Machine Learning for Diagnosis of Occlusive MI in LBBB Patients (AI-LBBB)

Development of a Machine Learning Model for the Diagnosis of Occlusive Myocardial Infarction in the Setting of Left Bundle Branch Block

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.

Study Overview

• Status: Recruiting
• Conditions: Myocardial Infarction; Chest Pain; Acute Myocardial Infarction (AMI); Left Bundle Branch Block; Coronary Occlusion/Thrombosis
• Intervention / Treatment: Other: Digital 12-Lead ECG Analysis and Invasive Coronary Angiography

• Study Type: Observational
• Enrollment (Estimated): 50

Contacts and Locations

Study Locations

• Turkey (Türkiye)
  • Karatay
    • Konya, Karatay, Turkey (Türkiye), 42100
      • Recruiting
      • Konya City Hospital
      • Contact: Ahmet Gumus, MD, Emergency Medicine Residen; Phone Number: +905547957490; Email: ahmetgms88@gmail.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• 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.

Description

Inclusion Criteria:

• 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.

Exclusion Criteria:

• 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.

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
Title: Differentiation Performance Between Type 1 MI and Type 2 MI	Evaluation of the machine learning model's performance (measured by AUC, sensitivity, and specificity) to distinguish between acute coronary occlusion (Type 1 MI) and non-occlusive ischemic myocardial injury or supply-demand mismatch presenting with elevated cardiac troponin (Type 2 MI).	Within the hospital stay (up to 7 days).
Projected Reduction Rate of Unnecessary Angiographies	Simulation and post-hoc analysis to quantify the potential relative reduction in the rate of emergency invasive coronary angiographies among LBBB patients without true coronary occlusion by applying the model's diagnostic probability scores.	Calculated at the study completion

Collaborators and Investigators

• Sponsor: Konya City Hospital

Study record dates

Study Major Dates

• Study Start (Estimated): June 1, 2026
• Primary Completion (Estimated): December 31, 2026
• Study Completion (Estimated): January 31, 2027

Study Registration Dates

• First Submitted: May 22, 2026
• First Submitted That Met QC Criteria: May 22, 2026
• First Posted (Actual): June 2, 2026

Study Record Updates

• Last Update Posted (Actual): June 2, 2026
• Last Update Submitted That Met QC Criteria: May 22, 2026
• Last Verified: May 1, 2026

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Cardiac Conduction System Disease; Pain; Neurologic Manifestations; Vascular Diseases; Cardiovascular Diseases; Pathologic Processes; Heart Diseases; Arrhythmias, Cardiac; Infarction; Necrosis; Heart Block; Embolism and Thrombosis; Coronary Disease; Myocardial Ischemia; Ischemia; Pathological Conditions, Signs and Symptoms; Signs and Symptoms; Thrombosis; Myocardial Infarction; Bundle-Branch Block; Chest Pain; Coronary Occlusion
• Other Study ID Numbers: 2026/133

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No