Vectorcardiography identifies patients with electrocardiographically concealed long QT syndrome

Daniel Cortez, J. Martijn Bos, Michael John Ackerman

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Background Long QT syndrome (LQTS) and genotypic subtypes are associated with distinctive T-wave patterns, arrhythmogenic triggers, and corrected QT (QTc) interval risk associations. Twenty percent of patients with LQTS have normal QTc values, defined as electrographically concealed LQTS (ecLQTS). Vectorcardiography (VCG) has value for sudden cardiac death risk assessment. Objective The purpose of this study was to determine the use of VCG to identify patients with ecLQTS. Methods We performed a retrospective analysis in patients with ecLQTS with resting QTc values <440 ms. Computerized derivation of the spatial mean and peak QRS-T angles, QTpeak, Tpeak-Tend (angle between QRS and T-wave peak amplitudes in 3-dimensional space), and T-wave eigenvalues (TwEVs; amplitudes [in microvolts] for each of the first 4 TwEVs were derived from the 12-lead electrocardiogram) was performed. The results were compared with those for healthy controls. Intergenotype differences were analyzed. Results Of 610 patients with LQTS, 169 patients (28%) had ecLQTS (86 (51%) men; mean age 22 ± 16 years; mean QTc interval 422 ± 14 ms). There were 519 healthy controls (44% men; mean age 19.8 ± 13.8 years) with a mean QTc interval of 426 ± 28 ms. Among VCG parameters, QTpeak and TwEVs significantly differentiated patients with ecLQTS from controls (P ≤.01 for each) as well as differentiated KCNQ1-encoded type 1 LQTS (ecLQT1), KCNH2-encoded type 2 LQTS (ecLQT2), and SCN5A-encoded type 3 LQTS (ecLQT3) from controls (P <.01). ecLQT3 was differentiated from controls and ecLQT1 and ecLQT2 by the fourth TwEV (P <.01 for each). The fourth TwEV differentiated symptomatic patients with ecLQTS from asymptomatic patients with ecLQTS (P <.01). Conclusion ecLQTS can be distinguished from controls using QTpeak. ecLQT3 was best differentiated by the fourth TwEV. VCG may facilitate familial diagnostic anticipation of LQTS status before the completion of mutation-specific genetic testing even with normal resting QTc values.

Original languageEnglish (US)
Pages (from-to)894-899
Number of pages6
JournalHeart Rhythm
Volume14
Issue number6
DOIs
StatePublished - Jun 1 2017

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Vectorcardiography
Long QT Syndrome
Romano-Ward Syndrome
Sudden Cardiac Death
Genetic Testing
Electrocardiography
Reference Values
Mutation

Keywords

  • Electrocardiographically concealed long QT syndrome (ecLQTS)
  • QTpeak
  • Spatial QRS-T angle
  • T-wave eigenvector
  • Tpeak-Tend

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine
  • Physiology (medical)

Cite this

Vectorcardiography identifies patients with electrocardiographically concealed long QT syndrome. / Cortez, Daniel; Bos, J. Martijn; Ackerman, Michael John.

In: Heart Rhythm, Vol. 14, No. 6, 01.06.2017, p. 894-899.

Research output: Contribution to journalArticle

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abstract = "Background Long QT syndrome (LQTS) and genotypic subtypes are associated with distinctive T-wave patterns, arrhythmogenic triggers, and corrected QT (QTc) interval risk associations. Twenty percent of patients with LQTS have normal QTc values, defined as electrographically concealed LQTS (ecLQTS). Vectorcardiography (VCG) has value for sudden cardiac death risk assessment. Objective The purpose of this study was to determine the use of VCG to identify patients with ecLQTS. Methods We performed a retrospective analysis in patients with ecLQTS with resting QTc values <440 ms. Computerized derivation of the spatial mean and peak QRS-T angles, QTpeak, Tpeak-Tend (angle between QRS and T-wave peak amplitudes in 3-dimensional space), and T-wave eigenvalues (TwEVs; amplitudes [in microvolts] for each of the first 4 TwEVs were derived from the 12-lead electrocardiogram) was performed. The results were compared with those for healthy controls. Intergenotype differences were analyzed. Results Of 610 patients with LQTS, 169 patients (28{\%}) had ecLQTS (86 (51{\%}) men; mean age 22 ± 16 years; mean QTc interval 422 ± 14 ms). There were 519 healthy controls (44{\%} men; mean age 19.8 ± 13.8 years) with a mean QTc interval of 426 ± 28 ms. Among VCG parameters, QTpeak and TwEVs significantly differentiated patients with ecLQTS from controls (P ≤.01 for each) as well as differentiated KCNQ1-encoded type 1 LQTS (ecLQT1), KCNH2-encoded type 2 LQTS (ecLQT2), and SCN5A-encoded type 3 LQTS (ecLQT3) from controls (P <.01). ecLQT3 was differentiated from controls and ecLQT1 and ecLQT2 by the fourth TwEV (P <.01 for each). The fourth TwEV differentiated symptomatic patients with ecLQTS from asymptomatic patients with ecLQTS (P <.01). Conclusion ecLQTS can be distinguished from controls using QTpeak. ecLQT3 was best differentiated by the fourth TwEV. VCG may facilitate familial diagnostic anticipation of LQTS status before the completion of mutation-specific genetic testing even with normal resting QTc values.",
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AU - Cortez, Daniel

