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). CONCLUSION: XCD is superior to existing parameters at discriminating patients with left ventricular dyssynchrony from those with normal function.
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). CONCLUSION:XCD is superior to existing parameters at discriminating patients with left ventricular dyssynchrony from those with normal function.
Authors: Brandon K Fornwalt; Joshua A Thomas; Mohit Bhasin; John D Merlino; Angel R León; Derek A Fyfe; John N Oshinski Journal: J Am Soc Echocardiogr Date: 2008-01-09 Impact factor: 5.251
Authors: Vincent C Thomas; Kristopher M Cumbermack; Carey K Lamphier; Christina R Phillips; Derek A Fyfe; Brandon K Fornwalt Journal: J Am Soc Echocardiogr Date: 2012-11-29 Impact factor: 5.251
Authors: Janaka P Wansapura; Douglas P Millay; R Scott Dunn; Jeffery D Molkentin; D Woodrow Benson Journal: Neuromuscul Disord Date: 2010-10-08 Impact factor: 4.296
Authors: Brandon K Fornwalt; William W Sprague; John D Carew; John D Merlino; Derek A Fyfe; Angel R León; John N Oshinski Journal: J Am Soc Echocardiogr Date: 2009-05 Impact factor: 5.251
Authors: Evangelos Sariyanidi; Casey J Zampella; Keith G Bartley; John D Herrington; Theodore D Satterthwaite; Robert T Schultz; Birkan Tunc Journal: Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit Date: 2020-08-05
Authors: Jana G Delfino; Brandon K Fornwalt; Robert L Eisner; Angel R Leon; John N Oshinski Journal: J Magn Reson Imaging Date: 2008-11 Impact factor: 4.813