilla
Collaborator
Karolinska Institutet
Collaborator
Region Stockholm
Collaborator
AstraZeneca
Collaborator
Servicio Madrileno De Salud (SERMAS)
Collaborator
