An observational study in Acute Myocardial Infarction (AMI), Left Bundle Branch Block and Coronary Occlusion/Thrombosis, sponsored by Konya City Hospital. Recruiting at 1 site in Turkey (Türkiye). Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2026-06-02.
Sponsored by Konya City Hospital · Observational
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.
94 studies on the registry are indexed under Bundle-Branch Block; 30 are open to participants now.
This study's planned enrollment of 50 is below the median of 162 across 33 observational studies indexed under Bundle-Branch Block.
Browse Bundle-Branch Block studies →Konya City Hospital is the lead sponsor of 81 studies on the registry; 23 are open to participants now.
Counted across the registry records on this site, refreshed daily.
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.
Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.
Patients with a confirmed Left Bundle Branch Block (LBBB) on their initial 12-lead (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 to participate in the study.
Exclusion Criteria:
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 .
Underlined terms in the criteria open these too. Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
A recording of the heart’s electrical activity, taken with stickers on the chest, arms and legs. It takes a few minutes and does not hurt.
Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.
You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.
ECG (electrocardiogram)
A recording of the heart’s electrical activity, taken with stickers on the chest, arms and legs. It takes a few minutes and does not hurt.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Informed consent
Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.
You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
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.
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).
Time frame: Within the emergency department index visit (typically within 24 hours of presentation).
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).
Time frame: 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.
Time frame: Calculated at the study completion
Start is estimated for Jun 2026; primary completion for Dec 2026, 6 months later.
How these figures are worked out, and which studies are left out of them.
Eligibility is decided by the study team. Share this record with your doctor or contact the team directly.
No contact was published for this record. The registry link below has the sponsor’s details.
Get an email when the registry record changes — status, dates, results — or when someone posts here.
Sign in to followQuestions and observations about this study, from anyone following it. Not medical advice, and not a channel to the study team — their contact details are on the registry record.
Sign in to join the discussion. Reading takes no account; posting does. You choose a display name, and a pseudonym is the default.
Nothing here yet. If you are running this trial, taking part in it, or weighing whether to, this is the place to say so.
Konya City Hospital