ST-segment Elevation Not Associated With Acute Cardiac Necrosis (LESTONNAC)

Active, not recruiting

• Conditions: STEMI - ST Elevation Myocardial Infarction
• Interventions: Diagnostic Test: AI platform to detect ST elevation in ECG

• Registration Number: NCT05689970
• Lead Sponsor: Idoven 1903 S.L.

Brief Summary

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). 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. This is a prospective multicenter study with 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. A manual centralized electrocardiographic analysis and another by AI algorithms will be performed.

Detailed Description

This is a prospective multicenter study promoted by the Ischemic Heart Disease and Acute Cardiovascular Care Section of the Spanish Society of Cardiology. Following institutional ethical approval, the surface ECG prior to the activation of the Infarction Code, and the ECGs before and after (up to a maximum of 20) the Infarction Code along with other clinical data will be collected across the different enrolled hospitals. Sites will securely transfer the data to a centralized repository for processing. Willem AI platform will automatically analyze the ECGs in parallel to an experienced observer. The results of the study will provide new information for the improvement in the stratification of patients with ST segment elevation.

Study Timeline

• Study Start: 2022-07-25
• Study First Submitted: 2022-12-20
• Study First Submitted QC: 2023-01-09
• Study First Posted: 2023-01-19
• Primary Completion: 2024-01-25
• Status Verified: 2024-03
• Last Update Submitted: 2024-03-04
• Last Update Posted: 2024-03-06
• Study Completion: 2024-07-24

Recruitment & Eligibility

• Status: ACTIVE_NOT_RECRUITING
• Sex: All
• Target Recruitment: 420

Inclusion Criteria

• Age≥18 years.
• Chest pain or symptoms suggestive of myocardial ischemia.
• STE at point J in the12-lead electrocardiogram prior to activation of the infarction code in two contiguous leads ≥0.1 mV, in V2 and V3 ≥0.2 mV.
• Signature of informed consent.

Exclusion Criteria

• Left bundle branch block.
• Acute cardiac necrosis in the absence of significant epicardial coronary artery stenosis >70% (vasospasm, takotsubo stress cardiomyopathy, myocarditis, coronary artery dissection, acute myocardial infarction without obstructive coronary lesions - MINOCA).
• STE≤0.1 mV with pathologic Q wave suggestive of previous chronic infarction.
• Severe anemia (hemoglobin <8.0 g/dl).

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
STE patients	AI platform to detect ST elevation in ECG	Patients with ST segment elevation (STE) and coronary arteries without significant lesions and without a serum troponin curve suggestive of acute necrosis (group without acute myocardial necrosis).
STEMI patients	AI platform to detect ST elevation in ECG	Patients with ST segment elevation with acute occlusion of at least one epicardial coronary artery and TIMI flow 0 or I (group with acute myocardial necrosis of ischemic origin), that meet the definition of myocardial infarction (STEMI) with an acute cardiac necrosis curve verified by measurement of troponin I or troponin T.

Primary Outcome Measures

Name	Time	Method
Clinical validation of a screening model assisted by AI	6 months after the last enrolled patient	The detection performance of acute myocardial ischemia will be evaluated for the AI platform in comparison to standard manual analysis.

Secondary Outcome Measures

Name	Time	Method
Cost-effectiveness analysis of a screening model assisted by AI	1 year after the last enrolled patient	The benefits of the screening model assisted by the AI platform will be evaluated using a hybrid decision tree/ Markov model.

Trial Locations

Locations (7)

Hospital Universitario de Canarias; 🇪🇸 La Laguna, Santa Cruz De Tenerife, Spain

Hospital de Basurto; 🇪🇸 Bilbao, Vizcaya, Spain

Hospital Vall D' Hebron; 🇪🇸 Barcelona, Spain

Idoven; 🇪🇸 Madrid, Spain

Hospital Clínico Universitario de Valladolid; 🇪🇸 Valladolid, Spain

Servicio Cardiología Hospital Universitario Gregorio Marañón; 🇪🇸 Madrid, Spain

Hospital Clínico San Carlos; 🇪🇸 Madrid, Spain