Improving long QT syndrome diagnosis by a polynomial-based T-wave morphology characterization.
Heart Rhythm
; 17(5 Pt A): 752-758, 2020 05.
Article
en En
| MEDLINE
| ID: mdl-31917370
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.Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Algoritmos
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Síndrome de QT Prolongado
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Electrocardiografía
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Aprendizaje Automático
Tipo de estudio:
Diagnostic_studies
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Observational_studies
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Prognostic_studies
Límite:
Adult
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Revista:
Heart Rhythm
Año:
2020
Tipo del documento:
Article