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

Recruiting

• Conditions: Chest Pain; Acute Coronary Syndrome

• Registration Number: NCT06669884
• Lead Sponsor: Qilu Hospital of Shandong University

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.

Detailed Description

Not available

Study Timeline

• Study Start: 2021-10-28
• Primary Completion: 2024-09-30
• Status Verified: 2024-10
• Study Completion: 2024-10-31
• Study First Submitted: 2024-10-31
• Study First Submitted QC: 2024-10-31
• Study First Posted: 2024-11-01
• Last Update Submitted: 2024-11-07
• Last Update Posted: 2024-11-11

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 8000

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.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
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.

Trial Locations

Locations (1)

Qilu Hospital of Shandong University; 🇨🇳 Jinan, Shandong, China