Z-score for benchmarking reader competence in a central ECG laboratory

Ann Noninvasive Electrocardiol. 2009 Jan;14(1):19-25. doi: 10.1111/j.1542-474X.2008.00269.x.


Background: ECGs from thorough QT studies must be read in a central laboratory by trained experts. Standards of expertise are not presently defined. We, therefore, studied the use of Z-scores to define reader competence.

Methods: Two hundred ECGs were read by 24 experts and the mean and standard deviation (SD) of QT measurements calculated for each ECG. Z-scores ([QT(reader)- mean QT(experts)]/ SD(experts)) for each ECG and mean of absolute Z-scores of all ECGs read by a reader were calculated. The highest mean absolute Z-score of experts was considered the cutoff to define competence. Hundred of these standardized ECGs were used to assess performance of readers from the central laboratory.

Results: All experts had mean absolute Z-scores < or = 1.5. Using this cutoff, one of 28 experienced readers and 7 of 15 trainees had unacceptable Z-scores. After re-training, all achieved Z-scores <1.5. Comparing histograms of actual Z-scores of the 100 ECGs of readers with unacceptable scores with that of the reader with the best Z-score showed two patterns. Readers with histograms having a peak and tails similar to that of the best reader, but with leftward or rightward shift, consistently made shorter or longer QT measurements, respectively. A histogram with a flatter peak and wider tails, suggested that measurements were long in some ECGs and short in others.

Conclusion: Mean absolute Z-score is useful to assess competence for measuring the QT interval on ECGs. Analysis of histograms can pinpoint problems in QT measurements.

Publication types

  • Comparative Study
  • Validation Study

MeSH terms

  • Benchmarking / methods*
  • Cardiology*
  • Clinical Competence*
  • Electrocardiography / methods*
  • Female
  • Humans
  • India
  • Laboratories*
  • Long QT Syndrome / diagnosis*
  • Male
  • Observer Variation
  • Sensitivity and Specificity
  • Signal Processing, Computer-Assisted
  • Total Quality Management