High-risk Cardiac Patients
Conditions
Keywords
artificial intelligence, electrocardiogram, deep learning, cardiac disease, registry
Brief summary
The WILLEM Registry is a large-scale, single-group, observational, registry study to collect continuous clinical evidence of Willem in real-world settings. Cardiovascular diseases are a major problem for public health and healthcare systems. Electrocardiograms (ECGs) are simple tests which increase diagnostic performance and early detection of cardiovascular diseases. However, its interpretation is complex, time consuming for cardiology experts, and entails high costs for healthcare systems. Willem allows AI-based automatic interpretation and its performance has been examined in previous clinical trials, but additional clinical evidence is needed for its integration in real-world clinical settings. This study will collect clinical evidence of Willem performance to detect cardiac abnormalities in ECGs from high-risk cardiac patients admitted to cardiovascular units.
Detailed description
Patient enrollment will be both retrospective and prospective.
Interventions
DEVICEWillem AI ECG assessment
There is no study intervention. The Willem AI platform will assess all study ECGs for the identification of cardiac patterns, arrhythmias, and/or cardiac diseases. Regardless of retrospective or prospective enrollment, Willem output will not be provided to the healthcare professional user for clinical evaluation, and therefore routine practice will not be impacted nor altered.
Sponsors
Idoven 1903 S.L.
Study design
Observational model
CASE_CONTROL
Time perspective
OTHER
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
* EC/IRB approval of ICF waiver prior to recruitment; otherwise, signed informed consent form by subject and investigator * Age \> 18 years-old, with no upper limit * Subjects undergoing standard of care electrocardiogram (ECG) of any duration from any hardware device * All available, but at least one, legible ECG tracings in raw data format (e.g. DICOM, XML, EDF, JSON, HL7, SCP, WFDB, CSV, etc.) * Available subject clinical data associated with the ECG * For 12-lead ECGs, a minimum length of 10 seconds at a minimum sample frequency of 250 Hz * For ECGs from Holters, wearables, patches, insertable cardiac monitors, telemetries, etc., a minimum length of 30 seconds at a minimum sample frequency of 200 Hz with a lead I / II or its MCL-DII lead approximation * For prospective eligibility only: * Signed informed consent form, unless previously waived by the EC/IRB * Site technical viability for ECG and subject clinical data transfer (e.g. end-to-end integration following interoperability standards such as FHIR, HL7 or DICOM)
Exclusion criteria
* Unavailable or suboptimal quality of the raw data from the ECG signal * Age \< 18 years-old
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Primary endpoint analysis: Willem performance | From enrollment to any standard of care timepoint when the patient underwent (retrospective) or will undergo within the next 10 years (prospective) an eligible electrocardiogram | ECG data will be categorized according to SOC-defined cardiopathies, arrhythmic events, and cardiac diseases. If SOC diagnosis is unavailable or inconsistent, an independent committee of expert cardiologists will review and provide their diagnosis according to a cardiac defined ontology which extends values defined in HL7-aECG data store. Then, the performance of Willem to detect cardiac patterns, arrhythmias, and cardiac disease from ECGs will be assessed. In order to define True Positive, True Negative, False Positive, and False Negative classifications, the ground truth for comparison will be Standard Of Care (SOC) manually performed cardiologist diagnosis. Performance metrics such as diagnostic accuracy, sensitivity, specificity, predictive positive value (PPV), negative predictive value (NPV), F1-Score and Area Under the Receiver Operating Characteristic Curve (AUROC) will be obtained. |
Countries
Ecuador, Spain, United States
Contacts
CONTACTManuel Marina-Breysse, MD, PhD
Outcome results
None listed