Aug 8, 2025

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. Official Title Determining Efficacy of an Artificial Intelligence-based System for Heart Failure Detection Through Interpretation of Electrocardiograms: a Pragmatic Randomized Clinical Trial (DECISION) Conditions Heart Failure Cardiovascular Risk Factors Heart Failure Cardiovascular Risk Factors Heart Failure Cardiovascular Risk Factors Heart Failure Cardiovascular Risk Factors Intervention / Treatment Device Willem™ platform ECG assessment Other Study ID Numbers 230225 ( Other Grant/Funding Number ) (OTHER_GRANT: EIT Health) 230225 ( Other Grant/Funding Number ) (OTHER_GRANT: EIT Health) Study Start Actual Primary Completion Estimated Study Completion Estimated Enrollment Estimated Study Type Interventional Phase Not Applicable Resource links provided by the National Library of Medicine MedlinePlus (https://medlineplus.gov/) related topics: Heart Failure (https://medlineplus.gov/heartfailure.html) FDA Drug and Device Resources (https://clinicaltrials.gov/fda-links) Contacts and Locations This section provides contact details for people who can answer questions about joining this study, and information on where this study is taking place. To learn more, please see the Contacts and Locations section in How to Read a Study Record (https://clinicaltrials.gov/study-basics/how-to-read-study-record#contacts-and-locations) Study Contact Name: Juan Francisco Delgado Jiménez, MD, PhD Phone Number: Email: juan.delgado@salud.madrid.org This study has 5 locations Spain Madrid, Spain Not yet recruiting Hospital General Universitario Gregorio Marañón Contact Javier Bermejo Thomas, MD, PhD javier.bermejo@salud.madrid.org Madrid, Spain Recruiting Hospital Universitario 12 de Octubre Contact Javier de Juan Bagudá, MD, PhD javier.juan@salud.madrid.org Madrid, Spain Recruiting Primary Care: Gerencia Asistencial Atención Primaria Madrid Contact Sara Ares Blanco, MD, PhD sara.ares@salud.madrid.org Santander, Spain Not yet recruiting Hospital Universitario Marqués de Valdecilla Contact José María de la Torre, MD, PhD josemariadela.torre@scsalud.es Sweden Stockholm, Sweden Not yet recruiting Region Stockholm Contact Jacob Andersson Emad, MD, PhD jacob.andersson-emad@regionstockholm.se Click to view interactive map Participation Criteria Researchers look for people who fit a certain description, called eligibility criteria . Some examples of these criteria are a person's general health condition or prior treatments. For general information about clinical research, read Learn About Studies (https://clinicaltrials.gov/study-basics/learn-about-studies) Eligibility Criteria Description 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. Ages Eligible for Study 65 Years and older Older Adult Sexes Eligible for Study All Accepts Healthy Volunteers No Study Plan This section provides details of the study plan, including how the study is designed and what the study is measuring. Expand all Collapse all How is the study designed? Design Details Primary Purpose Diagnostic Allocation Randomized Interventional Model Parallel Assignment Interventional Model Description: Multicenter, randomized, two-arm parallel-group, controlled trial Masking Single Participant Arms and Interventions Participant Group/Arm Intervention/Treatment Participant Group/Arm Experimental Experimental AI-assisted ECG analysis via the Willem™ platform Intervention/Treatment Device Willem™ platform ECG assessment AI-assisted ECG analysis via the Willem™ platform Participant Group/Arm No Intervention Comparator Standard ECG assessment Intervention/Treatment What is the study measuring? Primary Outcome Measures Outcome Measure Measure Description Time Frame 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. After performing the transthoracic echocardiography (TTE) in Visit 3 (7 days after screening) Secondary Outcome Measures Outcome Measure Measure Description Time Frame 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. After performing the transthoracic echocardiography (TTE) in Visit 3 (7 days after screening) 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. Six months after the ECG was performed Collaborators and Investigators This is where you will find people and organizations involved with this study. 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) Study Record Dates These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website. Study Registration Dates First Submitted First Submitted that Met QC Criteria First Posted (Estimated) Study Record Updates Last Update Submitted that met QC Criteria Last Update Posted (Estimated) Last Verified More Information