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NCT Number: NCT07536932

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

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.

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Key information

Conditions

Aortic Dissection Type A Chest Pain Neurologic Manifestations Pain Pathological Conditions, Signs and Symptoms Signs and Symptoms

Age range

18 year–80 year

Sex eligibility

All sexes

Study type

Observational

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • 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.

Exclusion criteria

  • 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.

Treatment and study plan

Primary outcomes

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

    Time frame: 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).

Secondary outcomes

  1. Sensitivity of the AI-based electrocardiogram model for acute type A aortic dissection

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

    Sensitivity 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.

  2. Specificity of the AI-based electrocardiogram model for acute type A aortic dissection

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

    Specificity of the artificial intelligence model based on electrocardiograms for correctly identifying participants who do not have acute type A aortic dissection, using CTA-confirmed final diagnosis as the reference standard.

  3. Positive predictive value of the AI-based electrocardiogram model for acute type A aortic dissection

    Time frame: 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 artificial intelligence model based on electrocardiograms for acute type A aortic dissection among participants classified as positive by the model, using CTA-confirmed final diagnosis as the reference standard.

  4. Negative predictive value of the AI-based electrocardiogram model for acute type A aortic dissection

    Time frame: 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 artificial intelligence model based on electrocardiograms for acute type A aortic dissection among participants classified as negative by the model, using CTA-confirmed final diagnosis as the reference standard.

  5. Diagnostic time from emergency department presentation to AI model output

    Time frame: At the index visit, up to 24 hours

    Elapsed time from emergency department presentation to generation of the artificial intelligence model output after electrocardiogram acquisition.

  6. Diagnostic time reduction associated with the AI-based electrocardiogram workflow compared with standard care

    Time frame: At the index visit, up to 24 hours

    Difference in diagnostic time between the AI-based electrocardiogram workflow and the conventional diagnostic process. This outcome will be calculated as the time from emergency department presentation to final diagnostic confirmation under standard care minus the time from emergency department presentation to AI model output.

Sponsors and collaborators

Lead sponsor

Shanghai Zhongshan Hospital

Other

Collaborators

  • Guangdong Provincial People's Hospital
  • Mianyang Central Hospital
  • Taian City Central Hospital
  • Yan'an Affiliated Hospital of Kunming Medical University

Registry information

Official study title

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

Acronym: TRACE

Important dates

Study start
2026
Primary completion
2026
Study completion
2026
First posted
Apr 17, 2026
Registry last updated
Apr 17, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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