AU - Bos, J. Martijn

AU - Ackerman, Michael John

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N2 - Background Long QT syndrome (LQTS) and genotypic subtypes are associated with distinctive T-wave patterns, arrhythmogenic triggers, and corrected QT (QTc) interval risk associations. Twenty percent of patients with LQTS have normal QTc values, defined as electrographically concealed LQTS (ecLQTS). Vectorcardiography (VCG) has value for sudden cardiac death risk assessment. Objective The purpose of this study was to determine the use of VCG to identify patients with ecLQTS. Methods We performed a retrospective analysis in patients with ecLQTS with resting QTc values <440 ms. Computerized derivation of the spatial mean and peak QRS-T angles, QTpeak, Tpeak-Tend (angle between QRS and T-wave peak amplitudes in 3-dimensional space), and T-wave eigenvalues (TwEVs; amplitudes [in microvolts] for each of the first 4 TwEVs were derived from the 12-lead electrocardiogram) was performed. The results were compared with those for healthy controls. Intergenotype differences were analyzed. Results Of 610 patients with LQTS, 169 patients (28%) had ecLQTS (86 (51%) men; mean age 22 ± 16 years; mean QTc interval 422 ± 14 ms). There were 519 healthy controls (44% men; mean age 19.8 ± 13.8 years) with a mean QTc interval of 426 ± 28 ms. Among VCG parameters, QTpeak and TwEVs significantly differentiated patients with ecLQTS from controls (P ≤.01 for each) as well as differentiated KCNQ1-encoded type 1 LQTS (ecLQT1), KCNH2-encoded type 2 LQTS (ecLQT2), and SCN5A-encoded type 3 LQTS (ecLQT3) from controls (P <.01). ecLQT3 was differentiated from controls and ecLQT1 and ecLQT2 by the fourth TwEV (P <.01 for each). The fourth TwEV differentiated symptomatic patients with ecLQTS from asymptomatic patients with ecLQTS (P <.01). Conclusion ecLQTS can be distinguished from controls using QTpeak. ecLQT3 was best differentiated by the fourth TwEV. VCG may facilitate familial diagnostic anticipation of LQTS status before the completion of mutation-specific genetic testing even with normal resting QTc values.

AB - Background Long QT syndrome (LQTS) and genotypic subtypes are associated with distinctive T-wave patterns, arrhythmogenic triggers, and corrected QT (QTc) interval risk associations. Twenty percent of patients with LQTS have normal QTc values, defined as electrographically concealed LQTS (ecLQTS). Vectorcardiography (VCG) has value for sudden cardiac death risk assessment. Objective The purpose of this study was to determine the use of VCG to identify patients with ecLQTS. Methods We performed a retrospective analysis in patients with ecLQTS with resting QTc values <440 ms. Computerized derivation of the spatial mean and peak QRS-T angles, QTpeak, Tpeak-Tend (angle between QRS and T-wave peak amplitudes in 3-dimensional space), and T-wave eigenvalues (TwEVs; amplitudes [in microvolts] for each of the first 4 TwEVs were derived from the 12-lead electrocardiogram) was performed. The results were compared with those for healthy controls. Intergenotype differences were analyzed. Results Of 610 patients with LQTS, 169 patients (28%) had ecLQTS (86 (51%) men; mean age 22 ± 16 years; mean QTc interval 422 ± 14 ms). There were 519 healthy controls (44% men; mean age 19.8 ± 13.8 years) with a mean QTc interval of 426 ± 28 ms. Among VCG parameters, QTpeak and TwEVs significantly differentiated patients with ecLQTS from controls (P ≤.01 for each) as well as differentiated KCNQ1-encoded type 1 LQTS (ecLQT1), KCNH2-encoded type 2 LQTS (ecLQT2), and SCN5A-encoded type 3 LQTS (ecLQT3) from controls (P <.01). ecLQT3 was differentiated from controls and ecLQT1 and ecLQT2 by the fourth TwEV (P <.01 for each). The fourth TwEV differentiated symptomatic patients with ecLQTS from asymptomatic patients with ecLQTS (P <.01). Conclusion ecLQTS can be distinguished from controls using QTpeak. ecLQT3 was best differentiated by the fourth TwEV. VCG may facilitate familial diagnostic anticipation of LQTS status before the completion of mutation-specific genetic testing even with normal resting QTc values.

KW - Electrocardiographically concealed long QT syndrome (ecLQTS)

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KW - T-wave eigenvector

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