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

NCT07536932 | Not Yet Recruiting

• 1 other identifier: B2026-106
• Study Type: observational
• Target: 10,000
• Locations: 0 countries
• Sites: N/A

Brief Summary

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.

Conditions

Aortic Dissection Type A; Chest Pain

Outcome Measures

Primary Outcomes (1)

• 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).

  From emergency department presentation to completion of CTA and final diagnostic confirmation during the index visit, up to 24 hours

Secondary Outcomes (6)

• 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

Study Arms (2)

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.

Eligibility Criteria

• Age: 18 Years - 80 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study 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.

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Shanghai Zhongshan Hospital: lead
• Yan'an Hospital of Kunming City: collaborator
• Taian City Central Hospital: collaborator
• Mianyang Central Hospital: collaborator
• Guangdong Provincial People's Hospital: collaborator

MeSH Terms

Conditions

Chest Pain

Condition Hierarchy (Ancestors)

Pain; Neurologic Manifestations; Signs and Symptoms; Pathological Conditions, Signs and Symptoms

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 1 Day
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: April 11, 2026
• First Posted: April 17, 2026
• Study Start: April 1, 2026
• Primary Completion (Estimated): December 1, 2026
• Study Completion (Estimated): December 1, 2026
• Last Updated: April 17, 2026

Record last verified: 2026-04