AI-powered ECG Analysis Using Willem™ Software in High-risk Cardiac Patients (WILLEM)
Study Details
Study Description
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:
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A new AI-powered ECG analysis platform can automatice the classification and prediction of cardiac arrhythmic episodes at a cardiologist level.
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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.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
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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).
Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| 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
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. |
Diagnostic Test: AI-powered ECG analysis to detect cardiac arrhythmic episodes
ECG recording and processing by AI platform
|
Outcome Measures
Primary Outcome Measures
- Detection of cardiac arrhythmias and cardiac patterns in the electrocardiographic signals [real time to 7 minutes]
Willem™ heart rhythm and cardiac pattern performance compared to standard manually performed cardiologist diagnosis.
Secondary Outcome Measures
- Survival at follow-up [1 year after the first ECG (prospective patients) or after patient enrollment (retrospective patients)]
Patients alive at the time of follow-up
- Major Adverse Cardiovascular and Cerebrovascular Events (MACCE) [1 year after the first ECG (prospective patients) or after patient enrollment (retrospective patients)]
MACCE rates defined as cardiovascular and cerebrovascular events during the follow up
- Re-hospitalization [1 year after the first ECG (prospective patients) or after patient enrollment (retrospective patients)]
Number of Re-hospitalizations during the follow up.
- Change in quality of life [1 year after the first ECG (prospective patients) or after patient enrollment (retrospective patients)]
European Quality of Life-5 Dimensions (EQ-5D) index an utility scores anchored at 0 for death and 1 for perfect health.
Eligibility Criteria
Criteria
Inclusion Criteria:
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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.
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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).
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At least one ECG tracing that can be exported in raw data.
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Signed informed consent. Patients unable to consent, it will be requested to an authorized relative.
Exclusion Criteria:
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Unwillingness or inability to sign study written informed consent.
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Unavailable or suboptimal quality of the electrocardiographic signal in raw data.
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | Hospital General Universitario de Ciudad Real | Ciudad Real | Spain | 13005 | |
| 2 | Idoven 1903 S.L. | Madrid | Spain | 28002 | |
| 3 | Hospital Clínico San Carlos | Madrid | Spain | 28040 | |
| 4 | Hospital Universitario General de Villalba | Madrid | Spain | 28400 | |
| 5 | Hospital Universitario del Henares | Madrid | Spain | 28822 |
Sponsors and Collaborators
- Idoven 1903 S.L.
- Fundación de Investigación en Red en Enfermedades Cardiovasculares
- Spanish Society of Cardiology
Investigators
- Principal Investigator: María De La Parte, MD, Idoven 1903 S.L.
Study Documents (Full-Text)
None provided.More Information
Publications
- 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.
- 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.
- 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.
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