Artificial Intelligence Based Timing, Infarct Size and Outcomes in Acute Coronary Occlusion Myocardial Infarction

Overview

The present study is practice-driven and merely observational and prospective. In clinical routine, patients who suffer from suspected ACS and do not show ST elevation in the ECG, different timing proposals in the guidelines and logistically driven differences lead to considerably variable timings in invasive coronary anatomy assessments. This handling may lead to larger infarct sizes when OMI is overseen. Therefore, the present study aims to observe a) whether an AI model is capable of correctly identify OMI in eligible patients and b) if in these patients troponin peak levels vary depending on the elapsed time between OMI diagnosis and coronary intervention. As the model has not been established yet clinically and in the guidelines, it is safe to assume the usual pathway from first medical contact to specialist's attention is undertaken. When a patient presents in an emergency department or places an emergency call, the physicians assess the situation as usal and as stated in the current guidelines1. If no STEMI is confirmed, the NSTE-ACS protocol is started. The patients who are ruled out for ACS are excluded from the final analysis (screening). In this case, the AI model is tested on their ECG in order to assess whether there are false positives. The patients which are in the ACS "rule-in" trail and undergo final coronary angiography will naturally be divided in patients which were classified as OMI and as non-OMI by the AI model. Furthermore, they will present a different "Time from OMI diagnosis to PCI) and variable troponin peak levels. By leveraging this natural variability, a practical distinction and multiple analyses can be done: 1. The feasibility of AI-powered ECG interpretation in the care of patients with suspected ACS and without clear ST-elevation infarction 2. The accuracy of AI-powered ECG interpretation in detecting OMI compared to the classical STEMI criteria 3. How infarct size correlates with different ECG readings by AI and (hypothesis generating) if changing the clinical practice could lead to a benefit in patients with suspected OMI.

The 12-lead electrocardiogram (ECG) is the most widely used initial diagnostic tool to guide the management of patients with suspected acute coronary syndrome (ACS). At present, ACS is divided into ST-segment elevation myocardial infarction (STEMI) and non-ST-segment elevation acute coronary syndrome (NSTE-ACS), with different treatment protocols. However, some patients with NSTE-ACS have an acute coronary occlusion (OMI) and may benefit from immediate reperfusion by percutaneous coronary intervention (PCI), but are often treated late. ECG signs suggestive of OMI have been described, but their visual interpretation by experts is variable and suboptimal. Recent studies have shown that artificial intelligence (AI) models for ECG analysis can outperform clinicians in the detection of OMI, suggesting the potential use of AI to improve triage and timely access to PCI. The investigators therefore aim to use these models to analyze ECGs of Patients with NSTE-ACS and to check whether the model outputs OMI or not OMI. Based on that information, the investigators will analyze the time it had taken from admission to intervention (PCI), in order to correlate possible late reperfusions with infarct size of the ventricle. The hypothesis is that a occluded coronary artery will in fact produce a larger infarct size (scar) in the ventricle after longer occlusion times (=reperfusion time), therefore the patients will be dichotomized in early and late intervention patients and analyzed based on their infarct size and outcome, stratified by the OMI diagnosis made by the AI ECG algorithm.