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

NCT06669884 | Recruiting DMC

• 1 other identifier: QLEmer-CDG-1
• Study Type: observational
• Target: 8,000
• Locations: 1 country
• Sites: 1

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.

Conditions

Chest Pain; Acute Coronary Syndrome

Outcome Measures

Primary Outcomes (1)

• The efficacy of CDG in the risk stratification of patients who have symptoms of acute chest pain suspected with acute coronary syndrome （ACS)

  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.

  from the date of enrollment until the date of discharge, up to 30 days

Study Arms (1)

machine learning algorithm

machine learning algorithm based on ECG features

Other: Cardiodynamicsgram (CDG)

Interventions

Cardiodynamicsgram (CDG) OTHER

Cardiodynamicsgram (CDG) technique

machine learning algorithm

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

patients who suffers from acute chest pain suspected with acute coronary syndrome (ACS)

You may qualify if:

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

You may not qualify if:

• 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.

Sponsors & Collaborators

• Qilu Hospital of Shandong University: lead

Study Sites (1)

Qilu Hospital of Shandong University

Jinan, Shandong, 250012, China

RECRUITING

MeSH Terms

Conditions

Chest Pain; Acute Coronary Syndrome

Condition Hierarchy (Ancestors)

Pain; Neurologic Manifestations; Signs and Symptoms; Pathological Conditions, Signs and Symptoms; Myocardial Ischemia; Heart Diseases; Cardiovascular Diseases; Vascular Diseases

Study Officials

• Yuguo Chen, Professor

  Qliu Hospital of Shandong University

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Jiaojiao Pang, Doctor

CONTACT

0086-0531-82165674; jiaojiaopang@126.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: October 31, 2024
• First Posted: November 1, 2024
• Study Start: October 28, 2021
• Primary Completion: September 30, 2024
• Study Completion: October 31, 2024
• Last Updated: November 12, 2024

Record last verified: 2024-10

Locations

CN (1)