Rationale and design of the artificial intelligence scalable solution for acute myocardial infarction (ASSIST) study.

Autor: Domingo-Gardeta T; Department of Cardiology, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain; Centro de Investigación Biomédica en Red. Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain; Facultad de Medicina, Universidad Complutense, 28040 Madrid, Spain., Montero-Cabezas JM; Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands., Jurado-Román A; Cardiology Department, La Paz University Hospital, Fundación de Investigación Hospital La Paz, IdiPaz Madrid, Spain., Sabaté M; Centro de Investigación Biomédica en Red. Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain; Institut Clínic Cardiovascular, Hospital Clínic, Universitat de Barcelona, Barcelona, Spain; Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain., Aboal J; Servicio de Cardiología, Hospital Universitario Josep Trueta, Girona, Spain., Baranchuk A; Division of Cardiology, Kingston Health Science Center, Queen's University, Kingston, Ontario, Canada., Carrillo X; Hospital Germans Trias i Pujol, Badalona, Spain., García-Zamora S; Department of Cardiology, Sanatorio Delta, Rosario, Argentina., Dores H; Luz Hospital Lisbon, Lisbon, Portugal; NOVA Medical School, Lisbon, Portugal; CHRC, NOVA Medical School, Lisbon, Portugal., van der Valk V; Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands., Scherptong RWC; Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands., Andrés-Cordón JF; Hospital Germans Trias i Pujol, Badalona, Spain., Vidal P; Institut Clínic Cardiovascular, Hospital Clínic, Universitat de Barcelona, Barcelona, Spain., Moreno-Martínez D; Hospital Germans Trias i Pujol, Badalona, Spain; Research group on innovation, health economics and digital transformation, Germans Trias i Pujol Research Institute., Toribio-Fernández R; Idoven Research, Madrid, Spain., Lillo-Castellano JM; Idoven Research, Madrid, Spain; Centro Nacional de Investigaciones Cardiovasculares (CNIC), Myocardial Pathophysiology Area, Madrid, Spain., Cruz R; Idoven Research, Madrid, Spain., De Guio F; Idoven Research, Madrid, Spain., Marina-Breysse M; Centro de Investigación Biomédica en Red. Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain; Idoven Research, Madrid, Spain; Centro Nacional de Investigaciones Cardiovasculares (CNIC), Myocardial Pathophysiology Area, Madrid, Spain., Martínez-Sellés M; Department of Cardiology, Hospital General Universitario Gregorio Marañón, Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain; Centro de Investigación Biomédica en Red. Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain; Facultad de Medicina, Universidad Complutense, 28040 Madrid, Spain; Facultad de Ciencias de la Salud, Universidad Europea, Villaviciosa de Odón, 28670 Madrid, Spain. Electronic address: mmselles@secardiologia.es.
Jazyk: angličtina
Zdroj: Journal of electrocardiology [J Electrocardiol] 2024 Sep-Oct; Vol. 86, pp. 153768. Date of Electronic Publication: 2024 Aug 05.
DOI: 10.1016/j.jelectrocard.2024.153768
Abstrakt: Background: Acute coronary syndrome (ACS), specifically ST-segment elevation myocardial infarction is a major cause of morbidity and mortality throughout Europe. Diagnosis in the acute setting is mainly based on clinical symptoms and physician's interpretation of an electrocardiogram (ECG), which may be subject to errors. ST-segment elevation is the leading criteria to activate urgent reperfusion therapy, but a clear ST-elevation pattern might not be present in patients with coronary occlusion and ST-segment elevation might be seen in patients with normal coronary arteries.
Methods: The ASSIST project is a retrospective observational study aiming to improve the ECG-assisted assessment of ACS patients in the acute setting by incorporating an artificial intelligence platform, Willem™ to analyze 12‑lead ECGs. Our aim is to improve diagnostic accuracy and reduce treatment delays. ECG and clinical data collected during this study will enable the optimization and validation of Willem™. A retrospective multicenter study will collect ECG, clinical, and coronary angiography data from 10,309 patients. The primary outcome is the performance of this tool in the correct identification of acute myocardial infarction with coronary artery occlusion. Model performance will be evaluated internally with patients recruited in this retrospective study while external validation will be performed in a second stage.
Conclusion: ASSIST will provide key data to optimize Willem™ platform to detect myocardial infarction based on ECG-assessment alone. Our hypothesis is that such a diagnostic approach may reduce time delays, enhance diagnostic accuracy, and improve clinical outcomes.
(Copyright © 2024 Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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