Machine Learning for Diagnosis of Occlusive MI in LBBB Patients

Condition(s) studied

Investigational drug(s) / intervention(s)

Digital 12-Lead ECG Analysis and Invasive Coronary Angiography: 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.

Study 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.

Eligibility

Primary outcome measure(s)

• Diagnostic Performance for Occlusive Acute Myocardial Infarction — Within the emergency department index visit (typically within 24 hours of presentation).
  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).

Trial sites (1)

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