Use of Determine Learning-based Cardiodynamicsgram (CDG) for Rapid and Precise Stratification of Chest Pain in Emergency Department
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 8,000
- 试验地点
- 1
- 主要终点
- The efficacy of CDG in the risk stratification of patients who have symptoms of acute chest pain suspected with acute coronary syndrome （ACS)
研究概览
简要总结
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •aged 18 years or older
- •Those with suspected ACS who have symptoms of acute chest pain, visiting in the emergency department
排除标准
- •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.
结局指标
主要结局
The efficacy of CDG in the risk stratification of patients who have symptoms of acute chest pain suspected with acute coronary syndrome （ACS)
时间窗: 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.
次要结局
未报告次要终点