AI-powered ECG Analysis Using Willem™ Software in High-risk Cardiac Patients (WILLEM)

NCT05890716 | Recruiting

• 1 other identifier: 1903/21
• Study Type: observational
• Target: 5,342
• Locations: 2 countries
• Sites: 13

Brief Summary

WILLEM is a multi-center, prospective and retrospective cohort study. The study will assess the performance of a cloud-based and AI-powered ECG analysis platform, named Willem™, developed to detect arrhythmias and other abnormal cardiac patterns. The main questions it aims to answer are:

1. 1.A new AI-powered ECG analysis platform can automatice the classification and prediction of cardiac arrhythmic episodes at a cardiologist level.
2. 2.This AI-powered ECG analysis can delay or even avoid harmful therapies and severe cardiac adverse events such as sudden death.

Conditions

Cardiomyopathies; Cardiac Arrest; Cardiac Arrhythmias; Sudden Cardiac Death

Keywords

Artificial intelligence; Cardiac arrhythmias; Heart disease; Cardiac electrical signals; Electrocardiogram; Electrogram

Outcome Measures

Primary Outcomes (1)

• Detection of cardiac arrhythmias and cardiac patterns in the electrocardiographic signals

  Willem™ heart rhythm and cardiac pattern performance compared to standard manually performed cardiologist diagnosis.

  real time to 7 minutes

Secondary Outcomes (4)

• Survival at follow-up

  1 year after the first ECG (prospective patients) or after patient enrollment (retrospective patients)

• Major Adverse Cardiovascular and Cerebrovascular Events (MACCE)

  1 year after the first ECG (prospective patients) or after patient enrollment (retrospective patients)

• Re-hospitalization

  1 year after the first ECG (prospective patients) or after patient enrollment (retrospective patients)

• Change in quality of life

  1 year after the first ECG (prospective patients) or after patient enrollment (retrospective patients)

Study Arms (2)

Train group

Consecutive patients admitted to the hospital due to cardiac disorders (retrospective and prospective) with at least one relevant ECG record \>10 sec in raw data will be used to design new methodologies and algorithms for cardiac patterns recognition.

Diagnostic Test: AI-powered ECG analysis to detect cardiac arrhythmic episodes

Test group

Consecutive patients admitted to the hospital due to cardiac disorders (retrospective and prospective) with at least one relevant ECG record \>10 sec in raw data will be used to evaluate performance of methodologies aiming to avoid overfitting. Every 10 patients included in Train group; a new patient is included in the test group.

Diagnostic Test: AI-powered ECG analysis to detect cardiac arrhythmic episodes

Interventions

AI-powered ECG analysis to detect cardiac arrhythmic episodes DIAGNOSTIC_TEST

ECG recording and processing by AI platform

Test group; Train group

Eligibility Criteria

• Age: 4 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients recorded with a mid to long-term ECG device according to guidelines. ECG data (ECG must have been recorded according to the technical standards for the safety and essential performance of medical electrical equipment defined in EN 60601-2-47:2015.): 12-lead ECGs including rest electrocardiograms, stress ECG Test (exercise Electrocardiogram or treadmill test), Holter devices, long-duration Holter devices, event recorders, insertable cardiac monitors, 6,3,2,1-lead ECG wearables, textile electrodes and patches, smartwatches, cardiac monitors, cardiac telemetries, hemodynamic and electrophysiology recording system (i.e., polygraphs), automatic external defibrillator (AED), semi-automatic defibrillator (DESA), home telemonitoring systems and other similar devices.

You may qualify if:

• Patient presenting relevant cardiac arrhythmias and cardiac patterns (including supraventricular tachycardias, abnormal ECG patterns, ventricular tachycardias, ventricular fibrillation, pulseless electrical activity or asystole among others) that have been recorded with at least one short-term ECG medical device according to guidelines with ≥1 signal-channel.
• Patient with suspected or diagnosed acute/chronic cardiac diseases (including patients with heart failure, patients with history of cardiac arrhythmias, patients with probable coronary artery diseases, patients with cardiomyopathies, patients with pacemakers or implantable cardioverter-defibrillators (ICD), patients with indication of pacemaker or ICD in current or short-term phase, patients participating in other interventional clinical investigation, patients with hemodynamic instability or acute coronary syndromes, pregnant patients, patients with cancer and chemotherapy, patients with life-expectancy lower than 24 months, patients with in or out-of-hospital cardiac arrest with ventricular fibrillation as first documented rhythm).
• At least one ECG tracing that can be exported in raw data.
• Signed informed consent. Patients unable to consent, it will be requested to an authorized relative.

You may not qualify if:

• Unwillingness or inability to sign study written informed consent.
• Unavailable or suboptimal quality of the electrocardiographic signal in raw data.

