The development and validation of an easy to use automatic QT-interval algorithm

Ben J. M. Hermans, Arja S. Vink, Frank C. Bennis, Luc H. Filippini, Veronique M. F. Meijborg, Arthur A. M. Wilde, Laurent Pison, Pieter G. Postema, Tammo Delhaas*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

15 Citations (Web of Science)

Abstract

Background

To evaluate QT-interval dynamics in patients and in drug safety analysis, beat-to-beat QT-interval measurements are increasingly used. However, interobserver differences, aberrant T-wave morphologies and changes in heart axis might hamper accurate QT-interval measurements.

Objective

To develop and validate a QT-interval algorithm robust to heart axis orientation and T-wave morphology that can be applied on a beat-to-beat basis.

Methods

Additionally to standard ECG leads, the root mean square (ECG(RMS)), standard deviation and vectorcardiogram were used. QRS-onset was defined from the ECGRMS. T-wave end was defined per individual lead and scalar ECG using an automated tangent method. A median of all T-wave ends was used as the general T-wave end per beat.

Supine-standing tests of 73 patients with Long-QT syndrome (LQTS) and 54 controls were used because they have wide ranges of RR and QT-intervals as well as changes in T-wave morphology and heart axis orientation. For each subject, automatically estimated QT-intervals in three random complexes chosen from the low, middle and high RR range, were compared with manually measured QT-intervals by three observers.

Results

After visual inspection of the randomly selected complexes, 21 complexes were excluded because of evident noise, too flat T-waves or premature ventricular beats. Bland-Altman analyses of automatically and manually determined QT-intervals showed a bias of <4ms and limits of agreement of +/- 25ms. Intra-class coefficient indicated excellent agreement (> 0.9) between the algorithm and all observers individually as well as between the algorithm and the mean QT-interval of the observers.

Conclusion

Our automated algorithm provides reliable beat-to-beat QT-interval assessment, robust to heart axis and T-wave morphology.

Original languageEnglish
Article number0184352
Number of pages14
JournalPLOS ONE
Volume12
Issue number9
DOIs
Publication statusPublished - 1 Sep 2017

Keywords

  • LONG-QT
  • VARIABILITY
  • DISPERSION
  • TANGENT
  • SIGNALS
  • LEADS

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