Clinical trial NCT05890716: AI-powered ECG Analysis Using Willem™ Software in High-risk Cardiac Patients (WILLEM)
Official title: Evaluation of Electrocardiographic Data From High-risk Cardiac Patients Using Willem™ Cardiologist-level Artificial Intelligence Software. WILLEM Trial.
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Cardiomyopathies
Cardiac Arrest
Cardiac Arrhythmias
Sudden Cardiac Death
Keywords: Artificial intelligence
Cardiac arrhythmias
Heart disease
Cardiac electrical signals
Electrocardiogram
Electrogram
Lead sponsor: Idoven 1903 S.L.
Sponsor class: INDUSTRY
Collaborators: name: Fundación de Investigación en Red en Enfermedades Cardiovasculares
name: Spanish Society of Cardiology
First posted: 2023-06-06
Enrollment: 5342
Sex: ALL
Minimum age (years): 4
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: RETROSPECTIVE
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.

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. 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.

Detailed 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).

Eligibility criteria: Inclusion Criteria:

* 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.

Exclusion Criteria:

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

Primary outcomes:
measure: Detection of cardiac arrhythmias and cardiac patterns in the electrocardiographic signals
timeFrame: real time to 7 minutes
Secondary outcomes:
measure: Survival at follow-up
timeFrame: 1 year after the first ECG (prospective patients) or after patient enrollment (retrospective patients)
measure: Major Adverse Cardiovascular and Cerebrovascular Events (MACCE)
timeFrame: 1 year after the first ECG (prospective patients) or after patient enrollment (retrospective patients)
measure: Re-hospitalization
timeFrame: 1 year after the first ECG (prospective patients) or after patient enrollment (retrospective patients)
measure: Change in quality of life
timeFrame: 1 year after the first ECG (prospective patients) or after patient enrollment (retrospective patients)
Other outcomes:
Locations:
city: Groningen
country: Netherlands
facility: University Medical Center Groningen
state: Groninga
city: Pamplona
country: Spain
facility: Clínica Universitaria Navarra
state: Navarra
city: Bilbao
country: Spain
facility: Hospital Universitario de Basurto
state: Vizcaya
city: Barcelona
country: Spain
facility: Hospital Sant Joan de Déu
city: Ciudad Real
country: Spain
facility: Hospital General Universitario de Ciudad Real
city: La Coruña
country: Spain
facility: Complejo Hospitalario Universitario A Coruña
city: Madrid
country: Spain
facility: Idoven 1903 S.L.
city: Madrid
country: Spain
facility: Hospital Clínico San Carlos
city: Madrid
country: Spain
facility: Hospital Universitario Puerta de Hierro
city: Madrid
country: Spain
facility: Hospital Universitario General de Villalba
city: Madrid
country: Spain
facility: Hospital Universitario del Henares
city: Murcia
country: Spain
facility: Hospital Virgen de Arrixaca
city: Santa Cruz de Tenerife
country: Spain
facility: Hospital Universitario Nuestra Señora de Candelaria
city: Valencia
country: Spain
facility: Hospital Universitario y Politécnico La Fe
Study officials:
affiliation: Idoven 1903 S.L.
name: María De La Parte, MD
role: PRINCIPAL_INVESTIGATOR
References:
citation: 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.
citation: 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.
citation: 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.
citation: 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.