Improving long QT syndrome diagnosis by a polynomial-based T-wave morphology characterization.
Diagnosis
LQTS
Machine learning
QT
T-wave morphology
Journal
Heart rhythm
ISSN: 1556-3871
Titre abrégé: Heart Rhythm
Pays: United States
ID NLM: 101200317
Informations de publication
Date de publication:
05 2020
05 2020
Historique:
received:
16
09
2019
accepted:
29
12
2019
pubmed:
10
1
2020
medline:
24
6
2021
entrez:
10
1
2020
Statut:
ppublish
Résumé
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. 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. 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. 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%). 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.
Sections du résumé
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.
Identifiants
pubmed: 31917370
pii: S1547-5271(20)30001-1
doi: 10.1016/j.hrthm.2019.12.020
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
752-758Informations de copyright
Copyright © 2020 Heart Rhythm Society. Published by Elsevier Inc. All rights reserved.