Myocardial Infarction Prediction

Status Completed

Clinical Trial Summary

prediction of MI in patients with chest pain and nondiagnostic ECG was done in 2 weeks

Clinical Trial Description

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. ;

Study Design

Allocation: Non-Randomized, Endpoint Classification: Safety/Efficacy Study, Intervention Model: Parallel Assignment, Masking: Double Blind (Subject, Caregiver, Investigator, Outcomes Assessor), Primary Purpose: Diagnostic

Related Conditions & MeSH terms

Clinical Trial Details

NCT number	NCT01870258
Study type	Interventional
Source	Shiraz University of Medical Sciences
Contact
Status	Completed
Phase	N/A
Start date	January 2011
Completion date	June 2012

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