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Genotype-Specific ECG-Based Risk Stratification Approaches in Patients With Long-QT Syndrome
BACKGROUND: Congenital long-QT syndrome (LQTS) is a major cause of sudden cardiac death (SCD) in young individuals, calling for sophisticated risk assessment. Risk stratification, however, is challenging as the individual arrhythmic risk varies pronouncedly, even in individuals carrying the same var...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9329832/ https://www.ncbi.nlm.nih.gov/pubmed/35911527 http://dx.doi.org/10.3389/fcvm.2022.916036 |
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author | Rieder, Marina Kreifels, Paul Stuplich, Judith Ziupa, David Servatius, Helge Nicolai, Luisa Castiglione, Alessandro Zweier, Christiane Asatryan, Babken Odening, Katja E. |
author_facet | Rieder, Marina Kreifels, Paul Stuplich, Judith Ziupa, David Servatius, Helge Nicolai, Luisa Castiglione, Alessandro Zweier, Christiane Asatryan, Babken Odening, Katja E. |
author_sort | Rieder, Marina |
collection | PubMed |
description | BACKGROUND: Congenital long-QT syndrome (LQTS) is a major cause of sudden cardiac death (SCD) in young individuals, calling for sophisticated risk assessment. Risk stratification, however, is challenging as the individual arrhythmic risk varies pronouncedly, even in individuals carrying the same variant. MATERIALS AND METHODS: In this study, we aimed to assess the association of different electrical parameters with the genotype and the symptoms in patients with LQTS. In addition to the heart-rate corrected QT interval (QTc), markers for regional electrical heterogeneity, such as QT dispersion (QT(max)-QT(min) in all ECG leads) and delta T(peak/end) (T(peak/end) V5 – T(peak/end) V2), were assessed in the 12-lead ECG at rest and during exercise testing. RESULTS: QTc at rest was significantly longer in symptomatic than asymptomatic patients with LQT2 (493.4 ms ± 46.5 ms vs. 419.5 ms ± 28.6 ms, p = 0.004), but surprisingly not associated with symptoms in LQT1. In contrast, post-exercise QTc (minute 4 of recovery) was significantly longer in symptomatic than asymptomatic patients with LQT1 (486.5 ms ± 7.0 ms vs. 463.3 ms ± 16.3 ms, p = 0.04), while no such difference was observed in patients with LQT2. Enhanced delta T(peak/end) and QT dispersion were only associated with symptoms in LQT1 (delta T(peak/end) 19.0 ms ± 18.1 ms vs. −4.0 ms ± 4.4 ms, p = 0.02; QT-dispersion: 54.3 ms ± 10.2 ms vs. 31.4 ms ± 10.4 ms, p = 0.01), but not in LQT2. Delta T(peak/end) was particularly discriminative after exercise, where all symptomatic patients with LQT1 had positive and all asymptomatic LQT1 patients had negative values (11.8 ± 7.9 ms vs. −7.5 ± 1.7 ms, p = 0.003). CONCLUSION: Different electrical parameters can distinguish between symptomatic and asymptomatic patients in different genetic forms of LQTS. While the classical “QTc at rest” was only associated with symptoms in LQT2, post-exercise QTc helped distinguish between symptomatic and asymptomatic patients with LQT1. Enhanced regional electrical heterogeneity was only associated with symptoms in LQT1, but not in LQT2. Our findings indicate that genotype-specific risk stratification approaches based on electrical parameters could help to optimize risk assessment in LQTS. |
format | Online Article Text |
