Ignite Creation Date:
2025-12-25 @ 12:43 AM
Ignite Modification Date:
2025-12-25 @ 10:56 PM
Study NCT ID:
NCT07163767
Status:
ACTIVE_NOT_RECRUITING
Last Update Posted:
2025-12-18
First Post:
2025-09-02
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Acute Myocardial Infarction Prediction Using Artificial Intelligence Applied to Electrocardiogram Images
Sponsor:
Organization:
Raw JSON
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Time-Dependent ECG-AI Prediction of Fatal Coronary Heart Disease: A Retrospective Study. J Cardiovasc Dev Dis. 2024 Dec 8;11(12):395. doi: 10.3390/jcdd11120395.'}, {'pmid': '16790531', 'type': 'BACKGROUND', 'citation': 'Sekhri N, Feder GS, Junghans C, Hemingway H, Timmis AD. How effective are rapid access chest pain clinics? Prognosis of incident angina and non-cardiac chest pain in 8762 consecutive patients. Heart. 2007 Apr;93(4):458-63. doi: 10.1136/hrt.2006.090894. Epub 2006 Jun 21.'}, {'pmid': '8704534', 'type': 'BACKGROUND', 'citation': 'Stubbs P, Collinson P, Moseley D, Greenwood T, Noble M. Prospective study of the role of cardiac troponin T in patients admitted with unstable angina. BMJ. 1996 Aug 3;313(7052):262-4. doi: 10.1136/bmj.313.7052.262.'}, {'pmid': '9862943', 'type': 'BACKGROUND', 'citation': 'Farkouh ME, Smars PA, Reeder GS, Zinsmeister AR, Evans RW, Meloy TD, Kopecky SL, Allen M, Allison TG, Gibbons RJ, Gabriel SE. 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No abstract available.'}, {'pmid': '37952131', 'type': 'BACKGROUND', 'citation': 'Lincoff AM, Brown-Frandsen K, Colhoun HM, Deanfield J, Emerson SS, Esbjerg S, Hardt-Lindberg S, Hovingh GK, Kahn SE, Kushner RF, Lingvay I, Oral TK, Michelsen MM, Plutzky J, Tornoe CW, Ryan DH; SELECT Trial Investigators. Semaglutide and Cardiovascular Outcomes in Obesity without Diabetes. N Engl J Med. 2023 Dec 14;389(24):2221-2232. doi: 10.1056/NEJMoa2307563. Epub 2023 Nov 11.'}, {'pmid': '39804243', 'type': 'BACKGROUND', 'citation': 'Dhingra LS, Aminorroaya A, Sangha V, Pedroso AF, Asselbergs FW, Brant LCC, Barreto SM, Ribeiro ALP, Krumholz HM, Oikonomou EK, Khera R. Heart failure risk stratification using artificial intelligence applied to electrocardiogram images: a multinational study. 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N Engl J Med. 2010 Nov 18;363(21):2015-26. doi: 10.1056/NEJMoa1003603. Epub 2010 Aug 28.'}, {'pmid': '38079535', 'type': 'BACKGROUND', 'citation': 'Dupulthys S, Dujardin K, Anne W, Pollet P, Vanhaverbeke M, McAuliffe D, Lammertyn PJ, Berteloot L, Mertens N, De Jaeger P. Single-lead electrocardiogram Artificial Intelligence model with risk factors detects atrial fibrillation during sinus rhythm. Europace. 2024 Feb 1;26(2):euad354. doi: 10.1093/europace/euad354.'}, {'pmid': '39272624', 'type': 'BACKGROUND', 'citation': 'Nechita LC, Nechita A, Voipan AE, Voipan D, Debita M, Fulga A, Fulga I, Musat CL. AI-Enhanced ECG Applications in Cardiology: Comprehensive Insights from the Current Literature with a Focus on COVID-19 and Multiple Cardiovascular Conditions. Diagnostics (Basel). 2024 Aug 23;14(17):1839. doi: 10.3390/diagnostics14171839.'}, {'pmid': '35479951', 'type': 'BACKGROUND', 'citation': 'Chen HY, Lin CS, Fang WH, Lee CC, Ho CL, Wang CH, Lin C. Artificial Intelligence-Enabled Electrocardiogram Predicted Left Ventricle Diameter as an Independent Risk Factor of Long-Term Cardiovascular Outcome in Patients With Normal Ejection Fraction. Front Med (Lausanne). 2022 Apr 11;9:870523. doi: 10.3389/fmed.2022.870523. eCollection 2022.'}, {'pmid': '40139860', 'type': 'BACKGROUND', 'citation': 'Lee HS, Kang S, Jo YY, Son JM, Lee MS, Kwon JM, Kim KH. AI-Enabled Smartwatch ECG: A Feasibility Study for Early Prediction and Prevention of Heart Failure Rehospitalization. JACC Basic Transl Sci. 2025 Mar;10(3):250-252. doi: 10.1016/j.jacbts.2025.01.005. Epub 2025 Feb 11. No abstract available.'}, {'pmid': '34743566', 'type': 'BACKGROUND', 'citation': 'Khurshid S, Friedman S, Reeder C, Di Achille P, Diamant N, Singh P, Harrington LX, Wang X, Al-Alusi MA, Sarma G, Foulkes AS, Ellinor PT, Anderson CD, Ho JE, Philippakis AA, Batra P, Lubitz SA. ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation. Circulation. 2022 Jan 11;145(2):122-133. doi: 10.1161/CIRCULATIONAHA.121.057480. Epub 2021 Nov 8.'