BACKGROUND: Methodological difficulties associated with QT measurements prompt the search for new electrocardiographic markers of repolarization heterogeneity. OBJECTIVE: We hypothesized that beat-to-beat 3-dimensional vectorcardiogram variability predicts ventricular arrhythmia (VA) in patients with structural heart disease, left ventricular systolic dysfunction, and implanted implantable cardioverter-defibrillators (ICDs). METHODS: Baseline orthogonal electrocardiograms were recorded in 414 patients with structural heart disease (mean age 59.4 ± 12.0; 280 white [68%] and 134 black [32%]) at rest before implantation of ICD for primary prevention of sudden cardiac death. R and T peaks of 30 consecutive sinus beats were plotted in 3 dimensions to form an R peaks cloud and a T peaks cloud. The volume of the peaks cloud was calculated as the volume within the convex hull. Patients were followed up for at least 6 months; sustained VA with appropriate ICD therapies served as an end point. RESULTS: During a mean follow-up time of 18.4 ± 12.5 months, 61 of the 414 patients (14.73% or 9.6% per person-year of follow-up) experienced sustained VA with appropriate ICD therapies: 41 of them were white and 20 were black. In the multivariate Cox model that included inducibility of VA and use of beta-blockers, the highest tertile of T/R peaks cloud volume ratio significantly predicted VA (hazard ratio 1.68, 95% confidence interval 1.01 to 2.80; P = .046) in all patients. T peaks cloud volume and T/R peaks cloud volume ratio were significantly smaller in black subjects (median 0.09 [interquartile range 0.04 to 0.15] vs. median 0.11 [interquartile range 0.06 to 0.22], P = .002). CONCLUSION: A relatively large T peaks cloud volume is associated with increased risk of VA in patients with structural heart disease and systolic dysfunction.
BACKGROUND: Methodological difficulties associated with QT measurements prompt the search for new electrocardiographic markers of repolarization heterogeneity. OBJECTIVE: We hypothesized that beat-to-beat 3-dimensional vectorcardiogram variability predicts ventricular arrhythmia (VA) in patients with structural heart disease, left ventricular systolic dysfunction, and implanted implantable cardioverter-defibrillators (ICDs). METHODS: Baseline orthogonal electrocardiograms were recorded in 414 patients with structural heart disease (mean age 59.4 ± 12.0; 280 white [68%] and 134 black [32%]) at rest before implantation of ICD for primary prevention of sudden cardiac death. R and T peaks of 30 consecutive sinus beats were plotted in 3 dimensions to form an R peaks cloud and a T peaks cloud. The volume of the peaks cloud was calculated as the volume within the convex hull. Patients were followed up for at least 6 months; sustained VA with appropriate ICD therapies served as an end point. RESULTS: During a mean follow-up time of 18.4 ± 12.5 months, 61 of the 414 patients (14.73% or 9.6% per person-year of follow-up) experienced sustained VA with appropriate ICD therapies: 41 of them were white and 20 were black. In the multivariate Cox model that included inducibility of VA and use of beta-blockers, the highest tertile of T/R peaks cloud volume ratio significantly predicted VA (hazard ratio 1.68, 95% confidence interval 1.01 to 2.80; P = .046) in all patients. T peaks cloud volume and T/R peaks cloud volume ratio were significantly smaller in black subjects (median 0.09 [interquartile range 0.04 to 0.15] vs. median 0.11 [interquartile range 0.06 to 0.22], P = .002). CONCLUSION: A relatively large T peaks cloud volume is associated with increased risk of VA in patients with structural heart disease and systolic dysfunction.
Authors: M S Fuller; G Sándor; B Punske; B Taccardi; R S MacLeod; P R Ershler; L S Green; R L Lux Journal: Circulation Date: 2000-08-08 Impact factor: 29.690
Authors: Arthur J Moss; Wojciech Zareba; W Jackson Hall; Helmut Klein; David J Wilber; David S Cannom; James P Daubert; Steven L Higgins; Mary W Brown; Mark L Andrews Journal: N Engl J Med Date: 2002-03-19 Impact factor: 91.245
Authors: Esther Pueyo; Peter Smetana; Pere Caminal; Antonio Bayes de Luna; Marek Malik; Pablo Laguna Journal: IEEE Trans Biomed Eng Date: 2004-09 Impact factor: 4.538
Authors: Mark C Haigney; Wojciech Zareba; Philip J Gentlesk; Robert E Goldstein; Michael Illovsky; Scott McNitt; Mark L Andrews; Arthur J Moss Journal: J Am Coll Cardiol Date: 2004-10-06 Impact factor: 24.094
Authors: Andrea M Russo; Gail E Hafley; Kerry L Lee; Nicholas J Stamato; Michael H Lehmann; Richard L Page; Teresa Kus; Alfred E Buxton Journal: Circulation Date: 2003-06-23 Impact factor: 29.690
Authors: Norbert Szentandrássy; Kornél Kistamás; Bence Hegyi; Balázs Horváth; Ferenc Ruzsnavszky; Krisztina Váczi; János Magyar; Tamás Bányász; András Varró; Péter P Nánási Journal: Pflugers Arch Date: 2014-08-02 Impact factor: 3.657
Authors: Larisa G Tereshchenko; Aaron McCabe; Lichy Han; Sanjoli Sur; Timothy Huang; Joseph E Marine; Alan Cheng; David D Spragg; Sunil Sinha; Hugh Calkins; Kenneth Stein; Gordon F Tomaselli; Ronald D Berger Journal: Heart Rhythm Date: 2012-06-29 Impact factor: 6.343
Authors: Tamás Hézső; Muhammad Naveed; Csaba Dienes; Dénes Kiss; János Prorok; Tamás Árpádffy-Lovas; Richárd Varga; Erika Fujii; Tanju Mercan; Leila Topal; Kornél Kistamás; Norbert Szentandrássy; János Almássy; Norbert Jost; János Magyar; Tamás Bányász; István Baczkó; András Varró; Péter P Nánási; László Virág; Balázs Horváth Journal: Sci Rep Date: 2021-05-05 Impact factor: 4.379
Authors: Jordi Heijman; Antonio Zaza; Daniel M Johnson; Yoram Rudy; Ralf L M Peeters; Paul G A Volders; Ronald L Westra Journal: PLoS Comput Biol Date: 2013-08-22 Impact factor: 4.475
Authors: Lichy Han; Alan Cheng; Sanjoli Sur; Gordon F Tomaselli; Ronald D Berger; Larisa G Tereshchenko Journal: Int J Cardiol Date: 2012-10-18 Impact factor: 4.164