1. Improving long QT syndrome diagnosis by a polynomial-based T-wave morphology characterization
- Author
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Aurore Lyon, Tijmen Koopsen, Arthur A.M. Wilde, Tammo Delhaas, Dieter Nuyens, Arja S. Vink, Pieter G. Postema, Ben J. M. Hermans, Tomas Robyns, Laurent Pison, Frank C. Bennis, RS: Carim - H08 Experimental atrial fibrillation, Biomedische Technologie, Promovendi MHN, RS: MHeNs - R3 - Neuroscience, RS: Carim - H07 Cardiovascular System Dynamics, Graduate School, Paediatric Cardiology, ACS - Heart failure & arrhythmias, Amsterdam Cardiovascular Sciences, and Cardiology
- Subjects
Adult ,Male ,medicine.medical_specialty ,Polynomial ,congenital, hereditary, and neonatal diseases and abnormalities ,Genotype ,Long QT syndrome ,Precordial examination ,030204 cardiovascular system & hematology ,FORMS ,QT interval ,03 medical and health sciences ,Electrocardiography ,0302 clinical medicine ,Physiology (medical) ,Internal medicine ,Diagnosis ,Machine learning ,medicine ,Humans ,INTERVAL ,030212 general & internal medicine ,cardiovascular diseases ,Retrospective Studies ,T-wave morphology ,business.industry ,Healthy subjects ,Reproducibility of Results ,Retrospective cohort study ,Signal Processing, Computer-Assisted ,QT ,Middle Aged ,medicine.disease ,Long QT Syndrome ,T wave morphology ,Cohort ,Cardiology ,LQTS ,Female ,Cardiology and Cardiovascular Medicine ,business ,Algorithms ,Follow-Up Studies - Abstract
BACKGROUND Diagnosing long QT syndrome (LQTS) remains challenging because of a considerable overlap in QT interval between patients with LQTS and healthy subjects. Characterizing T-wave morphology might improve LQTS diagnosis.OBJECTIVE The purpose of this study was to improve LQTS diagnosis by combining new polynomial-based T-wave morphology parameters with the corrected QT interval (QTc), age, and sex in a model.METHODS A retrospective cohort consisting of 333 patients with LQTS and 345 genotype-negative family members was used in this study. For each patient, a linear combination of the first 2 Hermite-Gauss (HG) polynomials was fitted to the STT segments of an average complex of all precordial leads and limb leads I and II. The weight coefficients as well as the error of the best fit were used to characterize T-wave morphology. Subjects were classified as patients with LQTS or controls by clinical QTc cutoffs and 3 support vector machine models fed with different features. An external cohort consisting of 72 patients and 45 controls was finally used to check the robustness of the models.RESULTS Baseline QTc cutoffs were specific but had low sensitivity in diagnosing LQTS. The model with T-wave morphology features, QTc, age, and sex had the best overall accuracy (84%), followed by a model with QTc, age, and sex (79%). The model with T-wave morphology features especially performed better in LQTS type 3 patients (69%).CONCLUSION T-wave morphologies can be characterized by fitting a linear combination of the first 2 Hermite-Gauss polynomials. Adding T-wave morphology characterization to age, sex, and QTc in a support vector machine model improves LQTS diagnosis.
- Published
- 2020