Prediction of Acute Myocardial Infarction With Artificial Neural Networks in Patients With Nondiagnostic Electrocardiogram
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 1,100
- 主要终点
- myocardial infarction
研究概览
简要总结
prediction of MI in patients with chest pain and nondiagnostic ECG was done in 2 weeks
详细描述
Myocardial infarction remains one the leading causes of mortality and morbidity and involves a high cost of care. Early prediction can be helpful in preventing the development of myocardial infarction with appropriate diagnosis and treatment. Artificial neural networks have opened new horizons in learning about the natural history of diseases and predicting cardiac disease.
Methods: A total of 935 cardiac patients with chest pain and nondiagnostic electrocardiogram (ECG) were enrolled and followed for 2 weeks in two groups based on the appearance of myocardial infarction. Two types of data were used for all patients: nominal (clinical data) and quantitative (ECG findings). Two different artificial neural networks - radial basis function (RBF) and multi-layer perceptron (MLP) - were used.
研究设计
- 研究类型
- Interventional
- 分配方式
- Non Randomized
- 干预模型
- Parallel
- 主要目的
- Diagnostic
- 盲法
- Quadruple (Participant, Care Provider, Investigator, Outcomes Assessor)
入排标准
- 年龄范围
- 40 Years 至 72 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •patient with chest pain refered to ER with nondiagnostic ECG
排除标准
- •Absence of a history of myocardial infarction
- •Absence of bundle branch block, Wolf-Parkinson-White abnormality, ventricular hypertrophy or previous ECG signs of myocardial infarction,
- •Absence of a history of percutaneous coronary surgery or coronary artery bypass grafting,
- •Absence of ECG abnormalities attributable to drugs such as digoxin or tricyclic antidepressants.
结局指标
主要结局
myocardial infarction
时间窗: 2 weeks
次要结局
- hospital admission due to cardiac events(2 weeks)
研究者
Javad Kojuri
professor
Shiraz University of Medical Sciences