Machine Learning for Diagnosis of Occlusive MI in LBBB Patients

NCT07620119 | Recruiting

• 1 other identifier: 2026/133
• Study Type: observational
• Target: 50
• Locations: 1 country
• Sites: 1

Brief Summary

This study investigates a new way to diagnose severe heart attacks in patients who have a specific electrical heart pattern called a Left Bundle Branch Block (LBBB). When patients present to the emergency department with chest pain, doctors routinely perform an electrocardiogram (ECG) to check for a heart attack. However, the presence of an LBBB can alter the heart's electrical signals on the ECG, effectively masking or hiding the typical signs of an ongoing acute coronary occlusion (a completely blocked artery). This making it highly challenging for emergency physicians to make an accurate and rapid diagnosis. The primary purpose of this prospective and observational research is to develop and evaluate an artificial intelligence/machine learning (ML) model that can analyze digital 12-lead ECG signals to accurately predict a true blocked coronary artery in patients with LBBB. The machine learning model will analyze raw digital ECG waveforms to detect subtle, microscopic patterns that might be missed by the human eye. To confirm the accuracy of the model, its predictions will be compared directly with invasive coronary angiography results, which is the gold standard reference method used to visualize blocked vessels. Additionally, the study aims to evaluate if the model can differentiate between a true heart attack caused by a blocked artery (Type 1 MI) and other non-occlusive conditions that cause elevated heart enzymes (Type 2 MI). Ultimately, the investigators intend to determine whether integrating this machine learning tool into emergency care can safely reduce the rate of unnecessary emergency invasive procedures for patients who do not have a true coronary blockage.

Conditions

Acute Myocardial Infarction (AMI); Left Bundle Branch Block; Coronary Occlusion/Thrombosis; Chest Pain; Myocardial Infarction

Outcome Measures

Primary Outcomes (1)

• Diagnostic Performance for Occlusive Acute Myocardial Infarction

  Evaluation of the developed machine learning model's diagnostic performance in predicting angiographically proven acute coronary occlusion (defined as TIMI 0-1 flow or equivalent true occlusion during catheterization). The primary metrics to evaluate this outcome will include the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC), Sensitivity, Specificity, Positive Predictive Value (PPV), and Negative Predictive Value (NPV).

  Within the emergency department index visit (typically within 24 hours of presentation).

Secondary Outcomes (2)

• Title: Differentiation Performance Between Type 1 MI and Type 2 MI

  Within the hospital stay (up to 7 days).

• Projected Reduction Rate of Unnecessary Angiographies

  Calculated at the study completion

Interventions

Digital 12-Lead ECG Analysis and Invasive Coronary Angiography OTHER

Standard 12-lead digital electrocardiogram (ECG) data recorded during the emergency department index visit will be analyzed using a developed machine learning model. The model's predictions will be compared against the results of standard invasive coronary angiography (the gold standard reference method) performed as part of routine clinical care.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The study population consists of adult patients who present to the emergency department of a major tertiary care referral and research hospital (Konya City Hospital) with clinical symptoms highly suggestive of acute myocardial ischemia (such as chest pain or dyspnea) and whose initial 12-lead electrocardiogram (ECG) demonstrates a Left Bundle Branch Block (LBBB). This population represents a real-world, unselected cohort of emergency patients requiring immediate diagnostic workup and potential emergent or urgent invasive coronary angiography for suspected acute coronary occlusion.

You may qualify if:

• Patients aged 18 years and older who present to the emergency department. Patients presenting with acute ischemic chest pain or clinical ischemia-equivalent symptoms (such as acute dyspnea, unexplained diaphoresis, or syncope).
• Patients with a confirmed Left Bundle Branch Block (LBBB) on their initial 12-lead electrocardiogram (ECG), which can be either newly developed or known/chronic.
• Patients who undergo invasive coronary angiography during their index hospital admission.
• Patients or their legally authorized representatives who provide written informed consent to participate in the study.

You may not qualify if:

• Patients under the age of 18. Pregnant or lactating women. Patients with poor-quality or uninterpretable digital ECG recordings due to severe artifact, missing leads, or technical errors.
• Patients who develop cardiopulmonary arrest before an initial diagnostic 12-lead ECG can be obtained in the emergency department.
• Patients transferred from another healthcare facility who have already undergone coronary angiography or revascularization.
• Patients who decline to participate or refuse to provide written informed consent.

Sponsors & Collaborators

• Konya City Hospital: lead

Study Sites (1)

Konya City Hospital

Konya, Karatay, 42100, Turkey (Türkiye)

RECRUITING

MeSH Terms

Conditions

Bundle-Branch Block; Coronary Occlusion; Thrombosis; Chest Pain; Myocardial Infarction

Condition Hierarchy (Ancestors)

Heart Block; Arrhythmias, Cardiac; Heart Diseases; Cardiovascular Diseases; Cardiac Conduction System Disease; Pathologic Processes; Pathological Conditions, Signs and Symptoms; Coronary Disease; Myocardial Ischemia; Vascular Diseases; Embolism and Thrombosis; Pain; Neurologic Manifestations; Signs and Symptoms; Infarction; Ischemia; Necrosis

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: MD, Emergency Medicine Resident

Study Record Dates

• First Submitted: May 22, 2026
• First Posted: June 2, 2026
• Study Start: June 1, 2026
• Primary Completion (Estimated): December 31, 2026
• Study Completion (Estimated): January 31, 2027
• Last Updated: June 2, 2026

Record last verified: 2026-05

Locations

TU (1)