Chest PainNCT06669884
Use of Determine Learning-based Cardiodynamicsgram (CDG) for Rapid and Precise Stratification of Chest Pain in Emergency Department
An observational study of Chest Pain and Acute Coronary Syndrome, run by Qilu Hospital of Shandong University at 1 site in China.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Chest Pain, Acute Coronary Syndrome
- What is being tested?
- Cardiodynamicsgram (CDG)
- How many people
- 8,000 participants
- Where
- 1 siteChina
- Study dates
- 28 Oct 2021to 31 Oct 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: patients who suffers from acute chest pain suspected with acute coronary syndrome (ACS)
Who may be able to join
- aged 18 years or older
- Those with suspected ACS who have symptoms of acute chest pain, visiting in the emergency department
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Qilu Hospital of Shandong University
Jinan, Shandong, 250012
Jiaojiao Pang, Doctor, 0086-0531-82165674, jiaojiaopang@126.com
About this study
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
- Being tested
- Cardiodynamicsgram (CDG)
- Medical subject headings
- Chest Pain, Acute Coronary Syndrome
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Cardiodynamicsgram (CDG)Other
Cardiodynamicsgram (CDG) technique
| Study group | Role | What they receive |
|---|---|---|
| machine learning algorithm | machine learning algorithm based on ECG features Receives: Other: Cardiodynamicsgram (CDG) |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: from the date of enrollment until the date of discharge, up to 30 days
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted1 Nov 2024
- Study start28 Oct 2021
- Primary completion30 Sep 2024
- Study completion31 Oct 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Chest PainStudies of the same condition, open and under way.
- More trials run by Qilu Hospital of Shandong UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 8,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Qilu Hospital of Shandong University
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Yuguo Chen, Professor, Principal investigator, Qliu Hospital of Shandong University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06669884
- Sponsor's protocol ID
- QLEmer-CDG-1
- Status last verified
- Oct 2024
- First submitted
- 31 Oct 2024
- Last updated
- 12 Nov 2024
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 12 Nov 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Use of Determine Learning-based Cardiodynamicsgram (CDG) for Rapid and Precise Stratification of Chest Pain in Emergency Department
- ClinicalTrials.gov ID
- NCT06669884
- Acronym / sponsor ID
- QLEmer-CDG-1
- Conditions
- Chest Pain, Acute Coronary Syndrome
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 8,000 participants (estimated)
- Lead sponsor
- Qilu Hospital of Shandong UniversityUniversity, hospital or other
- Study start
- 28 Oct 2021 (actual)
- Primary completion
- 30 Sep 2024 (actual)
- Study completion
- 31 Oct 2024 (estimated)
- First posted
- 1 Nov 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| machine learning algorithm | – | Other: Cardiodynamicsgram (CDG)machine learning algorithm based on ECG features |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- patients who suffers from acute chest pain suspected with acute coronary syndrome (ACS)
Key inclusion criteria (2 listed)
- aged 18 years or older
- Those with suspected ACS who have symptoms of acute chest pain, visiting in the emergency department
Key exclusion criteria (5 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Cardiodynamicsgram (CDG)Cardiodynamicsgram (CDG) technique | Other | machine learning algorithm |
Primary outcome
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.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 12 Nov 2024.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Yuguo Chen, Professor principal investigator
Qliu Hospital of Shandong University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Chest Pain and Acute Coronary Syndrome, run by Qilu Hospital of Shandong University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 12 Nov 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Jiaojiao Pang, Doctor (0086-0531-82165674, jiaojiaopang@126.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 12 Nov 2024.