[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100651306":3},{"organization":4,"armGroups":7,"interventions":19,"overallOfficials":25,"centralContacts":30,"locations":39,"responsibleParty":69,"collaborators":73,"id":76,"slug":77,"hasResults":78,"nctId":79,"briefTitle":80,"officialTitle":80,"acronym":18,"eligibilityCriteria":81,"healthyVolunteers":78,"sex":82,"minAge":83,"maxAge":18,"enrollmentInfo":84,"targetDuration":18,"studyType":87,"phases":88,"briefSummary":90,"conditions":91,"keywords":94,"overallStatus":98,"whyStopped":18,"lastUpdateSubmitDate":99,"lastUpdatePostDateStruct":100,"startDateStruct":103,"completionDateStruct":105,"leadSponsor":107,"locationsCount":108},{"fullName":5,"class":6},"Imperial College London","OTHER",[8,14],{"label":9,"type":10,"description":11,"interventionNames":12},"AI-ECG arm","EXPERIMENTAL","Patients in this arm will have AI-ECG undertaken alongside their standard care. For patients with non-cardiac chest pain, the clinician will have access to the AI-ECG prediction result denoting the risk level from the ECG. This risk prediction will contribute to their clinical assessment of the patient in addition to history and examination.",[13],"Diagnostic Test: AI-ECG",{"label":15,"type":16,"description":17,"interventionNames":18},"Standard care","NO_INTERVENTION","Patients will be reviewed in clinic as per best current practice. Decisions on their further care will be undertaken by clinicians as usual.",null,[20],{"type":21,"name":22,"description":23,"armGroupLabels":24,"otherNames":18},"DIAGNOSTIC_TEST","AI-ECG","The AI-ECG will take a digital ECG recording and produce a predictive report for risk of cardiovascular disease and death for each patient.",[9],[26],{"name":27,"affiliation":28,"role":29},"Jamil Mayet, MBChB","Imperial College NHS Healthcare Trust","PRINCIPAL_INVESTIGATOR",[31,36],{"name":32,"role":33,"phone":34,"phoneExt":18,"email":35},"Jamil Mayet, Professor, MBChB","CONTACT","+44 020 7594 1006","j.mayet@imperial.ac.uk",{"name":37,"role":33,"phone":18,"phoneExt":18,"email":38},"Fu Siong Ng, Professor, MBBS","f.ng@imperial.ac.uk",[40,57],{"facility":28,"status":18,"city":41,"state":41,"zip":18,"country":42,"countryCode":43,"cosmosGeoPoint":44,"geoPoint":49,"contacts":50},"London","United Kingdom","UK",{"type":45,"coordinates":46},"Point",[47,48],-0.12574,51.50853,{"lat":48,"lon":47},[51,52,54],{"name":32,"role":33,"phone":34,"phoneExt":18,"email":35},{"name":53,"role":29,"phone":18,"phoneExt":18,"email":18},"Jamil Mayet, Professor",{"name":55,"role":56,"phone":18,"phoneExt":18,"email":18},"Fu Siong Ng, MBBS","SUB_INVESTIGATOR",{"facility":58,"status":18,"city":41,"state":18,"zip":18,"country":42,"countryCode":43,"cosmosGeoPoint":59,"geoPoint":61,"contacts":62},"London North West University Healthcare NHS Trust",{"type":45,"coordinates":60},[47,48],{"lat":48,"lon":47},[63,67],{"name":64,"role":33,"phone":65,"phoneExt":18,"email":66},"Jaymin Shah, Dr, MBBS","+44 020 8869 2552","jaymin.shah@nhs.net",{"name":68,"role":29,"phone":18,"phoneExt":18,"email":18},"Jaymin Shah, MBBS",{"type":70,"investigatorFullName":71,"investigatorTitle":72,"investigatorAffiliation":5,"oldNameTitle":18,"oldOrganization":18},"SPONSOR_INVESTIGATOR","Jamil Mayet","Professor of Cardiology",[74],{"name":75,"class":6},"Imperial College Healthcare NHS Trust","100651306","the-use-of-artificial-intelligence-enhanced-electrocardiograms-in-the-chest-pain-clinic-to-risk-stratify-patients-provide-rapid-reassurance-and-enable-a-low-cost-clinical-pathway-100651306",false,"NCT07757425","The Use of Artificial Intelligence-Enhanced Electrocardiograms in the Chest Pain Clinic to Risk Stratify Patients, Provide Rapid Reassurance and Enable a Low-Cost Clinical Pathway","Inclusion Criteria:\n\n* Our study population will include all patients aged 18 years and older presenting with non-anginal chest pain (chest pain that is not typical to the heart) to 6 rapid access chest pain clinic sites across North West London. Chest pain typicality will be defined using the standardised Rose Angina questionnaire, based on clinical history.