Sponsors & Collaborators

• Idoven 1903 S.L.: lead
• Spanish Society of Cardiology: collaborator
• Fundación de Investigación en Red en Enfermedades Cardiovasculares: collaborator

Study Sites (13)

University Medical Center Groningen

Groningen, Provincie Groningen, 9713 GZ, Netherlands

COMPLETED

Hospital Sant Joan de Déu

Barcelona, Barcelona, 08950, Spain

COMPLETED

Hospital General Universitario de Ciudad Real

Ciudad Real, Ciudad Real, 13005, Spain

COMPLETED

Complejo Hospitalario Universitario A Coruña

A Coruña, La Coruña, 15006, Spain

COMPLETED

Hospital Clínico San Carlos

Madrid, Madrid, 28040, Spain

COMPLETED

Hospital Universitario Puerta de Hierro

Madrid, Madrid, 28222, Spain

COMPLETED

Hospital Universitario General de Villalba

Madrid, Madrid, 28400, Spain

COMPLETED

Hospital Universitario del Henares

Madrid, Madrid, 28822, Spain

COMPLETED

Hospital Virgen de Arrixaca

Murcia, Murcia, 30120, Spain

COMPLETED

Clínica Universitaria Navarra

Pamplona, Navarre, 31008, Spain

RECRUITING

Hospital Universitario Nuestra Señora de Candelaria

Santa Cruz de Tenerife, Santa Cruz de Tenerife, 38010, Spain

COMPLETED

Hospital Universitario y Politécnico La Fe

Valencia, Valencia, 46026, Spain

COMPLETED

Hospital Universitario de Basurto

Bilbao, Vizcaya, 48013, Spain

RECRUITING

Related Publications (4)

• Lillo-Castellano JM, Marina-Breysse M, Gomez-Gallanti A, Martinez-Ferrer JB, Alzueta J, Perez-Alvarez L, Alberola A, Fernandez-Lozano I, Rodriguez A, Porro R, Anguera I, Fontenla A, Gonzalez-Ferrer JJ, Canadas-Godoy V, Perez-Castellano N, Garofalo D, Salvador-Montanes O, Calvo CJ, Quintanilla JG, Peinado R, Mora-Jimenez I, Perez-Villacastin J, Rojo-Alvarez JL, Filgueiras-Rama D. Safety threshold of R-wave amplitudes in patients with implantable cardioverter defibrillator. Heart. 2016 Oct 15;102(20):1662-70. doi: 10.1136/heartjnl-2016-309295. Epub 2016 Jun 13.

  PMID: 27296239 BACKGROUND

• Lillo-Castellano JM, Gonzalez-Ferrer JJ, Marina-Breysse M, Martinez-Ferrer JB, Perez-Alvarez L, Alzueta J, Martinez JG, Rodriguez A, Rodriguez-Perez JC, Anguera I, Vinolas X, Garcia-Alberola A, Quintanilla JG, Alfonso-Almazan JM, Garcia J, Borrego L, Canadas-Godoy V, Perez-Castellano N, Perez-Villacastin J, Jimenez-Diaz J, Jalife J, Filgueiras-Rama D. Personalized monitoring of electrical remodelling during atrial fibrillation progression via remote transmissions from implantable devices. Europace. 2020 May 1;22(5):704-715. doi: 10.1093/europace/euz331.

  PMID: 31840163 BACKGROUND

• Quartieri F, Marina-Breysse M, Pollastrelli A, Paini I, Lizcano C, Lillo-Castellano JM, Grammatico A. Artificial intelligence augments detection accuracy of cardiac insertable cardiac monitors: Results from a pilot prospective observational study. Cardiovasc Digit Health J. 2022 Aug 4;3(5):201-211. doi: 10.1016/j.cvdhj.2022.07.071. eCollection 2022 Oct.

  PMID: 36310681 BACKGROUND

• Martinez-Selles M, Marina-Breysse M. Current and Future Use of Artificial Intelligence in Electrocardiography. J Cardiovasc Dev Dis. 2023 Apr 17;10(4):175. doi: 10.3390/jcdd10040175.

  PMID: 37103054 BACKGROUND

MeSH Terms

Conditions

Cardiomyopathies; Heart Arrest; Arrhythmias, Cardiac; Death, Sudden, Cardiac; Heart Diseases

Condition Hierarchy (Ancestors)

Cardiovascular Diseases; Pathologic Processes; Pathological Conditions, Signs and Symptoms; Death, Sudden; Death

Study Officials

• María De La Parte, MD

  Idoven 1903 S.L.

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Manuel Marina-Breysse, MSc, MD

CONTACT

+34618103160; manuel.marina@idoven.ai

José María Lillo, PhD

CONTACT

c@idoven.ai

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: INDUSTRY
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: May 26, 2023
• First Posted: June 6, 2023
• Study Start: April 4, 2023
• Primary Completion (Estimated): November 1, 2026
• Study Completion (Estimated): November 1, 2026
• Last Updated: July 20, 2026

Record last verified: 2026-07

Data Sharing

• IPD Sharing: Will not share

Locations

NL (1); ES (12)