Aortic Dissection Type ATRACENCT07536932

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

An observational study of Aortic Dissection Type A and Chest Pain, run by Shanghai Zhongshan Hospital.

Not yet recruiting

Opening soon. The study is approved but enrolment has not started yet. Sites may already take enquiries.

Trial 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. Read more about this study

Who is it for?: Ages 18 to 80 years
Conditions the study is for: Aortic Dissection Type A, Chest Pain
How many people: 10,000 participants
Where: No sites listed yet
Study dates: Apr 2026to Dec 2026
How long will I take part?: 1 Day
Phase: Observational
Are there results?: Not yet: the study is under way

Key requirements

Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.

Ages 18 to 80 years
All sexes
No healthy volunteers
Adult, Older adult

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.

Who may be able to join

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.

Who cannot join

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.

About this study

Official 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

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
Medical subject headings: Chest Pain

What participants receive

What participants receive. In a randomised study each person is assigned to one group by chance.

Study groups in NCT07536932
Study group	Role	What they receive
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.

What is being measured

The primary outcome is the main question the study is designed to answer.

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).
Measured: 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
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.
Measured: 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
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.
Measured: 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
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.
Measured: 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
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.
Measured: 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
Elapsed time from emergency department presentation to generation of the artificial intelligence model output after electrocardiogram acquisition.
Measured: At the index visit, up to 24 hours
Diagnostic time reduction associated with the AI-based electrocardiogram workflow compared with standard care
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.
Measured: At the index visit, up to 24 hours

Timeline

From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.

Apr 2026Dec 2026

First posted17 Apr 2026
Study startApr 2026 (estimated)
Primary completionDec 2026 (estimated)
Study completionDec 2026 (estimated)

Resources

Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
Other trials for Aortic Dissection Type AStudies of the same condition, open and under way.
More trials run by Shanghai Zhongshan HospitalThe sponsor's other registered studies.
What the trial phases meanHow a treatment moves from first tests in people to approval.

More details

For doctors and researchers: how the study is designed, who runs it, and its registry IDs.

Study design

Study type: Observational
Observational model: Cohort
Time perspective: Prospective
Enrolment: 10,000 participants (estimated)
Target duration: 1 Day

IDs, dates and publications

ClinicalTrials.gov ID: NCT07536932
Sponsor's protocol ID: B2026-106
Acronym: TRACE
Status last verified: Apr 2026
First submitted: 11 Apr 2026
Last updated: 17 Apr 2026
Results posted: No
Data monitoring committee: No
FDA-regulated drug: No
FDA-regulated device: No

Interested in taking part?

The sponsor has not listed a contact. The official record may have more details.

Talk to your doctor first and take this page with you. Only the study team can confirm whether you can join.

Questions to ask the study team

What is the study trying to find out?
What would I need to do, and how often would I visit?
Could I receive a placebo instead of the treatment?
What are the possible risks and benefits for me?
Will it cost me anything? Are travel or time paid for?
Can I keep seeing my own doctor during the study?
What happens when the study ends?

Official record

Active, not recruiting
GORE® Ascending Stent Graft in the Treatment of De Novo Type A Aortic Dissections
Aortic Dissection
Recruiting
Hemiarch vs Extended Arch in Type 1 Aortic Dissection
Aortic Dissection
Recruiting
Remote Ischemic Preconditioning for Aute Type A Aortic Dissection Surgery
Aortic Dissection Type A
Recruiting
EXtensive mulTilayer stEnt treatmeNt in Aortic disSectiOn
Type B Aortic Dissection
RecruitingPhase 3
Study on Ketorolac for Improving Outcomes and Prognosis in Patients With Stanford Type A Aortic Dissection
Aortic Dissection, Inflammation

All Aortic Dissection Type A trials

Study at a glance

From the registry record, last updated by the sponsor on 17 Apr 2026. Recruitment status is as the sponsor reported it: confirm with the study team.

Official 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
ClinicalTrials.gov ID: NCT07536932
Acronym / sponsor ID: TRACE / B2026-106
Conditions: Aortic Dissection Type A, Chest Pain
Study type: Observational
Recruitment status: Not yet recruiting
Enrolment: 10,000 participants (estimated)
Lead sponsor: Shanghai Zhongshan HospitalUniversity, hospital or other
Collaborators: Yan'an Affiliated Hospital of Kunming Medical University, Taian City Central Hospital, Mianyang Central Hospital, Guangdong Provincial People's Hospital
Study start: Apr 2026 (estimated)
Primary completion: Dec 2026 (estimated)
Study completion: Dec 2026 (estimated)
First posted: 17 Apr 2026 (actual)
Results: Not posted in the source record

Clinical summary

The research question as the sponsor registered it. This describes what the study plans to test, not what it found.

Study design

Observational model: Cohort
Time perspective: Prospective
Target follow-up: 1 Day
Number of arms: 2

Arm	Type	Receives
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.

Who may qualify

Ages: 18 to 80 years
Sex: All
Healthy volunteers: Not accepted
Age groups: Adult, Older adult
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.

Key inclusion criteria (5 listed)

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.

Key exclusion criteria (3 listed)

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.

Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.

Primary outcome

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).

6 secondary outcomes

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
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
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
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
Diagnostic time from emergency department presentation to AI model output
Time frame: At the index visit, up to 24 hours
Diagnostic time reduction associated with the AI-based electrocardiogram workflow compared with standard care
Time frame: At the index visit, up to 24 hours

Results and safety

This study is still under way. Results are not available in the source record yet.

Research links

Official record on ClinicalTrials.govLast updated by the sponsor 17 Apr 2026.

Related trials for Aortic Dissection Type A

Active, not recruiting
GORE® Ascending Stent Graft in the Treatment of De Novo Type A Aortic Dissections
Aortic Dissection
Recruiting
Hemiarch vs Extended Arch in Type 1 Aortic Dissection
Aortic Dissection
Recruiting
Remote Ischemic Preconditioning for Aute Type A Aortic Dissection Surgery
Aortic Dissection Type A
Recruiting
EXtensive mulTilayer stEnt treatmeNt in Aortic disSectiOn
Type B Aortic Dissection
RecruitingPhase 3
Study on Ketorolac for Improving Outcomes and Prognosis in Patients With Stanford Type A Aortic Dissection
Aortic Dissection, Inflammation

Referral and contact

AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.

The record lists no contact. Use the official record to reach the sponsor.

Questions about this trial

What is this clinical trial studying?

An observational study of Aortic Dissection Type A and Chest Pain, run by Shanghai Zhongshan Hospital.

The full registered description is in the About this study section and the official record.

Who may be eligible to take part?

The sponsor lists 5 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.

Is this clinical trial currently recruiting?

The record says: opening soon, as of the sponsor's last update on 17 Apr 2026. Status can change between updates, so confirm with the study contact before making plans.

What are the possible benefits and risks?

They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.

Are results available for this study?

This study is still under way. Results are not available in the source record yet.

How can a doctor refer a patient or contact the research team?

The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.

What does the primary outcome measure?

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). It is what the study was designed to measure, not a finding.

Where can I read the original study information?

On the ClinicalTrials.gov record for NCT07536932.

Does a listing mean the treatment is approved or proven to work?

No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.

How often is this information updated?

AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 17 Apr 2026.