Early Diagnosis Of Acute Myocardial Infarction

Using Clinical And Electrocardiographic Data

at Presentation: Derivation And Evaluation Of

Logistic Regression Models

European Heart Journal, Vol 17, August 1996, p 1181 - 1191

R.L. Kennedy, A.M. Burton, H.S. Fraser, L.N. McStay, R.F. Harrison

Abstract

Our aim was to determine which, and how many, data items are required to construct a decision support algorithm for early diagnosis of AMI using clinical and electrocardiographic data available at presentation. Logistic regression models were derived using data items from 600 consecutive patients at one centre (Edinburgh), then tested prospectively on 510 cases from the same centre and 662 consecutive cases from another centre (Sheffield). Although performance of the models increased with progressive addition of data inputs when applied to training data, a simple 6 factor model was the most effective on test data, yielding accuracy's of 84.3% and 83.6% on the two test sets. A model constructed solely of electrocardiographic data performed nearly as well as those incorporating clinical data. Previously published logistic regression models did not perform so well as the models derived from data collected for this study.