Diagnosis of acute myocardial ischaemia using probabilistic methods

J Cardiovasc Risk. 2002 Apr;9(2):115-21. doi: 10.1177/174182670200900207.

Abstract

Background: The present study was undertaken to test the effectiveness of an established equation for estimating the probability of acute cardiac ischaemia, but in an environment different from that in which it was developed.

Methods and results: A total of 255 patients who presented to the accident and emergency department of Glasgow Royal Infirmary with symptoms suggesting acute ischaemic heart disease were enrolled in the study. Their clinical data and ECG measurements were used as input to the Time-Insensitive Predictive Instrument (TIPI) equation to calculate the probability of acute myocardial ischaemia being present. Receiver operating characteristic (ROC) curves were derived to evaluate the usefulness of the equation in diagnosing acute myocardial infarction versus nonmyocardial infarction and acute cardiac ischaemia verses nonischaemic chest pain. For the diagnosis of acute cardiac ischaemia versus noncardiac chest pain, the area under the ROC curve was 0.61 (95% CI 0.55-0.67). For the diagnosis of acute myocardial infarction versus nonmyocardial infarction, there was increased performance with the area under the ROC curve being 0.71 (95% CI 0.65-0.76). This compares with the originally published findings of a value of 0.88 for the area under the curve for the diagnosis of acute myocardial ischaemia versus noncardiac chest pain.

Conclusion: The study suggests that equations for the diagnosis of acute cardiac ischaemia developed in one centre may not readily translate elsewhere, possibly due to varying interpretations of the clinical and ECG criteria used.

Publication types

  • Comparative Study
  • Evaluation Study

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Algorithms
  • Electrocardiography
  • Female
  • Humans
  • Male
  • Middle Aged
  • Models, Statistical*
  • Myocardial Ischemia / diagnosis*
  • Myocardial Ischemia / epidemiology
  • Prevalence
  • ROC Curve
  • Scotland / epidemiology
  • Sensitivity and Specificity