id | pubmed-9329832 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-93298322022-07-29 Genotype-Specific ECG-Based Risk Stratification Approaches in Patients With Long-QT Syndrome Rieder, Marina Kreifels, Paul Stuplich, Judith Ziupa, David Servatius, Helge Nicolai, Luisa Castiglione, Alessandro Zweier, Christiane Asatryan, Babken Odening, Katja E. Front Cardiovasc Med Cardiovascular Medicine BACKGROUND: Congenital long-QT syndrome (LQTS) is a major cause of sudden cardiac death (SCD) in young individuals, calling for sophisticated risk assessment. Risk stratification, however, is challenging as the individual arrhythmic risk varies pronouncedly, even in individuals carrying the same variant. MATERIALS AND METHODS: In this study, we aimed to assess the association of different electrical parameters with the genotype and the symptoms in patients with LQTS. In addition to the heart-rate corrected QT interval (QTc), markers for regional electrical heterogeneity, such as QT dispersion (QT(max)-QT(min) in all ECG leads) and delta T(peak/end) (T(peak/end) V5 – T(peak/end) V2), were assessed in the 12-lead ECG at rest and during exercise testing. RESULTS: QTc at rest was significantly longer in symptomatic than asymptomatic patients with LQT2 (493.4 ms ± 46.5 ms vs. 419.5 ms ± 28.6 ms, p = 0.004), but surprisingly not associated with symptoms in LQT1. In contrast, post-exercise QTc (minute 4 of recovery) was significantly longer in symptomatic than asymptomatic patients with LQT1 (486.5 ms ± 7.0 ms vs. 463.3 ms ± 16.3 ms, p = 0.04), while no such difference was observed in patients with LQT2. Enhanced delta T(peak/end) and QT dispersion were only associated with symptoms in LQT1 (delta T(peak/end) 19.0 ms ± 18.1 ms vs. −4.0 ms ± 4.4 ms, p = 0.02; QT-dispersion: 54.3 ms ± 10.2 ms vs. 31.4 ms ± 10.4 ms, p = 0.01), but not in LQT2. Delta T(peak/end) was particularly discriminative after exercise, where all symptomatic patients with LQT1 had positive and all asymptomatic LQT1 patients had negative values (11.8 ± 7.9 ms vs. −7.5 ± 1.7 ms, p = 0.003). CONCLUSION: Different electrical parameters can distinguish between symptomatic and asymptomatic patients in different genetic forms of LQTS. While the classical “QTc at rest” was only associated with symptoms in LQT2, post-exercise QTc helped distinguish between symptomatic and asymptomatic patients with LQT1. Enhanced regional electrical heterogeneity was only associated with symptoms in LQT1, but not in LQT2. Our findings indicate that genotype-specific risk stratification approaches based on electrical parameters could help to optimize risk assessment in LQTS. Frontiers Media S.A. 2022-07-14 /pmc/articles/PMC9329832/ /pubmed/35911527 http://dx.doi.org/10.3389/fcvm.2022.916036 Text en Copyright © 2022 Rieder, Kreifels, Stuplich, Ziupa, Servatius, Nicolai, Castiglione, Zweier, Asatryan and Odening. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Cardiovascular Medicine Rieder, Marina Kreifels, Paul Stuplich, Judith Ziupa, David Servatius, Helge Nicolai, Luisa Castiglione, Alessandro Zweier, Christiane Asatryan, Babken Odening, Katja E. Genotype-Specific ECG-Based Risk Stratification Approaches in Patients With Long-QT Syndrome |
title | Genotype-Specific ECG-Based Risk Stratification Approaches in Patients With Long-QT Syndrome |
title_full | Genotype-Specific ECG-Based Risk Stratification Approaches in Patients With Long-QT Syndrome |
title_fullStr | Genotype-Specific ECG-Based Risk Stratification Approaches in Patients With Long-QT Syndrome |
title_full_unstemmed | Genotype-Specific ECG-Based Risk Stratification Approaches in Patients With Long-QT Syndrome |
title_short | Genotype-Specific ECG-Based Risk Stratification Approaches in Patients With Long-QT Syndrome |
title_sort | genotype-specific ecg-based risk stratification approaches in patients with long-qt syndrome |
topic | Cardiovascular Medicine |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9329832/ https://www.ncbi.nlm.nih.gov/pubmed/35911527 http://dx.doi.org/10.3389/fcvm.2022.916036 |
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