Acute Myocardial Infarction (AMI)AI-LBBBNCT07620119
Machine Learning for Diagnosis of Occlusive MI in LBBB Patients
An observational study of Acute Myocardial Infarction (AMI) and Left Bundle Branch Block, run by Konya City Hospital at 1 site in Turkey (Türkiye).
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Acute Myocardial Infarction (AMI), Left Bundle Branch Block, Coronary Occlusion/Thrombosis
- What is being tested?
- Digital 12-Lead ECG Analysis and Invasive Coronary Angiography
- How many people
- 50 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 1 Jun 2026to 31 Jan 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Turkey (Türkiye)1 site, 1 recruiting
- Recruiting
Konya City Hospital
Konya, Karatay, 42100
Ahmet Gumus, MD, Emergency Medicine Residen, +905547957490, ahmetgms88@gmail.com
About this study
Official title: 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.
- Conditions
- Acute Myocardial Infarction (AMI), Left Bundle Branch Block, Coronary Occlusion/Thrombosis, Chest Pain, Myocardial Infarction
- Being tested
- Digital 12-Lead ECG Analysis and Invasive Coronary Angiography
- Medical subject headings
- Bundle-Branch Block, Coronary Occlusion, Thrombosis, Chest Pain, Myocardial Infarction
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Digital 12-Lead ECG Analysis and Invasive Coronary AngiographyOther
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.
What is being measured
The primary outcome is the main question the study is designed to answer.
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).
Measured: 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
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).
Measured: 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.
Measured: Calculated at the study completion
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted2 Jun 2026
- Study start1 Jun 2026 (estimated)
- Primary completion31 Dec 2026 (estimated)
- Study completion31 Jan 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Acute Myocardial Infarction (AMI)Studies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 50 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Konya City Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Ahmet Gumus, Konya City Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07620119
- Sponsor's protocol ID
- 2026/133
- Acronym
- AI-LBBB
- Status last verified
- May 2026
- First submitted
- 22 May 2026
- Last updated
- 2 Jun 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 2 Jun 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development of a Machine Learning Model for the Diagnosis of Occlusive Myocardial Infarction in the Setting of Left Bundle Branch Block
- ClinicalTrials.gov ID
- NCT07620119
- Acronym / sponsor ID
- AI-LBBB / 2026/133
- Conditions
- Acute Myocardial Infarction (AMI), Left Bundle Branch Block, Coronary Occlusion/Thrombosis, Chest Pain, Myocardial Infarction
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 50 participants (estimated)
- Lead sponsor
- Konya City HospitalUniversity, hospital or other
- Study start
- 1 Jun 2026 (estimated)
- Primary completion
- 31 Dec 2026 (estimated)
- Study completion
- 31 Jan 2027 (estimated)
- First posted
- 2 Jun 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (4 listed)
- 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.
Key exclusion criteria (4 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Digital 12-Lead ECG Analysis and Invasive Coronary AngiographyStandard 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. | Other | – |
Primary outcome
Diagnostic Performance for Occlusive Acute Myocardial Infarction
Time frame: 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).
2 secondary outcomes
Title: Differentiation Performance Between Type 1 MI and Type 2 MI
Time frame: Within the hospital stay (up to 7 days).
Projected Reduction Rate of Unnecessary Angiographies
Time frame: Calculated at the study completion
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 2 Jun 2026.
Related trials for Acute Myocardial Infarction (AMI)
Not yet recruitingObservational study
Elicit Drugs in Myocardial Infarction
Young Patients With Acute Myocardial Infarction
Recruiting
CT Stress Myocardial Perfusion, Fractional Flow Reserve and Angiography in Patients With Stable Chest Pain Syndromes
Coronary Artery Disease, Chest Syndrome
RecruitingPhase 4
Early Single Antiplatelet Therapy After IVUS-Guided PCI in Acute Coronary Syndrome
Acute Coronary Syndromes, STEMI
Recruiting
Heart@Home Safety and Efficacy of Hospital-at-Home Care Versus Standard Hospitalization in Cardiac Patients
Myocardial Infarction, Myocarditis Acute
Enrolling by invitationPhase 3
Selatogrel Outcome Study in Suspected Acute Myocardial Infarction
Acute Myocardial Infarction
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Acute Myocardial Infarction (AMI) and Left Bundle Branch Block, run by Konya City Hospital at 1 site in Turkey (Türkiye).
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 2 Jun 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Turkey (Türkiye). Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Each site lists its own contact: choose the nearest one under Trial locations. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic Performance for Occlusive Acute Myocardial Infarction (time frame: Within the emergency department index visit (typically within 24 hours of presentation).). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 2 Jun 2026.