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

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

Status

Not yet recruiting

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT07536932

Acronym

TRACE

Enrollment

10000

Registered

2026-04-17

Start date

2026-04-01

Completion date

2026-12-01

Last updated

2026-04-17

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Aortic Dissection Type A, Chest Pain

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.

Interventions

None listed

Study design

Observational model

COHORT

Time perspective

PROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to 80 Years

Healthy volunteers

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.

Design outcomes

Primary

Measure	Time frame	Description
Diagnostic performance 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 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

Measure	Time frame	Description
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	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.
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	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.
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	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.
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	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.
Diagnostic time from emergency department presentation to AI model output	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.
Diagnostic time reduction associated with the AI-based electrocardiogram workflow compared with standard care	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.

Outcome results