Genotype-Specific ECG-Based Risk Stratification Approaches in Patients With Long-QT Syndrome
BackgroundCongenital 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 varia...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-07-01
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| Series: | Frontiers in Cardiovascular Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fcvm.2022.916036/full |
| _version_ | 1797867196093825024 |
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| author | Marina Rieder Paul Kreifels Judith Stuplich David Ziupa Helge Servatius Luisa Nicolai Alessandro Castiglione Christiane Zweier Babken Asatryan Katja E. Odening Katja E. Odening |
| author_facet | Marina Rieder Paul Kreifels Judith Stuplich David Ziupa Helge Servatius Luisa Nicolai Alessandro Castiglione Christiane Zweier Babken Asatryan Katja E. Odening Katja E. Odening |
| author_sort | Marina Rieder |
| collection | DOAJ |
| description | BackgroundCongenital 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 MethodsIn 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 (QTmax-QTmin in all ECG leads) and delta Tpeak/end (Tpeak/end V5 – Tpeak/end V2), were assessed in the 12-lead ECG at rest and during exercise testing.ResultsQTc 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 Tpeak/end and QT dispersion were only associated with symptoms in LQT1 (delta Tpeak/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 Tpeak/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).ConclusionDifferent 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. |
| first_indexed | 2024-04-09T23:36:28Z |
| format | Article |
| id | doaj.art-a7ec6a5adbf2435fa5a35fa786ea9d82 |
| institution | Directory Open Access Journal |
| issn | 2297-055X |
| language | English |
| last_indexed | 2024-04-09T23:36:28Z |
| publishDate | 2022-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Cardiovascular Medicine |
| spelling | doaj.art-a7ec6a5adbf2435fa5a35fa786ea9d822023-03-20T10:23:40ZengFrontiers Media S.A.Frontiers in Cardiovascular Medicine2297-055X2022-07-01910.3389/fcvm.2022.916036916036Genotype-Specific ECG-Based Risk Stratification Approaches in Patients With Long-QT SyndromeMarina Rieder0Paul Kreifels1Judith Stuplich2David Ziupa3Helge Servatius4Luisa Nicolai5Alessandro Castiglione6Christiane Zweier7Babken Asatryan8Katja E. Odening9Katja E. Odening10Translational Cardiology, Department of Cardiology, Inselspital, University Hospital Bern, University of Bern, Bern, SwitzerlandDepartment of Cardiology and Angiology I, Faculty of Medicine, University Heart Center Freiburg-Bad Krozingen, University of Freiburg, Freiburg, GermanyDepartment of Cardiology and Angiology I, Faculty of Medicine, University Heart Center Freiburg-Bad Krozingen, University of Freiburg, Freiburg, GermanyDepartment of Cardiology and Angiology I, Faculty of Medicine, University Heart Center Freiburg-Bad Krozingen, University of Freiburg, Freiburg, GermanyTranslational Cardiology, Department of Cardiology, Inselspital, University Hospital Bern, University of Bern, Bern, SwitzerlandTranslational Cardiology, Department of Cardiology, Inselspital, University Hospital Bern, University of Bern, Bern, SwitzerlandTranslational Cardiology, Department of Cardiology, Inselspital, University Hospital Bern, University of Bern, Bern, SwitzerlandDepartment of Human Genetics, Inselspital, Bern University Hospital, University of Bern, Bern, SwitzerlandTranslational Cardiology, Department of Cardiology, Inselspital, University Hospital Bern, University of Bern, Bern, SwitzerlandTranslational Cardiology, Department of Cardiology, Inselspital, University Hospital Bern, University of Bern, Bern, SwitzerlandDepartment of Physiology, University of Bern, Bern, SwitzerlandBackgroundCongenital 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 MethodsIn 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 (QTmax-QTmin in all ECG leads) and delta Tpeak/end (Tpeak/end V5 – Tpeak/end V2), were assessed in the 12-lead ECG at rest and during exercise testing.ResultsQTc 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 Tpeak/end and QT dispersion were only associated with symptoms in LQT1 (delta Tpeak/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 Tpeak/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).ConclusionDifferent 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.https://www.frontiersin.org/articles/10.3389/fcvm.2022.916036/fulllong-QT syndromegenetic arrhythmia disordersrisk stratificationQTcelectrocardiogram |
| spellingShingle | Marina Rieder Paul Kreifels Judith Stuplich David Ziupa Helge Servatius Luisa Nicolai Alessandro Castiglione Christiane Zweier Babken Asatryan Katja E. Odening Katja E. Odening Genotype-Specific ECG-Based Risk Stratification Approaches in Patients With Long-QT Syndrome Frontiers in Cardiovascular Medicine long-QT syndrome genetic arrhythmia disorders risk stratification QTc electrocardiogram |
| 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 | long-QT syndrome genetic arrhythmia disorders risk stratification QTc electrocardiogram |
| url | https://www.frontiersin.org/articles/10.3389/fcvm.2022.916036/full |
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