Literature DB >> 26411977

Electrocardiographic Predictors of Torsadogenic Risk During Dofetilide or Sotalol Initiation: Utility of a Novel T Wave Analysis Program.

Alan Sugrue1, Vaclav Kremen2,3, Bo Qiang3, Seth H Sheldon4, Christopher V DeSimone3, Yehu Sapir5, Bryan L Striemer3, Peter Brady3, Samuel J Asirvatham3,6, Michael J Ackerman3,6,7, Paul Friedman3, Peter A Noseworthy8.   

Abstract

INTRODUCTION: Initiation of class III anti-arrhythmic medications requires telemetric monitoring for ventricular arrhythmias and QT prolongation to reduce the risk of torsades de pointes (TdP). Heart rate-corrected QT interval (QTc) is an indicator of risk, however it is imperfect, and subtle abnormalities of repolarization have been linked with arrhythmogenesis.
PURPOSE: Identification of electrocardiographic predictors of torsadogenic risk through the application of a novel T wave analysis tool.
METHODS: Among all patients admitted to Mayo Clinic for initiation of dofetilide or sotalol, we identified 13 cases who developed drug-induced TdP and 26 age and sex matched controls that did not develop TdP. The immediate pre-TdP ECG of those with TdP was compared to the last ECG performed prior to hospital discharge in controls using a novel T wave program that quantified subtle changes in T wave morphology.
RESULTS: The QTc and 12 T wave parameters successfully distinguished TdP cases from controls. The top performing parameters were the QTc in lead V3 (mean case vs control 480 vs 420 msec, p < 0.001, r = 0.72) and T wave right slope in lead I (mean case vs control -840.29 vs -1668.71 mV/s, p = 0.002, r = 0.45). The addition of T wave right slope to QTc improved prediction accuracy from 79 to 88 %.
CONCLUSION: Our data demonstrate that, in addition to QTc, the T wave right slope is correlated strongly with TdP risk. This suggests that a computer-based repolarization measurement tool that integrates additional data beyond the QTc may identify patients with the greatest torsadogenic potential.

Entities:  

Keywords:  Class III antiarrhythmics; Electrocardiography; Risk stratification; T wave analysis; Torsade de pointes

Mesh:

Substances:

Year:  2015        PMID: 26411977      PMCID: PMC4731047          DOI: 10.1007/s10557-015-6619-0

Source DB:  PubMed          Journal:  Cardiovasc Drugs Ther        ISSN: 0920-3206            Impact factor:   3.727


  36 in total

1.  Problems of heart rate correction in assessment of drug-induced QT interval prolongation.

Authors:  M Malik
Journal:  J Cardiovasc Electrophysiol       Date:  2001-04

Review 2.  Drug induced QT prolongation and torsades de pointes.

Authors:  Yee Guan Yap; A John Camm
Journal:  Heart       Date:  2003-11       Impact factor: 5.994

Review 3.  Drug-induced prolongation of the QT interval.

Authors:  Dan M Roden
Journal:  N Engl J Med       Date:  2004-03-04       Impact factor: 91.245

4.  Electrocardiographic and clinical predictors of torsades de pointes induced by almokalant infusion in patients with chronic atrial fibrillation or flutter: a prospective study.

Authors:  B Houltz; B Darpö; N Edvardsson; P Blomström; J Brachmann; H J Crijns; S M Jensen; E Svernhage; H Vallin; K Swedberg
Journal:  Pacing Clin Electrophysiol       Date:  1998-05       Impact factor: 1.976

5.  Sex difference in risk of torsade de pointes with d,l-sotalol.

Authors:  M H Lehmann; S Hardy; D Archibald; B quart; D J MacNeil
Journal:  Circulation       Date:  1996-11-15       Impact factor: 29.690

Review 6.  Cellular mechanisms underlying the long QT syndrome.

Authors:  Charles Antzelevitch; Wataru Shimizu
Journal:  Curr Opin Cardiol       Date:  2002-01       Impact factor: 2.161

7.  Age-gender influence on the rate-corrected QT interval and the QT-heart rate relation in families with genotypically characterized long QT syndrome.

Authors:  M H Lehmann; K W Timothy; D Frankovich; B S Fromm; M Keating; E H Locati; R T Taggart; J A Towbin; A J Moss; P J Schwartz; G M Vincent
Journal:  J Am Coll Cardiol       Date:  1997-01       Impact factor: 24.094

8.  T-peak to T-end interval may be a better predictor of high-risk patients with hypertrophic cardiomyopathy associated with a cardiac troponin I mutation than QT dispersion.

