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Non Invasive Extra-corporeal ECG Signal Analysis Algorithm( NID Algorithm) for Myocardial Ischemia

T

Taichung Veterans General Hospital

Status

Unknown

Conditions

Myocardial Ischemia
Acute Coronary Syndrome

Treatments

Other: Extra-corporeal ECG signal analysis

Study type

Observational

Funder types

Other

Identifiers

NCT02579512
IGA1041203

Details and patient eligibility

About

The NIA algorithm is similar to the traditional 12-lead ECG equipment. By analyzing patient data, NIA algorithm provides more detailed results compared to traditional 12-lead ECG. Patients with suspected coronary artery disease are conventionally diagnosed and treated by cardiac catheterization. However, cardiac catheterization is invasive procedure. Unless clinical diagnosis is evident before cardiac catheterization, a treadmill exercise test, a nuclear medicine myocardial perfusion test, or a multi-direction coronary CT angiogram is usually performed to increase the accuracy of diagnosis. But these examinations are not accessible to all patients, and are time-consuming and costly.

Full description

In this project, the investigators hope to compare the data collected under this new technology of NIA algorithm with results from final diagnoses of cardiac catheterization. As the NIA algorithm is a fast and less costly, if it provides more sensitivity and specificity than does exercise ECG, nuclear myocardial perfusion test, and high-resolution coronary CT angiogram, it will expedite diagnosis for patients with coronary artery disease.

Enrollment

500 estimated patients

Sex

All

Ages

20 to 95 years old

Volunteers

No Healthy Volunteers

Inclusion criteria

  • patient with acute coronary syndrome who accepted percutaneous coronary intervention

Exclusion criteria

  • no percutaneous coronary intervention

Trial design

500 participants in 1 patient group

Extra-corporeal ECG Signal Analysis
Treatment:
Other: Extra-corporeal ECG signal analysis

Trial contacts and locations

1

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Central trial contact

Yu-Tsung Cheng, M.D.; Hsian-Min Chen, PhD

Data sourced from clinicaltrials.gov

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