[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100555393":3},{"organization":4,"armGroups":7,"interventions":19,"overallOfficials":28,"centralContacts":18,"locations":32,"responsibleParty":54,"collaborators":57,"id":61,"slug":62,"hasResults":63,"nctId":64,"briefTitle":65,"officialTitle":66,"acronym":67,"eligibilityCriteria":68,"healthyVolunteers":69,"sex":70,"minAge":71,"maxAge":18,"enrollmentInfo":72,"targetDuration":18,"studyType":75,"phases":76,"briefSummary":78,"conditions":79,"keywords":84,"overallStatus":34,"whyStopped":18,"lastUpdateSubmitDate":90,"lastUpdatePostDateStruct":91,"startDateStruct":94,"completionDateStruct":96,"leadSponsor":98,"locationsCount":99},{"fullName":5,"class":6},"Northwestern University","OTHER",[8,14],{"label":9,"type":10,"description":11,"interventionNames":12},"Intervention","EXPERIMENTAL","Care teams randomized to the intervention will have access to the AI-enabled ECG-based screening tool.",[13],"Device: Risk-Based Assessment for Cardiac Dysfunction",{"label":15,"type":16,"description":17,"interventionNames":18},"Control","NO_INTERVENTION","Care teams randomized to control will continue routine practice without access to the AI-enabled ECG-based screening tool.",null,[20],{"type":21,"name":22,"description":23,"armGroupLabels":24,"otherNames":25},"DEVICE","Risk-Based Assessment for Cardiac Dysfunction","The AI-enabled ECG-based screening tool analyzes 12-lead ECG recordings to identify patients at increased risk for undiagnosed cardiovascular diseases, specifically atrial fibrillation (AF) and structural heart disease (SHD). Clinicians in the intervention group will receive a risk assessment for AF and SHD each time they order an ECG for their patients.",[9],[26,27],"rECHOmmend","ECG-AF",[29],{"name":30,"affiliation":5,"role":31},"Sanjiv Shah, MD","PRINCIPAL_INVESTIGATOR",[33],{"facility":5,"status":34,"city":35,"state":36,"zip":37,"country":38,"countryCode":39,"cosmosGeoPoint":40,"geoPoint":45,"contacts":46},"RECRUITING","Chicago","Illinois","60611","United States","US",{"type":41,"coordinates":42},"Point",[43,44],-87.65005,41.85003,{"lat":44,"lon":43},[47,51],{"name":30,"role":48,"phone":49,"phoneExt":18,"email":50},"CONTACT","312-498-0894","sanjiv.shah@northwestern.edu",{"name":52,"role":48,"phone":18,"phoneExt":18,"email":53},"Uma Mylavarapu","uma.mylavarapu@northwestern.edu",{"type":31,"investigatorFullName":55,"investigatorTitle":56,"investigatorAffiliation":5,"oldNameTitle":18,"oldOrganization":18},"Sanjiv Shah","Director, Institute for Artificial Intelligence in Medicine - Center for Deep Phenotyping and Precision Therapeutics",[58],{"name":59,"class":60},"Tempus AI","INDUSTRY","100555393","northwestern-tempus-ai-enabled-electrocardiography-notable-trial-100555393",false,"NCT06511505","NOrthwestern Tempus AI-enaBLed Electrocardiography (NOTABLE) Trial","NOrthwestern Tempus AI-enaBLed Electrocardiography (NOTABLE) Trial: A Pragmatic, Real-world Study of an Artificial-intelligence Enabled Electrocardiogram Algorithms to Improve the Diagnosis of Cardiovascular Disease","NOTABLE","Inclusion Criteria:\n\n1. Atrial fibrillation algorithm\n\n   1. Age 65 or over\n   2. ECG obtained as part of routine clinical care\n2. Structural heart disease algorithm\n\n   1. Age 40 or over\n   2. ECG obtained as part of routine clinical care\n\nExclusion Criteria:\n\n1. Atrial fibrillation algorithm\n\n   1. No history of AF\n   2. No permanent pacemaker (PPM) or implantable cardioverter defibrillator (ICD)\n   3. No recent cardiac surgery (within the preceding 30 days)\n2. Structural heart disease algorithm\n\n   1. No history of SHD\n   2. No echocardiogram within the past 1 year",true,"ALL","40 Years",{"count":73,"type":74},1000,"ESTIMATED","INTERVENTIONAL",[77],"NA","The goal of this clinical trial is to determine if a machine learning\u002Fartificial intelligence (AI)-based electrocardiogram (ECG) algorithm (rECHOmmend and ECG-AF) can identify undiagnosed cardiovascular disease in patients. It will also examine the safety and effectiveness of using this AI-based tool in a clinical setting. The main questions it aims to answer are:\n\n1. Can the AI-based ECG algorithm improve the detection of atrial fibrillation and structural heart disease?\n2. How does the use of this algorithm affect clinical decision-making and patient outcomes?\n\nResearchers will compare the outcomes of healthcare providers who receive the AI-based ECG results to those who do not. Participants (healthcare providers) will:\n\nBe randomized into two groups: one that receives AI-based ECG results and one that does not.\n\nIn the intervention group, receive an assessment of their patient's risk of atrial fibrillation or structural heart disease with each ordered ECG.\n\nDecide whether to perform further clinical evaluation based on the AI-generated risk assessment as part of routine clinical care.",[80,81,82,83],"Atrial Fibrillation","Cardiovascular Diseases","Arrhythmia","Valvular Disease",[85,86,87,88,89],"early detection","artificial intelligence","structural heart disease","atrial fibrillation","cardiac diagnostics","2026-08-19",{"date":92,"type":93},"2026-08-20","ACTUAL",{"date":95,"type":93},"2024-09-16",{"date":97,"type":74},"2028-09",{"name":5,"class":6},1]