Triage and Recognition of Acute Aortic Dissection in Chest Pain by Electrocardiogram-Artificial Intelligence

ConditionsAortic Dissection Type A Chest Pain

Trial statusNot yet recruiting

Trial phaseNot listed

Trial typeObservational

Biological sexAll

Age18-80

SponsorShanghai Zhongshan Hospital

About this trial

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.

Eligibility criteria

This trial does not accept healthy volunteers

Qualifiers

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%;

Disqualifiers

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.

Trial population

Adult male and female emergency department patients aged 18 to 80 years who present with clear chest pain or related chest/back pain symptoms at five tertiary hospitals, undergo standard 12-lead electrocardiography within 24 hours of symptom onset, and subsequently receive definitive diagnostic evaluation confirming acute type A aortic dissection or another final diagnosis.

Trial design

Design model

Cohort

Time perspective

Prospective

Treatments tested in this trial

Not listed

Trial groups

10,000 Participants

are grouped into 2 trial groups

Group A: Acute Type A Aortic Dissection (TAAD)

Group B: Non-TAAD Chest Pain

Trial outcomes

Primary outcomes

Diagnostic performance of the AI-based electrocardiogram model for acute type A aortic dissection

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).

Time frame