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.
aged 18 years or older
Those with suspected ACS who have symptoms of acute chest pain, visiting in the emergency department
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
patients who suffers from acute chest pain suspected with acute coronary syndrome (ACS)
Cohort
Prospective
Cardiodynamicsgram (CDG)
Other interventionCardiodynamicsgram (CDG) technique
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.
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