Authors:  Masami Shimizu; Hidekazu Ino; Kazuyasu Okeie; Masato Yamaguchi; Mitsuru Nagata; Kenshi Hayashi; Hideki Itoh; Taku Iwaki; Kotaro Oe; Tetsuo Konno; Hiroshi Mabuchi
Journal:  Clin Cardiol       Date:  2002-07       Impact factor: 2.882

9.  Increased short-term variability of repolarization predicts d-sotalol-induced torsades de pointes in dogs.

Authors:  Morten B Thomsen; S Cora Verduyn; Milan Stengl; Jet D M Beekman; Geert de Pater; Jurren van Opstal; Paul G A Volders; Marc A Vos
Journal:  Circulation       Date:  2004-10-11       Impact factor: 29.690

10.  Incidence and clinical features of the quinidine-associated long QT syndrome: implications for patient care.

Authors:  D M Roden; R L Woosley; R K Primm
Journal:  Am Heart J       Date:  1986-06       Impact factor: 4.749

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  7 in total

1.  Automated T-wave analysis can differentiate acquired QT prolongation from congenital long QT syndrome.

Authors:  Alan Sugrue; Peter A Noseworthy; Vaclav Kremen; J Martijn Bos; Bo Qiang; Ram K Rohatgi; Yehu Sapir; Zachi I Attia; Peter Brady; Pedro J Caraballo; Samuel J Asirvatham; Paul A Friedman; Michael J Ackerman
Journal:  Ann Noninvasive Electrocardiol       Date:  2017-04-21       Impact factor: 1.468

Review 2.  Risk assessment tools for QT prolonging pharmacotherapy in older adults: a systematic review.

Authors:  Simone Skullbacka; Marja Airaksinen; Juha Puustinen; Terhi Toivo
Journal:  Eur J Clin Pharmacol       Date:  2022-02-14       Impact factor: 2.953

3.  Electrophysiological Characteristics of Human iPSC-Derived Cardiomyocytes for the Assessment of Drug-Induced Proarrhythmic Potential.

Authors:  Wataru Yamamoto; Keiichi Asakura; Hiroyuki Ando; Tomohiko Taniguchi; Atsuko Ojima; Takaaki Uda; Tomoharu Osada; Seiji Hayashi; Chieko Kasai; Norimasa Miyamoto; Hiroyuki Tashibu; Takashi Yoshinaga; Daiju Yamazaki; Atsushi Sugiyama; Yasunari Kanda; Kohei Sawada; Yuko Sekino
Journal:  PLoS One       Date:  2016-12-06       Impact factor: 3.240

4.  Noninvasive assessment of dofetilide plasma concentration using a deep learning (neural network) analysis of the surface electrocardiogram: A proof of concept study.

Authors:  Zachi I Attia; Alan Sugrue; Samuel J Asirvatham; Michael J Ackerman; Suraj Kapa; Paul A Friedman; Peter A Noseworthy
Journal:  PLoS One       Date:  2018-08-22       Impact factor: 3.240

5.  Computational cardiology and risk stratification for sudden cardiac death: one of the grand challenges for cardiology in the 21st century.

Authors:  Adam P Hill; Matthew D Perry; Najah Abi-Gerges; Jean-Philippe Couderc; Bernard Fermini; Jules C Hancox; Bjorn C Knollmann; Gary R Mirams; Jon Skinner; Wojciech Zareba; Jamie I Vandenberg
Journal:  J Physiol       Date:  2016-06-09       Impact factor: 5.182

6.  Arrhythmic hazard map for a 3D whole-ventricle model under multiple ion channel block.

Authors:  Jun-Ichi Okada; Takashi Yoshinaga; Junko Kurokawa; Takumi Washio; Tetsushi Furukawa; Kohei Sawada; Seiryo Sugiura; Toshiaki Hisada
Journal:  Br J Pharmacol       Date:  2018-07-22       Impact factor: 8.739

7.  Applications of machine learning in decision analysis for dose management for dofetilide.

Authors:  Andrew E Levy; Minakshi Biswas; Rachel Weber; Khaldoun Tarakji; Mina Chung; Peter A Noseworthy; Christopher Newton-Cheh; Michael A Rosenberg
Journal:  PLoS One       Date:  2019-12-31       Impact factor: 3.240

  7 in total

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