Background

Patients with chest pain and persistent ST segment elevation (STE) may not have acute coronary occlusions or serum troponin curves suggestive of acute necrosis. Our objective is the validation and cost-effectiveness analysis of a diagnostic model assisted by artificial intelligence (AI).

Methods

Prospective multicenter registry in two groups of patients with STE: I) coronary arteries without significant lesions and without serum troponin curve suggestive of acute necrosis, II) myocardial infarction with acute coronary occlusion. The inclusion criteria are the following: 1) age ≥ 18 years, 2) chest pain or symptoms suggestive of myocardial ischemia, 3) STE at point J in two contiguous leads ≥0.1 mV, in V2 and V3 ≥ 0,2 mV and 4) signature of informed consent. The exclusion criteria are the following: 1) left bundle branch block, 2) acute cardiac necrosis in the absence of significant epicardial coronary artery stenosis, 3) STE ≤ 0.1 mV with pathologic Q wave, 4) severe anemia (hemoglobin <8.0 g/dl). For each patient without acute cardiac necrosis, the next patient from that center of the same sex and similar age (± 5 years) with myocardial infarction and acute coronary occlusion will be included. A manual centralized electrocardiographic analysis and another by deep learning AI will be performed.

Conclusions

The results of the study will provide new information for the stratification of patients with STE. Our hypothesis is that an AI analysis of the surface electrocardiogram allows a better distinction of patients with STE due to acute myocardial ischemia, from those with another etiology.

中文翻译：

---

与急性心脏坏死无关的 ST 段抬高的合理性和设计 (LESTONNAC)。用于验证由人工智能辅助的深度学习系统的前瞻性注册表

背景

胸痛和持续性 ST 段抬高 (STE) 的患者可能没有提示急性坏死的急性冠状动脉闭塞或血清肌钙蛋白曲线。我们的目标是对人工智能 (AI) 辅助的诊断模型进行验证和成本效益分析。

方法

两组 STE 患者的前瞻性多中心注册：I) 冠状动脉无显着病变且无血清肌钙蛋白曲线提示急性坏死，II) 心肌梗死伴急性冠状动脉闭塞。纳入标准如下：1) 年龄 ≥ 18 岁，2) 胸痛或提示心肌缺血的症状，3) J 点 STE 两条连续导联≥0.1 mV，V2 和 V3 ≥ 0.2 mV 和 4 ) 签署知情同意书。排除标准如下：1) 左束支传导阻滞，2) 急性心脏坏死，但不存在明显的心外膜冠状动脉狭窄，3) STE ≤ 0.1 mV，伴病理性 Q 波，4) 严重贫血（血红蛋白 <8.0 g/ dl)。对于每一位没有急性心脏坏死的患者，来自该中心的下一位患有心肌梗塞和急性冠状动脉闭塞的相同性别和类似年龄（± 5 岁）的患者将被包括在内。将执行手动集中心电图分析和深度学习 AI 的另一个分析。

结论

研究结果将为STE患者的分层提供新的信息。我们的假设是，表面心电图的 AI 分析可以更好地区分急性心肌缺血导致的 STE 患者与其他病因的患者。