Rational and design of ST-segment elevation not associated with acute cardiac necrosis (LESTONNAC). A prospective registry for validation of a deep learning system assisted by artificial intelligence

Abstract

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.

Keywords: Acute myocardial infarction; Artificial intelligence; Electrocardiogram.