Errors in the computerized electrocardiogram interpretation of cardiac rhythm

J Electrocardiol. Sep-Oct 2007;40(5):385-90. doi: 10.1016/j.jelectrocard.2007.03.008. Epub 2007 May 24.

Abstract

Background: More than 100 million computer-interpreted electrocardiograms (ECG-C) are obtained annually. However, there are few contemporary published data on the accuracy of cardiac rhythm interpretation by this method.

Purpose: The purpose of this study is to determine the accuracy of ECG-C rhythm interpretation in a typical patient population.

Methods: We compared the ECG-C rhythm interpretation to that of 2 expert overreaders in 2112 randomly selected standard 12-lead ECGs.

Results: The ECG-C correctly interpreted the rhythm in 1858 and incorrectly identified the rhythm in 254 (overall accuracy, 88.0%). Sinus rhythm was correctly interpreted in 95.0% of the ECGs (1666/1753) with this rhythm, whereas nonsinus rhythms were correctly interpreted with an accuracy of only 53.5% (192/359) (P < .0001). The ECG-C interpreted sinus rhythm with a sensitivity of 95% (confidence interval, 93.8-96.7), specificity of 66.3%, and positive predictive value of 93.2%. The ECG-C interpreted nonsinus rhythms with a sensitivity of 72%, (confidence interval, 68.7-73.7), a specificity of 93%, and a positive predictive value of 59.3%. Of the 254 ECGs that had incorrect rhythm interpretation, additional major errors were noted in 137 (54%).

Conclusions: The ECG-C demonstrates frequent errors in the interpretation of nonsinus rhythms. In addition, incorrect rhythm interpretation by the ECG-C was frequently further compounded by additional major inaccuracies. Expert overreading of the ECG remains important in clinical settings with a high percentage of nonsinus rhythms.

MeSH terms

  • Arrhythmias, Cardiac / diagnosis*
  • Arrhythmias, Cardiac / epidemiology
  • Artifacts*
  • California
  • Diagnosis, Computer-Assisted / methods*
  • Electrocardiography / methods*
  • Electrocardiography / statistics & numerical data*
  • Humans
  • Observer Variation
  • Quality Assurance, Health Care / methods*
  • Reproducibility of Results
  • Retrospective Studies
  • Sensitivity and Specificity