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

October 31, 2024 updated by: Qilu Hospital of Shandong University
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. In this study, we will further improve the CDG technique and explore its accuracy in stratification of patients with chest pain in Emergency department.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Study Type

Observational

Enrollment (Estimated)

8000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • China
    • Shandong
      • Jinan, Shandong, China, 250012
        • Recruiting
        • Qilu Hospital of Shandong University
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

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

Description

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 Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
machine learning algorithm
machine learning algorithm based on ECG features
Other: Cardiodynamicsgram (CDG)
Cardiodynamicsgram (CDG) technique

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
The efficacy of CDG in the risk stratification of patients who have symptoms of acute chest pain suspected with acute coronary syndrome （ACS)
Time Frame: 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.
from the date of enrollment until the date of discharge, up to 30 days

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Qilu Hospital of Shandong University

Investigators

  • Principal Investigator: Yuguo Chen, Professor, Qliu Hospital of Shandong University

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

October 28, 2021

Primary Completion (Actual)

September 30, 2024

Study Completion (Estimated)

October 31, 2024

Study Registration Dates

First Submitted

October 31, 2024

First Submitted That Met QC Criteria

October 31, 2024

First Posted (Estimated)

November 1, 2024

Study Record Updates

Last Update Posted (Estimated)

November 1, 2024

Last Update Submitted That Met QC Criteria

October 31, 2024

Last Verified

October 1, 2024

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • QLEmer-CDG-1

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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