title: Unstable QT interval dynamics precedes ventricular tachycardia onset in patients with acute myocardial infarction: a novel approach to detect instability in QT interval dynamics from clinical ECG.
abstract: BACKGROUND
  Instability in ventricular repolarization in the presence of premature activations (PA) plays an important role in arrhythmogenesis. However, such instability cannot be detected clinically. This study developed a methodology for detecting QT interval (QTI) dynamics instability from the ECG and explored the contribution of PA and QTI instability to ventricular tachycardia (VT) onset.
  
  
  RESULTS
  To examine the contribution of PAs and QTI instability to VT onset, ECGs of 24 patients with acute myocardial infarction, 12 of whom had sustained VT (VT) and 12 nonsustained VT (NSVT), were used. From each patient ECG, 2 10-minute-long ECG recordings were extracted, 1 right before VT onset (onset epoch) and 1 at least 1 hour before it (control epoch). To ascertain how PA affects QTI dynamics stability, pseudo-ECGs were calculated from an MRI-based human ventricular model. Clinical and pseudo-ECGs were subdivided into 1-minute recordings (minECGs). QTI dynamics stability of each minECG was assessed with a novel approach. Frequency of PAs (f(PA)) and the number of minECGs with unstable QTI dynamics (N(us)) were determined for each patient. In the VT group, f(PA) and N(us) of the onset epoch were larger than in control. Positive regression relationships between f(PA) and N(us) were identified in both groups. The simulations showed that both f(PA) and the PA degree of prematurity contribute to QTI dynamics instability.
  
  
  CONCLUSIONS
  Increased PA frequency and QTI dynamics instability precede VT onset in patients with acute myocardial infarction, as determined by novel methodology for detecting instability in QTI dynamics from clinical ECGs.
authors.affiliation: Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, USA.
authors.forename: Xiaozhong
authors.initials: X
authors.lastname: Chen
authors.forename: Yuxuan
authors.initials: Y
authors.lastname: Hu
authors.forename: Barry J
authors.initials: BJ
authors.lastname: Fetics
authors.forename: Ronald D
authors.initials: RD
authors.lastname: Berger
authors.forename: Natalia A
authors.initials: NA
authors.lastname: Trayanova
mesh_terms: D000368:Aged; D000465:Algorithms; D000704:Analysis of Variance; D018880:Atrial Premature Complexes / Q000175:diagnosis* / Q000209:etiology / Q000503:physiopathology; D003198:Computer Simulation; D004562:Electrocardiography; D005260:Female; D006329:Heart Conduction System / Q000503:physiopathology*; D006801:Humans; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D008955:Models, Cardiovascular; D009203:Myocardial Infarction / Q000150:complications* / Q000175:diagnosis / Q000503:physiopathology; D011237:Predictive Value of Tests; D018570:Risk Assessment; D012307:Risk Factors; D012815:Signal Processing, Computer-Assisted; D017180:Tachycardia, Ventricular / Q000175:diagnosis* / Q000209:etiology / Q000503:physiopathology; D013997:Time Factors; D018879:Ventricular Premature Complexes / Q000175:diagnosis* / Q000209:etiology / Q000503:physiopathology