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

• Published in: PloS one Vol. 12; no. 9; p. e0184352
• Main Authors: Hermans, Ben J. M., Vink, Arja S., Bennis, Frank C., Filippini, Luc H., Meijborg, Veronique M. F., Wilde, Arthur A. M., Pison, Laurent, Postema, Pieter G., Delhaas, Tammo
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.09.2017; Public Library of Science (PLoS)
• Subjects: Aberration; Adult; Aged; Algorithms; Analysis; Arrhythmias, Cardiac - diagnosis; Arrhythmias, Cardiac - physiopathology; Automation; Biology and Life Sciences; Cardiac arrhythmia; Diagnosis; Dynamic tests; EKG; Electrocardiography; Electrocardiography - methods; Female; Heart; Heart - physiology; Heart Conduction System - physiopathology; Heart Rate; Humans; Inspection; Intervals; Long QT syndrome; Long QT Syndrome - diagnosis; Long QT Syndrome - physiopathology; Male; Medical diagnosis; Medicine and Health Sciences; Middle Aged; Models, Statistical; Morphology; Observers; Orientation; Orientation behavior; Patient Safety; Patients; Pattern Recognition, Automated; Pharmacovigilance; Physical Sciences; Reproducibility of Results; Research and Analysis Methods; Risk factors; Signal processing; Signal Processing, Computer-Assisted; Ventricle; Visual observation; Visual perception; Young Adult; Netherlands
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0184352

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. 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. Additionally to standard ECG leads, the root mean square (ECGRMS), 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. 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. Our automated algorithm provides reliable beat-to-beat QT-interval assessment, robust to heart axis and T-wave morphology.