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

Status

Recruiting

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT06669884

Enrollment

8000

Registered

2024-11-01

Start date

2021-10-28

Completion date

2024-10-31

Last updated

2024-11-12

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

Conditions

Acute Coronary Syndrome, Chest Pain

Brief summary

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.

Interventions

OTHERCardiodynamicsgram (CDG)

Cardiodynamicsgram (CDG) technique

Study design

Observational model

COHORT

Time perspective

PROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to No maximum

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.

Design outcomes

Primary

Measure	Time frame	Description
The efficacy of CDG in the risk stratification of patients who have symptoms of acute chest pain suspected with acute coronary syndrome （ACS)	from the date of enrollment until the date of discharge, up to 30 days	Establishing an algorithm model of CDG in risk stratification in chest pain patients, the efficacy of the model was assessed by sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and AUC, etc.

Countries

China

Contacts

Primary ContactJiaojiao Pang, Doctor

jiaojiaopang@126.com0086-0531-82165674

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026