Literature DB >> 27899947

Novel conduction-repolarization indices for the stratification of arrhythmic risk.

Gary Tse1.   

Abstract

Entities:  

Keywords:  Conduction; Depolarization; QRS; QT dispersion; Repolarization; Transmural dispersion of repolarization; Wavelength

Year:  2016        PMID: 27899947      PMCID: PMC5122508          DOI: 10.11909/j.issn.1671-5411.2016.09.008

Source DB:  PubMed          Journal:  J Geriatr Cardiol        ISSN: 1671-5411            Impact factor:   3.327


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Sudden cardiac death (SCD) affects approximately 800,000 individuals per annum globally.[1] It is most frequently due to cardiac tachy-arrhythmias, which include mono-morphic or polymorphic ventricular tachycardia (VT), torsade de pointes and ventricular fibrillation (VF). Risk stratification for SCD remains a challenging problem in clinical practice. Patients with structural heart disease or cardiac ion channelopathies have an increased risk of SCD, their risks are not the same. Consequently, several indices have been devised for this purpose, mainly focusing on ventricular repolarization, which is reflected by the QT interval on the electrocardiogram.[2] These include QT interval corrected for heart rate (QTc), QT dispersion (QTd), interval from the peak to the end of the T wave (Tpeak – Tend, reflecting increased transmural dispersion of repolarization, TDR), and (Tpeak – Tend)/QT ratio.[3] There are two major problems with these indices. Firstly, TDR has been shown to be a poor predictor of arrhythmogenicity in pre-clinical models even in disorders of repolarization, such as long QT and short QT syndromes. Secondly, none of the above indices takes into account the role of abnormal conduction in ventricular arrhythmogenesis. It may be reasonable to assume that abnormal conduction plays a minor role in disorders primarily affecting repolarization. However, it is not appropriate to do so in structural heart diseases such as heart failure or Brugada syndrome. In these conditions, reduced conduction velocities (CVs) are observed,[4] which manifest as prolonged QRS durations on the ECG.[5],[6] Reduced CV increases the risk of reentrant arrhythmias by shortening the excitation wavelength λ given by CV × effective refractory period, as demonstrated in pre-clinical experiments. However, a major disadvantage of λ is that it must be determined invasively by electrophysiological studies in humans. In view of this, there is a need for arrhythmic risk markers that take into account both conduction and repolarization. Recently, the index of Cardiac Electrophysiological Balance, iCEB, given by QT/QRS, was proposed.[7] This ratio can easily be derived from the electrocardiogram and is a good approximate of λ. Previous work has shown that Tpeak – Tend and (Tpeak – Tend)/QT are superior to the QT interval in predicting arrhythmic risk.[2] Thus, I recently proposed two novel indices, (Tpeak – Tend)/QRS and (Tpeak – Tend)/(QT × QRS), that might be able to better predict arrhythmic risk in Brugada syndrome.[8],[9] There is no reason why these cannot not be applied in other clinical conditions where conduction is abnormal, such as heart failure. The advantage of (Tpeak – Tend)/QRS is that it can be easily calculated and therefore sufficiently convenient for clinical use. (Tpeak – Tend)/(QT × QRS) is potentially more accurate for risk stratification for research purposes, but is too cumbersome to use by the bedside. Both indices are derived from electrophysiological findings that both conduction and repolarization abnormalities are important in arrhythmogenesis. The validity of these indices will require further study, but may ultimately provide superior predictive values than ventricular repolarization markers such as QTc, QTd, Tpeak – Tend or (Tpeak – Tend)/QT ratio. Animal models are useful for studying arrhythmogenic mechanisms and provide a platform for assessing the efficacy of pharmacological therapy.[10]–[16] Measurement of the magnetic field in the heart has been useful for characterizing cardiac structural abnormalities.[17]–[19] It can be used to diagnose and predict the risk of cardiac arrhythmias in clinical practice by magnetocardiography, which may provide helpful clinical markers for risk stratification in the future.[20]
  18 in total

Review 1.  Significance of QRS complex duration in patients with heart failure.

Authors:  Amir Kashani; S Serge Barold
Journal:  J Am Coll Cardiol       Date:  2005-12-20       Impact factor: 24.094

2.  (Tpeak - Tend)/QRS and (Tpeak - Tend)/(QT × QRS): novel markers for predicting arrhythmic risk in the Brugada syndrome.

Authors:  Gary Tse
Journal:  Europace       Date:  2017-04-01       Impact factor: 5.214

3.  Prolonged QRS duration in lead V2 and risk of life-threatening ventricular Arrhythmia in patients with Brugada syndrome.

Authors:  Kimie Ohkubo; Ichiro Watanabe; Yasuo Okumura; Sonoko Ashino; Masayoshi Kofune; Koichi Nagashima; Tatsuya Kofune; Toshiko Nakai; Satoshi Kunimoto; Yuji Kasamaki; Atsushi Hirayama
Journal:  Int Heart J       Date:  2011       Impact factor: 1.862

4.  Novel arrhythmic risk markers incorporating QRS dispersion: QRSd × (Tpeak - Tend )/QRS and QRSd × (Tpeak - Tend )/(QT × QRS).

Authors:  Gary Tse; Bryan P Yan
Journal:  Ann Noninvasive Electrocardiol       Date:  2016-08-18       Impact factor: 1.468

5.  A new biomarker--index of cardiac electrophysiological balance (iCEB)--plays an important role in drug-induced cardiac arrhythmias: beyond QT-prolongation and Torsades de Pointes (TdPs).

