Acute Coronary Syndrome Clinical Trial
Official title:
Magnetocardiography in the Identification and Risk Stratification of Patients With Acute Chest Pian
This prospective cohort study is aimed at identification and risk stratification of patients who have symptoms of acute chest pain suspected with acute coronary syndrome (ACS) using Magnetocardiography (MCG).
Status | Not yet recruiting |
Enrollment | 3620 |
Est. completion date | August 31, 2024 |
Est. primary completion date | April 30, 2024 |
Accepts healthy volunteers | No |
Gender | All |
Age group | N/A and older |
Eligibility | Inclusion Criteria: - Those with suspected ACS who presented with symptoms of acute chest pain. - Signed informed consent. Exclusion Criteria: - Those with known structural heart disease such as cardiomyopathy and valvular disease; - Those with atrial fibrillation, supraventricular tachycardia, atrioventricular block and other arrhythmias that have not returned to normal; - Those who have chest pain with known pulmonary embolism or aortic coarctation; - Those who are thought to be incapable of completing the MCG examination by an attending physician (or physician with higher qualifications) due to unstable clinical conditions, metal implants, etc; - Those who are unable to perform magnetocardiography examinations due to claustrophobia, etc., or those who fail to receive magnetocardiography examination; - Those with malignant tumors; - Pregnant or lactating women. - Those with acute or critical illnesses of other systems, such as acute or severe respiratory illnesses, marked abnormalities in liver function or kidney function. |
Country | Name | City | State |
---|---|---|---|
n/a |
Lead Sponsor | Collaborator |
---|---|
Qilu Hospital of Shandong University |
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | Efficacy of MCG for the detection and risk stratification in patients with chest pain | Establishing an algorithm model of MCG in identifying high-risk cardiogenic chest pain, 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 |
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