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.
Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands.
J Electrocardiol. 2024 Sep-Oct;86:153768. doi: 10.1016/j.jelectrocard.2024.153768. Epub 2024 Aug 5.
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.
急性冠状动脉综合征（ACS），特别是 ST 段抬高型心肌梗死，是整个欧洲发病率和死亡率的主要原因。急性情况下的诊断主要基于临床症状和医生对心电图（ECG）的解读，这可能存在误差。ST 段抬高是激活紧急再灌注治疗的主要标准，但冠状动脉闭塞的患者可能没有明显的 ST 段抬高模式，而正常冠状动脉的患者可能会出现 ST 段抬高。
ASSIST 项目是一项回顾性观察性研究，旨在通过整合人工智能平台 Willem™来分析 12 导联心电图，从而改善急性情况下 ACS 患者的 ECG 辅助评估。我们的目的是提高诊断准确性并减少治疗延误。该研究将收集心电图、临床和冠状动脉造影数据，共纳入 10309 例患者。主要结局是该工具在正确识别伴有冠状动脉闭塞的急性心肌梗死方面的性能。模型性能将在内部使用该回顾性研究中招募的患者进行评估，而外部验证将在第二阶段进行。
ASSIST 将提供关键数据，以优化 Willem™平台，实现基于心电图评估的心肌梗死检测。我们的假设是，这种诊断方法可以减少时间延迟，提高诊断准确性，并改善临床结局。
