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

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 su...

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Veröffentlicht in:Journal of electrocardiology 2024-09, Vol.86, p.153768, Article 153768
Hauptverfasser: Domingo-Gardeta, Tomás, Montero-Cabezas, José M., Jurado-Román, Alfonso, Sabaté, Manel, Aboal, Jaime, Baranchuk, Adrián, Carrillo, Xavier, García-Zamora, Sebastián, Dores, Hélder, van der Valk, Viktor, Scherptong, Roderick W.C., Andrés-Cordón, Joan F., Vidal, Pablo, Moreno-Martínez, Daniel, Toribio-Fernández, Raquel, Lillo-Castellano, José María, Cruz, Roberto, De Guio, François, Marina-Breysse, Manuel, Martínez-Sellés, Manuel
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container_title Journal of electrocardiology
container_volume 86
creator Domingo-Gardeta, Tomás
Montero-Cabezas, José M.
Jurado-Román, Alfonso
Sabaté, Manel
Aboal, Jaime
Baranchuk, Adrián
Carrillo, Xavier
García-Zamora, Sebastián
Dores, Hélder
van der Valk, Viktor
Scherptong, Roderick W.C.
Andrés-Cordón, Joan F.
Vidal, Pablo
Moreno-Martínez, Daniel
Toribio-Fernández, Raquel
Lillo-Castellano, José María
Cruz, Roberto
De Guio, François
Marina-Breysse, Manuel
Martínez-Sellés, Manuel
description 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. 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. 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. •ECG is the first step to diagnose myocardial infarction in emergency setting.•ST elevation activates emergent reperfusion but might be absent in coronary artery occlusion.•AI-based ECG interpretation may reduce time delays and enhance diagnostic•accuracy.•The ASSIST project aims at providing key data to optimize such an AI platform.
doi_str_mv 10.1016/j.jelectrocard.2024.153768
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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. 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. ASSIST will provide key data to optimize Willem™ platform to detect myocardial infarction based on ECG-assessment alone. 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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. ASSIST will provide key data to optimize Willem™ platform to detect myocardial infarction based on ECG-assessment alone. 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subjects Acute coronary syndrome
Artificial intelligence
Electrocardiogram
Myocardial infarction
title Rationale and design of the artificial intelligence scalable solution for acute myocardial infarction (ASSIST) study
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