ArticlePloS one2019
Artificial intelligence to predict needs for urgent revascularization from 12-leads electrocardiography in emergency patients.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05348109 (Assessment of Decision Support System Software in Extraction and Anchorage Planning Among Adult Patients Using Computer Algorithm), which is not on this map. Cited by 39 papers, 1 of them a synthesis that pooled it.
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Assessment of Decision Support System Software in Extraction and Anchorage Planning Among Adult Patients Using Computer Algorithm
Who cites it
39 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Guideline of the Brazilian Society of Cardiology on Telemedicine in Cardiology - 2019.Arquivos brasileiros de cardiologia · 2019Guideline
- Improved Detection of Acute Coronary Occlusion Myocardial Infarction by an Artificial Intelligence Electrocardiogram Model in Swedish Emergency Departments.Journal of the American College of Emergency Physicians open · 2026Article
- Application Progress of Machine Learning in Prognostic Prediction of Percutaneous Coronary Intervention: A Systematic Review.Journal of clinical medicine research · 2026Review
- Channel and Spatial Parallel Attention for ECG-Based Prediction of Concealed Accessory Pathways and Atrioventricular Nodal Reentry Tachycardia.Journal of arrhythmia · 2026Article
- Optimizing single-lead ECG axis for AI-based detection of myocardial diseases.NPJ cardiovascular health · 2026Article
- Impact of Prehospital Administration of Aspirin and Unfractionated Heparin on In-Hospital Outcomes in Patients with Suspected Myocardial Infarction: A Retrospective Cohort Study.Open access emergency medicine : OAEM · 2026Article
- Real-world application of deep learning for ECG-based prediction of coronary artery disease and revascularization needs.European heart journal. Digital health · 2025Article
- Review
- Deep learning-based analysis of 12-lead electrocardiograms in school-age children: a proof of concept study.Frontiers in cardiovascular medicine · 2025Article
- Electrocardiogram Signal Analysis With a Machine Learning Model Predicts the Presence of Pulmonary Embolism With Accuracy Dependent on Embolism Burden.Mayo Clinic proceedings. Digital health · 2024Article
- JCS/JHRS 2022 Guideline on Diagnosis and Risk Assessment of Arrhythmia.Journal of arrhythmia · 2024Article
- Elucidating the nexus between onco-immunology and kidney transplantation: An insight from precision medicine perspective.Heliyon · 2024Review
- International evaluation of an artificial intelligence-powered electrocardiogram model detecting acute coronary occlusion myocardial infarction.European heart journal. Digital health · 2024Article
- Deep learning-mediated prediction of concealed accessory pathway based on sinus rhythmic electrocardiograms.Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc · 2023Article
- Deep learning-based model detects atrial septal defects from electrocardiography: a cross-sectional multicenter hospital-based study.EClinicalMedicine · 2023Article
- Detection of the Relationship between the Multi-Dimensional Data Sets of Serially Measured Blood Pressure and the Future Risk of Death in Healthy Elderly Japanese Population.Journal of atherosclerosis and thrombosis · 2023Article
- Improved prediction of sudden cardiac death in patients with heart failure through digital processing of electrocardiography.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2023Observational
- Transfer learning enables prediction of myocardial injury from continuous single-lead electrocardiography.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- The Future Role of High-Performance Computing in Cardiovascular Medicine and Science -Impact of Multi-Dimensional Data Analysis.Journal of atherosclerosis and thrombosis · 2022Article
- Deep Learning for Detecting and Locating Myocardial Infarction by Electrocardiogram: A Literature Review.Frontiers in cardiovascular medicine · 2022Review
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10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPatient with acute coronary syndrome benefits from early revascularization. However, methods for the selection of patients who require urgent revascularization from a variety of patients visiting the emergency room with chest symptoms is not fully established. Electrocardiogram is an easy and rapid procedure, but may contain crucial information not recognized even by well-trained physicians.
objectiveTo make a prediction model for the needs for urgent revascularization from 12-lead electrocardiogram recorded in the emergency room.
methodWe developed an artificial intelligence model enabling the detection of hidden information from a 12-lead electrocardiogram recorded in the emergency room. Electrocardiograms obtained from consecutive patients visiting the emergency room at Keio University Hospital from January 2012 to April 2018 with chest discomfort was collected. These data were splitted into validation and derivation dataset with no duplication in each dataset. The artificial intelligence model was constructed to select patients who require urgent revascularization within 48 hours. The model was trained with the derivation dataset and tested using the validation dataset.
resultsOf the consecutive 39,619 patients visiting the emergency room with chest discomfort, 362 underwent urgent revascularization. Of them, 249 were included in the derivation dataset and the remaining 113 were included in validation dataset. For the control, 300 were randomly selected as derivation dataset and another 130 patients were randomly selected for validation dataset from the 39,317 who did not undergo urgent revascularization. On validation, our artificial intelligence model had predictive value of the c-statistics 0.88 (95% CI 0.84-0.93) for detecting patients who required urgent revascularization.
conclusionsOur artificial intelligence model provides information to select patients who need urgent revascularization from only 12-leads electrocardiogram in those visiting the emergency room with chest discomfort.
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