Determining Efficacy of an Artificial Intelligence-based System for Heart Failure Detection Through Interpretation of Electrocardiograms (DECISION)

Not Applicable

Recruiting

• Conditions: Heart Failure; Cardiovascular Risk Factors

• Registration Number: NCT07113223
• Lead Sponsor: Idoven 1903 S.L.

Brief 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.

Detailed Description

Heart failure (HF) is a prevalent and underdiagnosed condition with high morbidity and mortality. Up to 50% of HF cases remain undetected, often due to subtle or absent symptoms in early stages. Early diagnosis is critical to improving outcomes, reducing hospitalizations, and alleviating healthcare costs. While ECGs are a cornerstone in HF diagnosis, their interpretation in primary care can be challenging, leading to diagnostic delays.

Artificial intelligence (AI) has emerged as a promising tool to support clinicians by enhancing ECG interpretation. In this regard, the DECISION trial evaluates the Willem™ platform, an AI-powered decision-support system, to improve HF detection. Willem™ uses a proprietary database to analyze ECGs, identifying over 80 cardiac patterns with high accuracy.

This study hypothesizes that AI-assisted ECG improves HF detection compared to standard ECG interpretation. Therefore, the main goal of the DECISION trial is to assess the diagnostic performance of AI-assisted ECG in detecting structural and functional cardiac abnormalities indicative of HF.

This multicenter, randomized trial includes primary care centers (PCCs) in Spain and Sweden, randomized into two groups: an intervention group using AI-assisted ECG and a control group using standard ECG. AI outputs will be available for physicians in the intervention group as supplementary information during decision making.

Primary outcomes focus on the accuracy of HF detection confirmed by transthoracic echocardiograms (TTE). Secondary outcomes include healthcare resource utilization, clinical outcomes, and physician satisfaction. The results will inform whether AI can be integrated into primary care workflows to optimize HF diagnosis and management.

Study Timeline

• Study Start: 2025-07-23
• Study First Submitted: 2025-07-28
• Status Verified: 2025-08
• Study First Submitted QC: 2025-08-04
• Last Update Submitted: 2025-08-04
• Study First Posted: 2025-08-08
• Last Update Posted: 2025-08-08
• Primary Completion: 2025-12
• Study Completion: 2025-12-01

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 1968

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.

Study & Design

• Study Type: INTERVENTIONAL
• Study Design: PARALLEL

Primary Outcome Measures

Name	Time	Method
Device performance in patients with suspected Heart Failure	After performing the transthoracic echocardiography (TTE) in Visit 3 (7 days after screening)	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.

Secondary Outcome Measures

Name	Time	Method
Device performance in patients at cardiovascular risk but without Heart Failure symptoms	After performing the transthoracic echocardiography (TTE) in Visit 3 (7 days after screening)	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	Six months after the ECG was performed	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.

Trial Locations

Locations (5)

Hospital General Universitario Gregorio Marañón; 🇪🇸 Madrid, Spain

Hospital Universitario 12 de Octubre; 🇪🇸 Madrid, Spain

Primary Care: Gerencia Asistencial Atención Primaria Madrid; 🇪🇸 Madrid, Spain

Hospital Universitario Marqués de Valdecilla; 🇪🇸 Santander, Spain

Region Stockholm; 🇸🇪 Stockholm, Sweden