Use of Determine Learning-based Cardiodynamicsgram (CDG) for Rapid and Precise Stratification of Chest Pain in Emergency Department

Shandong University

Status

Enrolling

Conditions

Acute Coronary Syndrome

Chest Pain

Treatments

Other: Cardiodynamicsgram (CDG)

Study type

Observational

Funder types

Other

Identifiers

NCT06669884

QLEmer-CDG-1

Details and patient eligibility

About

Chest pain accounts for 10-20 percent of all emergency department visits. The stratification of chest pain is always a challenge. Electrocardiograms (ECG) have been used in clinical practice for 100 years, which is too important to be replaced due to its advantages of non-invasive, simple, rapid and inexpensive. ECG contains numerous signals derived from depolarization and repolarization of cardiomyocytes. However, the interpretation of ECG hasn't improved much in a hundred years. Based on determine-learning, Cong W's team developed an technique called "cardiodynamicsgram (CDG)", which is an outstanding method to identify myocardial ischemia. This study will further investigate the accuracy of CDG in stratification of patients with chest pain in Emergency department.

Enrollment

8,000 estimated patients

Sex

All

Ages

18+ years old

Volunteers

No Healthy Volunteers

Inclusion criteria

aged 18 years or older
Those with suspected ACS who have symptoms of acute chest pain, visiting in the emergency department

Exclusion criteria

Those who diagnosed with ST-segment elevation myocardial infarction (STEMI)
Those with hemodynamic instability (cardiogenic shock, cardiac arrest)
Those with malignant arrhythmias(ventricular tachycardia, ventricular fibrillation, third-degree atrioventricular block)
Those with aortic coarctation, or acute pulmonary embolism
Those who has an unanalysable ECG report due to loosened leads, unstable baseline, or signal interference, etc.