AoS-SEDAI study is an observational, multicenter, retrospective and prospective clinical study.
This study aims to assess Willem Artificial Intelligence (AI) ability to distinguish between aortic stenosis (AS) and non-AS patients from 12-lead electrocardiogram (ECG) data.
Age ≥ 18 years;
All available 12-lead ECG with a 10 seconds minimum length on raw data digital format will be included
Available clinical data corresponding to each ECG to confirm patient demographics and Aortic Stenosis diagnosis
Available transthoracic echocardiogram (TTE) within +/- 90 days of each ECG recording
None
The target population consists of patients under assessment of Aortic Stenosis (AS). Screened patients need to have 12-lead ECG recordings within 90 days of a transthoracic echocardiogram (TTE). In the case of multiple ECGs and TTEs per patient, all of them will be selected and analysed accordingly considering the corresponding PI diagnosis at each time. In the event of a patient with a different diagnosis over time (e.g. non-AS confirmed diagnosis later on diagnosed with AS), multiple timepoint ECGs will be categorized so that the same patient is not included both in the control and in the AS populations. Controls will include non-AS patients with relevant TTE and clinical findings during AS differential diagnosis (such as hypertensive heart disease, structural heart diseases, hypertrophic cardiomyopathy among other cardiomyopathies, mild aortic stenosis, and no significant aortic leaflet calcification).
Case-control
Other
Willem AI ECG assessment
DeviceThere is no study intervention. The Willem AI platform will assess all study electrocardiograms (ECGs) for the identification of aortic stenosis. 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.
Willem performance to detect severe Aortic Stenosis
Performance of Willem AI platform to distinguish between severe Aortic Stenosis (AS) patients with confirmed diagnosis and non-AS patients, by means of the following performance metrics: Area Under the Receiver Operating Characteristic curve (AUROC), diagnostic accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). The Standard Of Care (SOC) investigator diagnosis will be used as the ground truth.
Willem performance to detect moderate Aortic Stenosis
Performance of Willem AI platform to distinguish between moderate Aortic Stenosis (AS) patients with confirmed diagnosis and non-AS patients, by means of the following performance metrics: Area Under the Receiver Operating Characteristic curve (AUROC), diagnostic accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). The Standard Of Care (SOC) investigator diagnosis will be used as the ground truth.
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