[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"trial:NCT07333547":3,"trial-entities:NCT07333547":143,"trial-summary:NCT07333547":147},{"id":4,"nct_id":4,"org_study_id":5,"brief_title":6,"official_title":6,"overall_status":7,"completion_date":8,"status_verified_date":9,"last_update_date":10,"start_date":11,"sponsor_name":12,"lead_sponsor_class":13,"has_dmc":14,"brief_summary":15,"detailed_description":16,"conditions":17,"keywords":19,"study_type":25,"primary_purpose":26,"phases":27,"enrollment_info":28,"interventions":31,"primary_outcomes":39,"secondary_outcomes":44,"sex":45,"minimum_age":46,"maximum_age":26,"healthy_volunteers":47,"eligibility_criteria":48,"std_ages":64,"locations":67,"central_contacts":135,"overall_officials":140,"references":141,"see_also_links":142},"NCT07333547","WR_01","Registry Study for the Evaluation of High-risk Cardiac Patients by WILLEM AI-based ECG Platform","RECRUITING","2036-01","2026-07","2026-07-20","2026-02-03","Idoven 1903 S.L.","INDUSTRY",false,"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.","Patient enrollment will be both retrospective and prospective.",[18],"High-risk Cardiac Patients",[20,21,22,23,24],"artificial intelligence","electrocardiogram","deep learning","cardiac disease","registry","OBSERVATIONAL",null,[],{"count":29,"type":30},200000,"ESTIMATED",[32],{"type":33,"name":34,"description":35,"armGroupLabels":36},"DEVICE","Willem 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\u002For 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.",[37,38],"Controls","High-risk cardiac patients",[40],{"measure":41,"description":42,"timeFrame":43},"Primary endpoint analysis: Willem performance","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.\n\nPerformance 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.","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",[],"ALL","18 Years",true,{"inclusion":49,"exclusion":60,"raw_text":63},[50,51,52,53,54,55,56,57,58,59],"EC\u002FIRB 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 \u002F II or its MCL-DII lead approximation","For prospective eligibility only:","Signed informed consent form, unless previously waived by the EC\u002FIRB","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)",[61,62],"Unavailable or suboptimal quality of the raw data from the ECG signal","Age \\\u003C 18 years-old","Inclusion Criteria:\n\n* EC\u002FIRB approval of ICF waiver prior to recruitment; otherwise, signed informed consent form by subject and investigator\n* Age \\> 18 years-old, with no upper limit\n* Subjects undergoing standard of care electrocardiogram (ECG) of any duration from any hardware device\n* All available, but at least one, legible ECG tracings in raw data format (e.g. DICOM, XML, EDF, JSON, HL7, SCP, WFDB, CSV, etc.)\n* Available subject clinical data associated with the ECG\n* For 12-lead ECGs, a minimum length of 10 seconds at a minimum sample frequency of 250 Hz\n* 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 \u002F II or its MCL-DII lead approximation\n* For prospective eligibility only:\n* Signed informed consent form, unless previously waived by the EC\u002FIRB\n* 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)\n\nExclusion Criteria:\n\n* Unavailable or suboptimal quality of the raw data from the ECG signal\n* Age \\\u003C 18 years-old",[65,66],"ADULT","OLDER_ADULT",[68,84,98,112,121],{"facility":69,"status":7,"city":70,"state":71,"zip":72,"country":73,"contacts":74,"geoPoint":81},"Vanderbilt University Medical Center","Nashville","Tennessee","37232","United States",[75,78],{"name":76,"role":77},"Evan Brittain, MD, MSCI","CONTACT",{"name":79,"role":80},"Evan Brittain","PRINCIPAL_INVESTIGATOR",{"lat":82,"lon":83},36.16589,-86.78444,{"facility":85,"status":7,"city":86,"state":87,"zip":88,"country":89,"contacts":90,"geoPoint":95},"CardioHeredia","Guayaquil","Guayas","090101 - 090158","Ecuador",[91,93],{"name":92,"role":77},"Cinthia Madrid",{"name":94,"role":80},"Fausto Heredia",{"lat":96,"lon":97},-2.19616,-79.88621,{"facility":99,"status":100,"city":101,"zip":102,"country":103,"contacts":104,"geoPoint":109},"La Paz University Hospital","NOT_YET_RECRUITING","Madrid","28046","Spain",[105,107],{"name":106,"role":77},"Teresa López Fernández, MD, PhD",{"name":108,"role":80},"Teresa López Fernández",{"lat":110,"lon":111},40.4165,-3.70256,{"facility":113,"status":7,"city":101,"zip":114,"country":103,"contacts":115,"geoPoint":120},"Puerta de Hierro University Hospital","28222",[116,118],{"name":117,"role":77},"Pablo García Pavía, MD, PhD",{"name":119,"role":80},"Pablo García Pavía",{"lat":110,"lon":111},{"facility":122,"status":100,"city":123,"zip":124,"country":103,"contacts":125,"geoPoint":132},"Murcia University","Murcia","30100",[126,128,130],{"name":127,"role":77},"Sergio Manzano Fernández, MD, PhD",{"name":129,"role":80},"Sergio Manzano Fernández",{"name":131,"role":80},"Juan Antonio Gómez Company",{"lat":133,"lon":134},37.98704,-1.13004,[136],{"name":137,"role":77,"phone":138,"email":139},"Manuel Marina-Breysse, MD, PhD","+34669752391","clinical@idoven.ai",[],[],[],{"nct_id":4,"conditions":144,"biomarkers":146},[145],"Heart Disorder",[],{"nct_id":4,"found":47,"summary":148,"prompt_version":158},{"design":149,"status":150,"heading":151,"summary":152,"follow_up":153,"word_count":154,"commitments":155,"compensation":156,"drugs_mentioned":157},"This is a large, observational study involving 200,000 participants. It is a single-group registry study, meaning all participants will be assessed using the WILLEM AI platform.","completed","WILLEM AI-based ECG Platform for High-risk Cardiac Patients","This study is observing how well the WILLEM AI platform can identify heart patterns, irregular heartbeats (arrhythmias), or heart diseases from electrocardiograms (ECGs). ECGs are simple tests that help doctors detect heart problems early. The WILLEM AI platform automatically interprets these ECGs, which could make diagnosis faster and easier. Researchers want to see how well WILLEM performs in real-world situations. You might be able to join if you are over 18 and have had a standard ECG. The study aims to enroll 200,000 people. The study status is currently unclear.","Your ECGs will be assessed from enrollment up to 10 years after your ECG was taken, depending on whether you joined retrospectively or prospectively.",90,"Not specified in the trial record.","Not stated in the trial record.",[],"v2"]