Herman Robert, Kisova Timea, Belmonte Marta, Wilgenhof Adriaan, Toth Gabor, Demolder Anthony, Rafajdus Adam, Meyers H Pendell, Smith Stephen W, Bartunek Jozef, Barbato Emanuele
Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy.
Cardiovascular Centre Aalst, Aalst, Belgium.
J Soc Cardiovasc Angiogr Interv. 2025 Jan 13;4(3Part B):102494. doi: 10.1016/j.jscai.2024.102494. eCollection 2025 Mar.
The 12-lead electrocardiogram (ECG) is the gold standard for detecting patients who will benefit from emergent revascularization due to occlusive myocardial infarction (OMI). However, the pathophysiology of acute coronary syndromes (ACS) is dynamic, and nearly half of patients with OMI do not present with typical ST elevation or have dynamic ECG changes due to spontaneous recanalization before invasive coronary angiography (ICA). Recently, an ECG-based artificial intelligence (AI) model was developed using expert interpretation of OMI. However, its performance is limited to retrospective evaluation of ECGs recorded minutes to hours before ICA.
The AI-ECG thrombolysis in myocardial infarction (TIMI) study is an investigator-initiated prospective multicenter registry planning to enroll over 700 consecutive patients with ACS undergoing ICA in 9 centers across Europe. For all participants, a standard 10-second 12-lead ECG will be recorded at the time of coronary angiography. The primary end point is the AI model's ability to identify patients with an actively occluded (TIMI 0-1) culprit coronary artery at the time of invasive coronary angiography using only single-standard 12-lead ECGs. Standardized angiograms will be used as a reference standard.
AI-ECG TIMI is the first prospective registry of consecutive patients with ACS with standard 12-lead ECGs recorded at the very moment of ICA. This study will help characterize ECG findings of abnormal myocardial perfusion due to acute active ischemia and prospectively validate an AI model's ability to detect them.
12导联心电图（ECG）是检测因闭塞性心肌梗死（OMI）而将从紧急血运重建中获益的患者的金标准。然而，急性冠状动脉综合征（ACS）的病理生理学是动态变化的，近一半的OMI患者在进行有创冠状动脉造影（ICA）之前未表现出典型的ST段抬高，或因自发再通而出现动态心电图变化。最近，一种基于心电图的人工智能（AI）模型利用对OMI的专家解读得以开发。然而，其性能仅限于对ICA前数分钟至数小时记录的心电图进行回顾性评估。
AI-ECG心肌梗死溶栓（TIMI）研究是一项由研究者发起的前瞻性多中心注册研究，计划在欧洲9个中心连续纳入700多名接受ICA的ACS患者。对于所有参与者，将在冠状动脉造影时记录标准的10秒12导联心电图。主要终点是AI模型仅使用单标准12导联心电图在有创冠状动脉造影时识别出罪犯冠状动脉存在活动性闭塞（TIMI 0-1级）患者的能力。标准化血管造影将用作参考标准。
AI-ECG TIMI是首个对连续ACS患者进行前瞻性注册研究，在ICA当时记录标准12导联心电图。本研究将有助于明确急性活动性缺血导致的心肌灌注异常的心电图表现，并前瞻性地验证AI模型检测这些表现的能力。
