A Multicenter Prospective Study to Develop and Validate an Artificial Intelligence-Based Electrocardiogram Model for the Diagnosis of Acute Type A Aortic Dissection in Patients Presenting With Chest Pain
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 发起方
- 入组人数
- 10,000
- 主要终点
- Diagnostic performance of the AI-based electrocardiogram model for acute type A aortic dissection
研究概览
简要总结
The goal of this prospective multicenter observational study is to learn whether an artificial intelligence model based on electrocardiograms (ECGs) can help diagnose acute type A aortic dissection (TAAD) in adults who come to the emergency department with chest pain or related symptoms. The main question it aims to answer is:
Can the AI-ECG model accurately distinguish TAAD from other causes of chest pain in a real-world emergency setting? Researchers will compare the AI model's ECG-based predictions with the final diagnosis confirmed by computed tomographic angiography (CTA), which is the reference standard. Participants will undergo routine emergency ECG testing and subsequent diagnostic evaluation as part of standard care. Clinical and ECG data will be collected from five tertiary hospitals, and the model's diagnostic performance will be assessed across centers.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 80 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Male or female emergency department patients aged 18-80 years;
- •Clear presentation of chest pain or related chest/back pain;
- •Completion of standard 12-lead electrocardiography (ECG) within 24 hours after onset of chest pain;
- •ECG signal quality meeting the following criteria: QRS amplitude ≥ 0.1 mV and noise proportion < 20%;
- •Availability of subsequent diagnostic workup confirming whether the patient had acute type A aortic dissection (TAAD) or another definitive diagnosis.
排除标准
- •Poor-quality ECG recordings, defined as missing leads in ≥ 3 leads or severe baseline instability;
- •Indeterminate final diagnosis;
- •History of prior surgery involving the aortic valve, aortic root, or ascending aorta.
研究组 & 干预措施
Acute Type A Aortic Dissection (TAAD)
Participants presenting with chest pain or related symptoms who are ultimately diagnosed with acute type A aortic dissection based on computed tomographic angiography (CTA) or other definitive diagnostic modalities. All participants undergo electrocardiogram (ECG) acquisition and standard clinical evaluation in the emergency setting, and their data are used to assess the diagnostic performance of the artificial intelligence-based ECG model.
Non-TAAD Chest Pain
Participants presenting with chest pain or related symptoms who are determined not to have acute type A aortic dissection after complete diagnostic evaluation. Final diagnoses may include other cardiovascular or non-cardiovascular causes of chest pain. All participants undergo electrocardiogram (ECG) acquisition and standard clinical evaluation in the emergency setting, and their data are used to assess the diagnostic performance of the artificial intelligence-based ECG model.
结局指标
主要结局
Diagnostic performance of the AI-based electrocardiogram model for acute type A aortic dissection
时间窗: From emergency department presentation to completion of CTA and final diagnostic confirmation during the index visit, up to 24 hours
Diagnostic performance of the artificial intelligence model based on electrocardiograms for identifying acute type A aortic dissection among patients presenting with chest pain or related symptoms, using CTA-confirmed final diagnosis as the reference standard. Primary performance will be summarized by the area under the receiver operating characteristic curve (AUROC).
次要结局
- Sensitivity of the AI-based electrocardiogram model for acute type A aortic dissection(From emergency department presentation to completion of CTA and final diagnostic confirmation during the index visit, up to 24 hours)
- Specificity of the AI-based electrocardiogram model for acute type A aortic dissection(From emergency department presentation to completion of CTA and final diagnostic confirmation during the index visit, up to 24 hours)
- Positive predictive value of the AI-based electrocardiogram model for acute type A aortic dissection(From emergency department presentation to completion of CTA and final diagnostic confirmation during the index visit, up to 24 hours)
- Negative predictive value of the AI-based electrocardiogram model for acute type A aortic dissection(From emergency department presentation to completion of CTA and final diagnostic confirmation during the index visit, up to 24 hours)
- Diagnostic time from emergency department presentation to AI model output(At the index visit, up to 24 hours)
- Diagnostic time reduction associated with the AI-based electrocardiogram workflow compared with standard care(At the index visit, up to 24 hours)