Machine Learning for Diagnosis of Occlusive MI in LBBB Patients (AI-LBBB)
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.
調査の概要
状態
研究の種類
入学 (推定)
連絡先と場所
研究場所
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Karatay
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Konya、Karatay、トルコ（Türkiye）、42100
- 募集
- Konya City Hospital
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コンタクト：
- Ahmet Gumus, MD, Emergency Medicine Residen
- 電話番号：+905547957490
- メール：ahmetgms88@gmail.com
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参加基準
適格基準
就学可能な年齢
- 大人
- 高齢者
健康ボランティアの受け入れ
サンプリング方法
調査対象母集団
説明
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.
研究計画
研究はどのように設計されていますか？
デザインの詳細
この研究は何を測定していますか？
主要な結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
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Diagnostic Performance for Occlusive Acute Myocardial Infarction
時間枠：Within the emergency department index visit (typically within 24 hours of presentation).
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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).
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Within the emergency department index visit (typically within 24 hours of presentation).
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二次結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
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Title: Differentiation Performance Between Type 1 MI and Type 2 MI
時間枠：Within the hospital stay (up to 7 days).
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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).
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Within the hospital stay (up to 7 days).
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Projected Reduction Rate of Unnecessary Angiographies
時間枠：Calculated at the study completion
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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.
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Calculated at the study completion
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協力者と研究者
スポンサー
研究記録日
主要日程の研究
研究開始 (推定)
一次修了 (推定)
研究の完了 (推定)
試験登録日
最初に提出
QC基準を満たした最初の提出物
最初の投稿 (実際)
学習記録の更新
投稿された最後の更新 (実際)
QC基準を満たした最後の更新が送信されました
最終確認日
詳しくは
この情報は、Web サイト clinicaltrials.gov から変更なしで直接取得したものです。研究の詳細を変更、削除、または更新するリクエストがある場合は、register@clinicaltrials.gov。 までご連絡ください。 clinicaltrials.gov に変更が加えられるとすぐに、ウェブサイトでも自動的に更新されます。