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

Last updated: February 17, 2025
Sponsor: Idoven 1903 S.L.
Overall Status: Active - Recruiting

Phase

N/A

Condition

Heart Attack (Myocardial Infarction)

Cardiac Disease

Circulation Disorders

Treatment

AI-powered ECG analysis to detect cardiac arrhythmic episodes

Clinical Study ID

NCT05890716
1903/21
  • Ages > 4
  • All Genders
  • Accepts Healthy Volunteers

Study 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. A new AI-powered ECG analysis platform can automatice the classification and prediction of cardiac arrhythmic episodes at a cardiologist level.

  2. This AI-powered ECG analysis can delay or even avoid harmful therapies and severe cardiac adverse events such as sudden death.

The prerequisites for inclusion of patients will be the availability of at least one ECG record in raw data, along with patient clinical data and evolution data after more than 1-year follow-up.

Cardiac electrical signals from multiple medical devices will be collected by cardiology experts after obtaining the informed consent. Every cardiac electrical signal from every subject will be reviewed by a board-certified cardiologist to label the arrhythmias and patterns recorded in those tracings. In order to obtain tracings of relevant information, >95% of the subjects enrolled will have rhythm disorders or abnormal ECG's patterns at the time of enrollment.

Eligibility Criteria

Inclusion

Inclusion Criteria:

  • Patient presenting relevant cardiac arrhythmias and cardiac patterns (includingsupraventricular 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 toguidelines with ≥1 signal-channel.

  • Patient with suspected or diagnosed acute/chronic cardiac diseases (includingpatients with heart failure, patients with history of cardiac arrhythmias, patientswith probable coronary artery diseases, patients with cardiomyopathies, patientswith pacemakers or implantable cardioverter-defibrillators (ICD), patients withindication of pacemaker or ICD in current or short-term phase, patientsparticipating in other interventional clinical investigation, patients withhemodynamic instability or acute coronary syndromes, pregnant patients, patientswith cancer and chemotherapy, patients with life-expectancy lower than 24 months,patients with in or out-of-hospital cardiac arrest with ventricular fibrillation asfirst 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 anauthorized relative.

Exclusion

Exclusion Criteria:

  • Unwillingness or inability to sign study written informed consent.

  • Unavailable or suboptimal quality of the electrocardiographic signal in raw data.

Study Design

Total Participants: 5342
Treatment Group(s): 1
Primary Treatment: AI-powered ECG analysis to detect cardiac arrhythmic episodes
Phase:
Study Start date:
April 04, 2023
Estimated Completion Date:
October 04, 2025

Study Description

The WILLEM study is an investigator-initiated, multicenter, observational trial aiming to validate a cloud-based AI-powered ECG analysis platform to early diagnose and predict the behavior of cardiac abnormalities and cardiac diseases from patients admitted to cardiovascular units. Model-derived diagnosis will be compared with cardiology expert's diagnosis in a test dataset. Clinical outcomes will be included to assess model prediction capabilities: sensitivity, specificity and accuracy. In this observational study, patients will be randomly divided into two groups: (1) a training group to design new methodologies and algorithms; and (2) a test group to evaluate performance of methodologies aiming to avoid overfitting.

Willem™ AI-powered ECG analysis platform supports the analysis of cardiac electrical signals ≥ 10 seconds onwards obtained from devices in-clinic (E.g., 12-lead ECG devices at hospitals or primary care, telemetries, monitors) and at-home or telemedicine interfaces (E.g., Holter devices, event recorders, 6, 3, 2, 1-lead ECG wearables, textile electrodes and patches for mobile cardiac telemetry).

Connect with a study center

  • University Medical Center Groningen

    Groningen, Groninga 9713 GZ
    Netherlands

    Active - Recruiting

  • Hospital Universitario de Basurto

    Bilbao, Vizcaya 48013
    Spain

    Active - Recruiting

  • Hospital Sant Joan de Déu

    Barcelona, 08950
    Spain

    Active - Recruiting

  • Hospital General Universitario de Ciudad Real

    Ciudad Real, 13005
    Spain

    Active - Recruiting

  • Complejo Hospitalario Universitario A Coruña

    La Coruña, 15006
    Spain

    Active - Recruiting

  • Hospital Clínico San Carlos

    Madrid, 28040
    Spain

    Active - Recruiting

  • Hospital Universitario General de Villalba

    Madrid, 28400
    Spain

    Active - Recruiting

  • Hospital Universitario del Henares

    Madrid, 28822
    Spain

    Active - Recruiting

  • Idoven 1903 S.L.

    Madrid, 28002
    Spain

    Active - Recruiting

  • Hospital Universitario Nuestra Señora de Candelaria

    Santa Cruz de Tenerife, 38010
    Spain

    Active - Recruiting

  • Hospital Universitario y Politécnico La Fe

    Valencia, 46026
    Spain

    Active - Recruiting

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