}]}, 'descriptionModule': {'briefSummary': "The goal of this observational study is to develop and validate an artificial intelligence(AI)-based prediction model for new-onset acute myocardial infarction(AMI) using electrocardiogram(ECG) data. The main question it aims to answer is whether the AI-based ECG accurately forecast new-onset AMI by previous ECG data with 'normal' diagnosis?", 'detailedDescription': "Myocardial infarction (MI), as one of the most critical acute clinical events in cardiovascular diseases, has high morbidity and mortality, imposing a significant burden on public health and medical resources. Traditional risk assessment for MI relies on clinical indicators (e.g., lipids, blood pressure, diabetes status, family history) and cardiac imaging, but these methods often suffer from invasive procedure, high cost, or limited predictive accuracy. Electrocardiography (ECG), a non-invasive, widely available, and low-cost modality, captures rich electrophysiological signals reflecting cardiac function. However, conventional analysis methods struggle to detect subtle, progressive patterns in ECG signals, leading to sub-optimal sensitivity and specificity in predicting new-onset MI.\n\nRecent advancements in deep learning and big data have enabled significant progress in ECG-based prediction models. For example, deep convolutional neural networks (CNNs) for automatic feature extraction and pattern recognition from 12-lead ECGs have demonstrated promise in predicting cardiovascular events such as atrial fibrillation, left ventricular hypertrophy, and heart failure re-hospitalization. AI algorithms have also been shown to extract subtle information from ECGs that traditional methods miss, such as dynamic changes in ventricular electrical activity and early signs of micro-myocardial injury, enabling early risk warning of cardiac events. While numerous ECG-based AI models exist for predicting arrhythmia , heart failure, and other cardiovascular outcomes, research on predicting new-onset MI-particularly using non-invasive ECG data and deep learning to extract latent predictive markers-remains in its infancy. Traditional risk models, though successful in MI prevention, lack precision in individual-level prediction and early intervention.\n\nThis study aims to leverage large-scale electronic health records and ECG datasets with advanced deep learning to explore the quantitative relationship between fine-grained ECG signal features and MI incidence, thereby developing a clinical tool for early risk assessment. Inspirations also derive from recent attempts to build multi-modal prediction models combining ECG with physiological, genetic, and biochemical markers. Additionally, studies have highlighted ECG's unique advantages in evaluating myocardial compensatory mechanisms and early injury. Despite existing ECG-AI applications, direct prediction of new-onset MI remains a critical unmet need and a key direction for precision medicine using AI.\n\nThis is a multi-center observational cohort study. Large-scale in-hospital ECG data will be integrated to develop a deep learning model for MI prediction using an end-to-end deep neural network approach, with the goal of deriving a high-performance model for new-onset MI prediction. The ECG data from 5 multicenter Cardiorenal ImprovemeNt II (CIN-II) sites between 2010-2023 will be assessed."}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'In-hospital patients with ECG records', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Hospitalized in cardiology department with myocardial injury marker testing (troponin T/I).\n* In-hospital patients with ECG records.\n\nExclusion Criteria:\n\n* First ECG obtained in emergency department.\n* ACS diagnosis within 1 month of first ECG.'}, 'identificationModule': {'nctId': 'NCT07163767', 'briefTitle': 'Acute Myocardial Infarction Prediction Using Artificial Intelligence Applied to Electrocardiogram Images', 'organization': {'class': 'OTHER', 'fullName': "Guangdong Provincial People's Hospital"}, 'officialTitle': 'Acute Myocardial Infarction Prediction Using Artificial Intelligence Applied to Electrocardiogram Images', 'orgStudyIdInfo': {'id': 'KY2025-514'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Cardiorenal ImprovemeNt II (CIN-II)', 'description': 'This is a multi-center, retrospective observation study collecting data on 184855 coronary angiography patients from January 2000 to Decemeber 2020.', 'interventionNames': ['Other: Deep learning approach of ECG for AMI detection']}], 'interventions': [{'name': 'Deep learning approach of ECG for AMI detection', 'type': 'OTHER', 'description': 'AMIdECG was trained to perform AMI detection in a supervised manner as a classification task. And the classification labels of AMI subtypes (" STEMI "or" NSTEMI ") or non-AMI states used during the training phase are real-world diagnostic results', 'armGroupLabels': ['Cardiorenal ImprovemeNt II (CIN-II)']}]}, 'contactsLocationsModule': {'locations': [{'zip': '510080', 'city': 'Guangzhou', 'state': 'Guangdong', 'country': 'China', 'facility': "Guangdong Provincial People's Hospital", 'geoPoint': {'lat': 23.11667, 'lon': 113.25}}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': "Guangdong Provincial People's Hospital", 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Professor', 'investigatorFullName': 'Liu yong', 'investigatorAffiliation': "Guangdong Provincial People's Hospital"}}}}