title: Cross-correlation quantification of dyssynchrony: a new method for quantifying the synchrony of contraction and relaxation in the heart.
abstract: BACKGROUND
  Quantification of left ventricular dyssynchrony using Doppler tissue imaging may improve selection of patients who will benefit from cardiac resynchronization therapy. Most methods used to quantify dyssynchrony use a time-to-peak analysis, which is quantitatively simplistic and requires manual identification of systole and selection of peak velocities.
  
  
  METHODS
  We developed and tested a new, highly automatable dyssynchrony parameter, cross-correlation delay (XCD), that does not require identification of systole or manual selection of peak systolic velocities. XCD uses all velocity data points from 3 consecutive beats (approximately 420 points). We tested XCD on 11 members of a positive control group (responders to cardiac resynchronization therapy with a >or=15% reduction in left ventricular end-systolic volume) and 12 members of a negative control group (normal 12-lead electrocardiogram and 2-dimensional echocardiogram findings). We compared XCD to septal-to-lateral delay in time-to-peak (SLD), maximum difference in the basal 2- or 4-chamber times to peak (MaxDiff), and SD of the 12 basal and midwall times-to-peak (Ts-SD).
  
  
  RESULTS
  XCD and Ts-SD were significantly different between the positive and negative control groups (both P <or= .0001). SLD and MaxDiff demonstrated no difference between the positive and negative control groups. XCD and Ts-SD were superior to SLD and MaxDiff in discriminating between positive and negative control groups (both P < .01 by receiver operating characteristic comparison). XCD, SLD, MaxDiff, and Ts-SD demonstrated dyssynchrony in 0%, 50%, 58%, and 50% of the negative control group, respectively. XCD was the only parameter that decreased after resynchronization in the positive control group (from 160 +/- 88-69 +/- 61 milliseconds, P = .003).
  
  
  CONCLUSIONS
  XCD is superior to existing parameters at discriminating patients with left ventricular dyssynchrony from those with normal function.
authors.affiliation: Emory University School of Medicine, Atlanta, Georgia 30322, USA. bfornwa@emory.edu
authors.forename: Brandon K
authors.initials: BK
authors.lastname: Fornwalt
authors.forename: Takeshi
authors.initials: T
authors.lastname: Arita
authors.forename: Mohit
authors.initials: M
authors.lastname: Bhasin
authors.forename: George
authors.initials: G
authors.lastname: Voulgaris
authors.forename: John D
authors.initials: JD
authors.lastname: Merlino
authors.forename: Angel R
authors.initials: AR
authors.lastname: León
authors.forename: Derek A
authors.initials: DA
authors.lastname: Fyfe
authors.forename: John N
authors.initials: JN
authors.lastname: Oshinski
mesh_terms: D000328:Adult; D000465:Algorithms; D015150:Echocardiography, Doppler / Q000379:methods*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D009200:Myocardial Contraction; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013223:Statistics as Topic; D018487:Ventricular Dysfunction, Left / Q000000981:diagnostic imaging*