Analisi ECG basata sull'intelligenza artificiale utilizzando il software Willem™ nei pazienti cardiaci ad alto rischio (WILLEM) (WILLEM)
Valutazione dei dati elettrocardiografici di pazienti cardiaci ad alto rischio utilizzando il software di intelligenza artificiale a livello di cardiologo Willem™. Processo WILLEM.
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:
- A new AI-powered ECG analysis platform can automatice the classification and prediction of cardiac arrhythmic episodes at a cardiologist level.
- 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.
Panoramica dello studio
Stato
Intervento / Trattamento
Descrizione dettagliata
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).
Tipo di studio
Iscrizione (Stimato)
Contatti e Sedi
Contatto studio
- Nome: Manuel Marina-Breysse, MSc, MD
- Numero di telefono: +34618103160
- Email: manuel.marina@idoven.ai
Backup dei contatti dello studio
- Nome: José María Lillo, PhD
- Email: c@idoven.ai
Luoghi di studio
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Provincie Groningen
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Groningen, Provincie Groningen, Olanda, 9713 GZ
- Completato
- University Medical Center Groningen
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Barcelona
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Barcelona, Barcelona, Spagna, 08950
- Completato
- Hospital Sant Joan de Déu
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Ciudad Real
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Ciudad Real, Ciudad Real, Spagna, 13005
- Completato
- Hospital General Universitario de Ciudad Real
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La Coruña
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A Coruña, La Coruña, Spagna, 15006
- Completato
- Complejo Hospitalario Universitario A Coruña
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Madrid
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Madrid, Madrid, Spagna, 28822
- Completato
- Hospital Universitario del Henares
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Madrid, Madrid, Spagna, 28400
- Completato
- Hospital Universitario General de Villalba
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Madrid, Madrid, Spagna, 28040
- Completato
- Hospital Clínico San Carlos
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Madrid, Madrid, Spagna, 28222
- Completato
- Hospital Universitario Puerta de Hierro
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Murcia
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Murcia, Murcia, Spagna, 30120
- Completato
- Hospital Virgen de Arrixaca
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Navarre
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Pamplona, Navarre, Spagna, 31008
- Reclutamento
- Clínica Universitaria Navarra
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Contatto:
- Manuel García de Yébenes Castro, MD
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Investigatore principale:
- Manuel García de Yébenes Castro
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Santa Cruz de Tenerife
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Santa Cruz de Tenerife, Santa Cruz de Tenerife, Spagna, 38010
- Completato
- Hospital Universitario Nuestra Señora de Candelaria
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Valencia
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Valencia, Valencia, Spagna, 46026
- Completato
- Hospital Universitario y Politécnico La Fe
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Vizcaya
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Bilbao, Vizcaya, Spagna, 48013
- Reclutamento
- Hospital Universitario de Basurto
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Contatto:
- Alain García Olea, MD
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Investigatore principale:
- Alain García Olea
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Criteri di partecipazione
Criteri di ammissibilità
Età idonea allo studio
Accetta volontari sani
Sessi ammissibili allo studio
Metodo di campionamento
Popolazione di studio
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.
Descrizione
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.
Piano di studio
Come è strutturato lo studio?
Dettagli di progettazione
- Modelli osservazionali: COHORT
- Prospettive temporali: RETROSPECTIVE
Coorti e interventi
Gruppo / Coorte |
Intervento / Trattamento |
|---|---|
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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. |
ECG recording and processing by AI platform |
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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. |
ECG recording and processing by AI platform |
Cosa sta misurando lo studio?
Misure di risultato primarie
Misura del risultato |
Misura Descrizione |
Lasso di tempo |
|---|---|---|
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Detection of cardiac arrhythmias and cardiac patterns in the electrocardiographic signals
Lasso di tempo: real time to 7 minutes
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Willem™ heart rhythm and cardiac pattern performance compared to standard manually performed cardiologist diagnosis. |
real time to 7 minutes
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Misure di risultato secondarie
Misura del risultato |
Misura Descrizione |
Lasso di tempo |
|---|---|---|
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Survival at follow-up
Lasso di tempo: 1 year after the first ECG (prospective patients) or after patient enrollment (retrospective patients)
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Patients alive at the time of follow-up |
1 year after the first ECG (prospective patients) or after patient enrollment (retrospective patients)
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Major Adverse Cardiovascular and Cerebrovascular Events (MACCE)
Lasso di tempo: 1 year after the first ECG (prospective patients) or after patient enrollment (retrospective patients)
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MACCE rates defined as cardiovascular and cerebrovascular events during the follow up |
1 year after the first ECG (prospective patients) or after patient enrollment (retrospective patients)
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Re-hospitalization
Lasso di tempo: 1 year after the first ECG (prospective patients) or after patient enrollment (retrospective patients)
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Number of Re-hospitalizations during the follow up. |
1 year after the first ECG (prospective patients) or after patient enrollment (retrospective patients)
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Change in quality of life
Lasso di tempo: 1 year after the first ECG (prospective patients) or after patient enrollment (retrospective patients)
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European Quality of Life-5 Dimensions (EQ-5D) index an utility scores anchored at 0 for death and 1 for perfect health. |
1 year after the first ECG (prospective patients) or after patient enrollment (retrospective patients)
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Collaboratori e investigatori
Sponsor
Collaboratori
Investigatori
- Investigatore principale: María De La Parte, MD, Idoven 1903 S.L.
Pubblicazioni e link utili
Pubblicazioni generali
- 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.
- 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.
- 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.
- 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.
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- 1903/21
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