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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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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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.</description><identifier>ISSN: 0022-0736</identifier><identifier>ISSN: 1532-8430</identifier><identifier>EISSN: 1532-8430</identifier><identifier>DOI: 10.1016/j.jelectrocard.2024.153768</identifier><identifier>PMID: 39126971</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Acute coronary syndrome ; Artificial intelligence ; Electrocardiogram ; Myocardial infarction</subject><ispartof>Journal of electrocardiology, 2024-09, Vol.86, p.153768, Article 153768</ispartof><rights>2024 Elsevier Inc.</rights><rights>Copyright © 2024 Elsevier Inc. All rights reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c253t-e9b6d41405e7f1b783db82903cebead0696e9666e88279cdffe6b39b0a85f0813</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0022073624002383$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27903,27904,65309</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39126971$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Domingo-Gardeta, Tomás</creatorcontrib><creatorcontrib>Montero-Cabezas, José M.</creatorcontrib><creatorcontrib>Jurado-Román, Alfonso</creatorcontrib><creatorcontrib>Sabaté, Manel</creatorcontrib><creatorcontrib>Aboal, Jaime</creatorcontrib><creatorcontrib>Baranchuk, Adrián</creatorcontrib><creatorcontrib>Carrillo, Xavier</creatorcontrib><creatorcontrib>García-Zamora, Sebastián</creatorcontrib><creatorcontrib>Dores, Hélder</creatorcontrib><creatorcontrib>van der Valk, Viktor</creatorcontrib><creatorcontrib>Scherptong, Roderick W.C.</creatorcontrib><creatorcontrib>Andrés-Cordón, Joan F.</creatorcontrib><creatorcontrib>Vidal, Pablo</creatorcontrib><creatorcontrib>Moreno-Martínez, Daniel</creatorcontrib><creatorcontrib>Toribio-Fernández, Raquel</creatorcontrib><creatorcontrib>Lillo-Castellano, José María</creatorcontrib><creatorcontrib>Cruz, Roberto</creatorcontrib><creatorcontrib>De Guio, François</creatorcontrib><creatorcontrib>Marina-Breysse, Manuel</creatorcontrib><creatorcontrib>Martínez-Sellés, Manuel</creatorcontrib><title>Rationale and design of the artificial intelligence scalable solution for acute myocardial infarction (ASSIST) study</title><title>Journal of electrocardiology</title><addtitle>J Electrocardiol</addtitle><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.</description><subject>Acute coronary syndrome</subject><subject>Artificial intelligence</subject><subject>Electrocardiogram</subject><subject>Myocardial infarction</subject><issn>0022-0736</issn><issn>1532-8430</issn><issn>1532-8430</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNqNkM1PHCEYh4mxqeu2_4IhnvQwWz52GfBmrLYmJk269kwYeFE2s4MC02T_-7KONh49QeD3vB8PQqeULCih4ttmsYEebEnRmuQWjLDlgq54K-QBmtULa-SSk0M0I4SxhrRcHKHjnDeEEMVa9hkdcUWZUC2dofLblBAH0wM2g8MOcngYcPS4PNaXVIIPNpgeh6FA34cHGCzgbE1vuork2I97HPuYsLFjAbzdvUw1Md4k-_J_drle367vz3Euo9t9QZ-86TN8fT3n6M_N9f3Vz-bu14_bq8u7xrIVLw2oTrglXZIVtJ52reSuk0wRbqED44hQApQQAqRkrbLOexAdVx0xcuWJpHyOzqa6Tyk-j5CL3oZs6x5mgDhmzUn1IIWszuboYoraFHNO4PVTCluTdpoSvbeuN_q9db23rifrFT557TN2W3D_0TfNNfB9CkDd9m-ApLMNe5UupFpSuxg-0ucfmCeb2A</recordid><startdate>20240901</startdate><enddate>20240901</enddate><creator>Domingo-Gardeta, Tomás</creator><creator>Montero-Cabezas, José M.</creator><creator>Jurado-Román, Alfonso</creator><creator>Sabaté, Manel</creator><creator>Aboal, Jaime</creator><creator>Baranchuk, Adrián</creator><creator>Carrillo, Xavier</creator><creator>García-Zamora, Sebastián</creator><creator>Dores, Hélder</creator><creator>van der Valk, Viktor</creator><creator>Scherptong, Roderick W.C.</creator><creator>Andrés-Cordón, Joan F.</creator><creator>Vidal, Pablo</creator><creator>Moreno-Martínez, Daniel</creator><creator>Toribio-Fernández, Raquel</creator><creator>Lillo-Castellano, José María</creator><creator>Cruz, Roberto</creator><creator>De Guio, François</creator><creator>Marina-Breysse, Manuel</creator><creator>Martínez-Sellés, Manuel</creator><general>Elsevier Inc</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>20240901</creationdate><title>Rationale and design of the artificial intelligence scalable solution for acute myocardial infarction (ASSIST) study</title><author>Domingo-Gardeta, Tomás ; 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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. 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.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>39126971</pmid><doi>10.1016/j.jelectrocard.2024.153768</doi></addata></record> |
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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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