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

22. maj 2026 opdateret af: Ahmet Gumus, Konya City Hospital

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.

Studieoversigt

Status

Rekruttering

Betingelser

Intervention / Behandling

Undersøgelsestype

Observationel

Tilmelding (Anslået)

50

Kontakter og lokationer

Dette afsnit indeholder kontaktoplysninger for dem, der udfører undersøgelsen, og oplysninger om, hvor denne undersøgelse udføres.

Studiesteder

  • Tyrkiet (Türkiye)
    • Karatay
      • Konya, Karatay, Tyrkiet (Türkiye), 42100
        • Rekruttering
        • Konya City Hospital
        • Kontakt:
          • Ahmet Gumus, MD, Emergency Medicine Residen
          • Telefonnummer: +905547957490
          • E-mail: ahmetgms88@gmail.com

Deltagelseskriterier

Forskere leder efter personer, der passer til en bestemt beskrivelse, kaldet berettigelseskriterier. Nogle eksempler på disse kriterier er en persons generelle helbredstilstand eller tidligere behandlinger.

Berettigelseskriterier

Aldre berettiget til at studere

  • Voksen
  • Ældre voksen

Tager imod sunde frivillige

Ingen

Prøveudtagningsmetode

Ikke-sandsynlighedsprøve

Studiebefolkning

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.

Beskrivelse

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.

Studieplan

Dette afsnit indeholder detaljer om studieplanen, herunder hvordan undersøgelsen er designet, og hvad undersøgelsen måler.

Hvordan er undersøgelsen tilrettelagt?

Design detaljer

Hvad måler undersøgelsen?

Primære resultatmål

Resultatmål
Foranstaltningsbeskrivelse
Tidsramme
Diagnostic Performance for Occlusive Acute Myocardial Infarction
Tidsramme: 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).
Within the emergency department index visit (typically within 24 hours of presentation).

Sekundære resultatmål

Resultatmål
Foranstaltningsbeskrivelse
Tidsramme
Title: Differentiation Performance Between Type 1 MI and Type 2 MI
Tidsramme: Within the hospital stay (up to 7 days).
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
Tidsramme: Calculated at the study completion
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

Samarbejdspartnere og efterforskere

Det er her, du vil finde personer og organisationer, der er involveret i denne undersøgelse.

Sponsor

Konya City Hospital

Datoer for undersøgelser

Disse datoer sporer fremskridtene for indsendelser af undersøgelsesrekord og resumeresultater til ClinicalTrials.gov. Studieregistreringer og rapporterede resultater gennemgås af National Library of Medicine (NLM) for at sikre, at de opfylder specifikke kvalitetskontrolstandarder, før de offentliggøres på den offentlige hjemmeside.

Studer store datoer

Studiestart (Anslået)

1. juni 2026

Primær færdiggørelse (Anslået)

31. december 2026

Studieafslutning (Anslået)

31. januar 2027

Datoer for studieregistrering

Først indsendt

22. maj 2026

Først indsendt, der opfyldte QC-kriterier

22. maj 2026

Først opslået (Faktiske)

2. juni 2026

Opdateringer af undersøgelsesjournaler

Sidste opdatering sendt (Faktiske)

2. juni 2026

Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier

22. maj 2026

Sidst verificeret

1. maj 2026

Mere information

Begreber relateret til denne undersøgelse

Yderligere relevante MeSH-vilkår

Andre undersøgelses-id-numre

  • 2026/133

Lægemiddel- og udstyrsoplysninger, undersøgelsesdokumenter

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