[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100560669":3},{"organization":4,"armGroups":7,"interventions":14,"overallOfficials":25,"centralContacts":29,"locations":37,"responsibleParty":53,"collaborators":10,"id":56,"slug":57,"hasResults":58,"nctId":59,"briefTitle":60,"officialTitle":61,"acronym":10,"eligibilityCriteria":62,"healthyVolunteers":58,"sex":63,"minAge":64,"maxAge":10,"enrollmentInfo":65,"targetDuration":10,"studyType":68,"phases":10,"briefSummary":69,"conditions":70,"keywords":10,"overallStatus":39,"whyStopped":10,"lastUpdateSubmitDate":73,"lastUpdatePostDateStruct":74,"startDateStruct":77,"completionDateStruct":79,"leadSponsor":81,"locationsCount":82},{"fullName":5,"class":6},"Mayo Clinic","OTHER",[8],{"label":9,"type":10,"description":10,"interventionNames":11},"Patients who are completing an outpatient electrocardiogram (ECG) at the Mayo Clinic.",null,[12,13],"Device: AI-ECG Dashboard","Diagnostic Test: Point of care ultrasound (POCUS)",[15,20],{"type":16,"name":17,"description":18,"armGroupLabels":19,"otherNames":10},"DEVICE","AI-ECG Dashboard","Patients standard of care ECG's will be processed through the AI-ECG Dashboard",[9],{"type":21,"name":22,"description":23,"armGroupLabels":24,"otherNames":10},"DIAGNOSTIC_TEST","Point of care ultrasound (POCUS)","Patients will undergo a ultrasound to confirm diagnosis of atrial stenosis or diastolic dysfunction.",[9],[26],{"name":27,"affiliation":5,"role":28},"Jae Oh, M.D.","PRINCIPAL_INVESTIGATOR",[30,35],{"name":31,"role":32,"phone":33,"phoneExt":10,"email":34},"Brian Rudquist","CONTACT","(507) 538-5146","Rudquist.Brian@mayo.edu",{"name":27,"role":32,"phone":10,"phoneExt":10,"email":36},"oh.jae@mayo.edu",[38],{"facility":5,"status":39,"city":40,"state":41,"zip":42,"country":43,"countryCode":44,"cosmosGeoPoint":45,"geoPoint":50,"contacts":51},"RECRUITING","Rochester","Minnesota","55905","United States","US",{"type":46,"coordinates":47},"Point",[48,49],-92.4699,44.02163,{"lat":49,"lon":48},[52],{"name":27,"role":32,"phone":10,"phoneExt":10,"email":36},{"type":28,"investigatorFullName":54,"investigatorTitle":55,"investigatorAffiliation":5,"oldNameTitle":10,"oldOrganization":10},"Jae K. Oh, M.D.","Principal Investigator","100560669","ai-in-outpatient-practice-for-diagnosing-aortic-stenosis-and-diastolic-dysfunction-100560669",false,"NCT06580158","AI in Outpatient Practice for Diagnosing Aortic Stenosis and Diastolic Dysfunction","The Clinical Utility of Artificial Intelligence-enabled Electrocardiograms in the Outpatient Practice - Diagnosing Aortic Stenosis and Diastolic Dysfunction","Inclusion Criteria:\n\n* ≥ 60 years of age must have a clinical scheduled ECG performed.\n\nExclusion Criteria:\n\n* \\\u003C 59 years of age\n* Is not scheduled for a clinical ECG\n* Unable to provide consent.","ALL","60 Years",{"count":66,"type":67},2000,"ESTIMATED","OBSERVATIONAL","Two recently developed artificial intelligence-enabled electrocardiogram (AI-ECG) models have been developed to detect aortic stenosis (AS) and diastolic dysfunction (DD). AI-ECG for AS has a sensitivity of 78% and specificity of 74%, and AI-ECG for DD has a sensitivity of 83% and specificity of 80%. However, these models have never been prospectively applied to diagnose AS or DD, which may be useful for patients and providers from a diagnostic and prognostic perspective and especially in settings where access to higher- level medical care is limited. In this study, we aim to determine the clinical utility of these AI-ECG models by prospectively applying them to an outpatient cohort and then completing a focused point-of-care ultrasound to evaluate those who are AI-ECG positive for AS and DD.",[71,72],"Aortic Stenosis","Diastolic Dysfunction","2026-03-02",{"date":75,"type":76},"2026-03-04","ACTUAL",{"date":78,"type":76},"2024-11-08",{"date":80,"type":67},"2027-03",{"name":5,"class":6},1]