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

22 maj 2026 uppdaterad av: 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.

Studieöversikt

Status

Rekrytering

Betingelser

Intervention / Behandling

Studietyp

Observationell

Inskrivning (Beräknad)

50

Kontakter och platser

Det här avsnittet innehåller kontaktuppgifter för dem som genomför studien och information om var denna studie genomförs.

Studieorter

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

Deltagandekriterier

Forskare letar efter personer som passar en viss beskrivning, så kallade behörighetskriterier. Några exempel på dessa kriterier är en persons allmänna hälsotillstånd eller tidigare behandlingar.

Urvalskriterier

Åldrar som är berättigade till studier

  • Vuxen
  • Äldre vuxen

Tar emot friska volontärer

Nej

Testmetod

Icke-sannolikhetsprov

Studera befolkning

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.

Beskrivning

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

Det här avsnittet ger detaljer om studieplanen, inklusive hur studien är utformad och vad studien mäter.

Hur är studien utformad?

Designdetaljer

Vad mäter studien?

Primära resultatmått

Resultatmått
Åtgärdsbeskrivning
Tidsram
Diagnostic Performance for Occlusive Acute Myocardial Infarction
Tidsram: 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).

Sekundära resultatmått

Resultatmått
Åtgärdsbeskrivning
Tidsram
Title: Differentiation Performance Between Type 1 MI and Type 2 MI
Tidsram: 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
Tidsram: 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

Samarbetspartners och utredare

Det är här du hittar personer och organisationer som är involverade i denna studie.

Sponsor

Konya City Hospital

Studieavstämningsdatum

Dessa datum spårar framstegen för inlämningar av studieposter och sammanfattande resultat till ClinicalTrials.gov. Studieposter och rapporterade resultat granskas av National Library of Medicine (NLM) för att säkerställa att de uppfyller specifika kvalitetskontrollstandarder innan de publiceras på den offentliga webbplatsen.

Studera stora datum

Studiestart (Beräknad)

1 juni 2026

Primärt slutförande (Beräknad)

31 december 2026

Avslutad studie (Beräknad)

31 januari 2027

Studieregistreringsdatum

Först inskickad

22 maj 2026

Först inskickad som uppfyllde QC-kriterierna

22 maj 2026

Första postat (Faktisk)

2 juni 2026

Uppdateringar av studier

Senaste uppdatering publicerad (Faktisk)

2 juni 2026

Senaste inskickade uppdateringen som uppfyllde QC-kriterierna

22 maj 2026

Senast verifierad

1 maj 2026

Mer information

Termer relaterade till denna studie

Ytterligare relevanta MeSH-villkor

Andra studie-ID-nummer

  • 2026/133

Läkemedels- och apparatinformation, studiedokument

Studerar en amerikansk FDA-reglerad läkemedelsprodukt

Nej

Studerar en amerikansk FDA-reglerad produktprodukt

Nej

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