\n\nExclusion Criteria:\n\n* Patients with typical cardiac (heart-related) chest pain, as defined by the Rose Angina classification\n* Patients with known moderate or severe stenosis (narrowing) in an epicardial coronary artery (the blood vessels supplying the heart)\n* Known left ventricular impairment (left ventricular ejection fraction \\\u003C50%), otherwise known as heart failure\n* Left bundle branch block (a significant electrical abnormality of the heart on ECG)\n* End-stage kidney failure requiring renal replacement therapy such as dialysis or a kidney transplant\n* Moderate or severe valvular heart disease (serious narrowing or leaking of the heart valves)\n* Paced rhythm at the time of ECG acquisition (lots of patients with pacemakers will fall into this category, but only if their pacemaker is firing at the time the ECG was taken).","ALL","18 Years",{"count":85,"type":86},4000,"ESTIMATED","INTERVENTIONAL",[89],"NA","Our current pathway for investigating patients with chest pain differs depending on if the pain is cardiac sounding or not. National guidelines advise us that patients with non-cardiac chest pain do not need further tests beyond seeing a clinician and having a test called an electrocardiogram (ECG), but often we do unnecessary additional investigations for these patients. Some of the tests we do involve invasive procedures or radiation, which have associated risks. We have recently developed an artificial intelligence (AI) ECG technology, which has been shown in various studies to reliably predict risk of heart disease, including heart attacks and death, from just one AI-ECG reading, which is a test that is painless with no radiation. We have shown that this AI-ECG is more accurate at predicting outcomes than the standard risk prediction models we use now.\n\nWe propose investigating whether this new technology helps to nudge our clinicians to avoid risk averse behaviour so that they undertake fewer unnecessary investigations, by comparing its use to our current treatment pathway.\n\nThe main questions our study aims to answer are:\n\n* Will an AI-ECG assisted chest pain clinic pathway result in lower healthcare resource costs than the standard pathway?\n* Will an AI-ECG assisted chest pain clinic pathway reduce the time from referral to diagnosis and treatment?\n* Will an AI-ECG assisted chest pain clinic pathway perform equally as well as our current pathway in resolving symptoms and preventing future heart disease?\n\nWe will randomly allocate half of the patients with non-cardiac pain in our chest pain clinics to have an AI-ECG, using it to determine which patients are low risk and which are higher risk. Feedback from the analysis will be given to the assessing clinician, with our hypothesis being that patients triaged as low risk by the AI-ECG will be reassured and discharged from clinic, with patients identified as higher risk undergoing further investigation.\n\nThe other half of patients not allocated to receive an additional AI-ECG test will be managed as usual. All patients' clinical assessment and management plans will be assessed by a Consultant Cardiologist, who will not have access to the AI-ECG data so that there is assurance that all assigned management pathways are clinically safe and appropriate. We will compare the cost spent for each group at one year, as well as how quickly we can provide a diagnosis\u002Fmanagement plan to patients, the number of cardiac events and the number of patients prescribed cholesterol and blood pressure lowering medications. We propose that this study will allow us to safely reassure more patients with chest pain more quickly.",[92,93],"Angina (Stable)","Chest Pain Atypical Syndrome",[95,22,96,97],"Artificial intelligence","Risk-stratification","Low-risk chest pain","NOT_YET_RECRUITING","2026-08-06",{"date":101,"type":102},"2026-08-11","ACTUAL",{"date":104,"type":86},"2026-08",{"date":106,"type":86},"2029-01",{"name":71,"class":6},2]