Authors:  Hua Rong Lu; Gan-Xin Yan; David J Gallacher
Journal:  J Pharmacol Toxicol Methods       Date:  2013-01-19       Impact factor: 1.950

6.  Development of a magnetocardiography-based algorithm for discrimination between ventricular arrhythmias originating from the right ventricular outflow tract and those originating from the aortic sinus cusp: a pilot study.

Authors:  Yoko Ito; Keisuke Shiga; Kentaro Yoshida; Kuniomi Ogata; Akihiko Kandori; Takeshi Inaba; Yoko Nakazawa; Yukio Sekiguchi; Hiroshi Tada; Kensuke Sekihara; Kazutaka Aonuma
Journal:  Heart Rhythm       Date:  2014-06-02       Impact factor: 6.343

Review 7.  Electrophysiological Mechanisms of Bayés Syndrome: Insights from Clinical and Mouse Studies.

Authors:  Gary Tse; Eric Tsz Him Lai; Jie Ming Yeo; Bryan P Yan
Journal:  Front Physiol       Date:  2016-05-31       Impact factor: 4.566

Review 8.  Reactive Oxygen Species, Endoplasmic Reticulum Stress and Mitochondrial Dysfunction: The Link with Cardiac Arrhythmogenesis.

Authors:  Gary Tse; Bryan P Yan; Yin W F Chan; Xiao Yu Tian; Yu Huang
Journal:  Front Physiol       Date:  2016-08-03       Impact factor: 4.566

Review 9.  Cardiac disease and arrhythmogenesis: Mechanistic insights from mouse models.

Authors:  Lois Choy; Jie Ming Yeo; Vivian Tse; Shing Po Chan; Gary Tse
Journal:  Int J Cardiol Heart Vasc       Date:  2016-09
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  17 in total

1.  Novel arrhythmic risk markers incorporating QRS dispersion: QRSd × (Tpeak - Tend )/QRS and QRSd × (Tpeak - Tend )/(QT × QRS).

Authors:  Gary Tse; Bryan P Yan
Journal:  Ann Noninvasive Electrocardiol       Date:  2016-08-18       Impact factor: 1.468

Review 2.  Electrophysiological Mechanisms of Bayés Syndrome: Insights from Clinical and Mouse Studies.

Authors:  Gary Tse; Eric Tsz Him Lai; Jie Ming Yeo; Bryan P Yan
Journal:  Front Physiol       Date:  2016-05-31       Impact factor: 4.566

Review 3.  What Is the Arrhythmic Substrate in Viral Myocarditis? Insights from Clinical and Animal Studies.

Authors:  Gary Tse; Jie M Yeo; Yin Wah Chan; Eric T H Lai Lai; Bryan P Yan
Journal:  Front Physiol       Date:  2016-07-21       Impact factor: 4.566

4.  Gap junction inhibition by heptanol increases ventricular arrhythmogenicity by reducing conduction velocity without affecting repolarization properties or myocardial refractoriness in Langendorff-perfused mouse hearts.

Authors:  Gary Tse; Jie Ming Yeo; Vivian Tse; Joseph Kwan; Bing Sun
Journal:  Mol Med Rep       Date:  2016-09-13       Impact factor: 2.952

Review 5.  Cardiac disease and arrhythmogenesis: Mechanistic insights from mouse models.

Authors:  Lois Choy; Jie Ming Yeo; Vivian Tse; Shing Po Chan; Gary Tse
Journal:  Int J Cardiol Heart Vasc       Date:  2016-09

6.  Animal models for the study of primary and secondary hypertension in humans.

Authors:  Hiu Yu Lin; Yee Ting Lee; Yin Wah Chan; Gary Tse
Journal:  Biomed Rep       Date:  2016-10-18

7.  Spontaneous type 1 pattern, ventricular arrhythmias and sudden cardiac death in Brugada Syndrome: an updated systematic review and meta-analysis.

Authors:  Ahmed Bayoumy; Meng-Qi Gong; Ka Hou Christien Li; Sunny Hei Wong; William Kk Wu; Guang-Ping Li; George Bazoukis; Konstantinos P Letsas; Wing Tak Wong; Yun-Long Xia; Tong Liu; Gary Tse
Journal:  J Geriatr Cardiol       Date:  2017-10       Impact factor: 3.327

8.  Meta-analysis of Fragmented QRS as an Electrocardiographic Predictor for Arrhythmic Events in Patients with Brugada Syndrome.

Authors:  Lei Meng; Konstantinos P Letsas; Adrian Baranchuk; Qingmiao Shao; Gary Tse; Nixiao Zhang; Zhiwei Zhang; Dan Hu; Guangping Li; Tong Liu
Journal:  Front Physiol       Date:  2017-09-12       Impact factor: 4.566

Review 9.  Mouse models of atherosclerosis: a historical perspective and recent advances.

Authors:  Yee Ting Lee; Hiu Yu Lin; Yin Wah Fiona Chan; Ka Hou Christien Li; Olivia Tsz Ling To; Bryan P Yan; Tong Liu; Guangping Li; Wing Tak Wong; Wendy Keung; Gary Tse
Journal:  Lipids Health Dis       Date:  2017-01-17       Impact factor: 3.876

10.  Animal models of atherosclerosis.

Authors:  Yee Ting Lee; Victoria Laxton; Hiu Yu Lin; Yin Wah Fiona Chan; Sophia Fitzgerald-Smith; Tsz Ling Olivia To; Bryan P Yan; Tong Liu; Gary Tse
Journal:  Biomed Rep       Date:  2017-01-17
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