Andrew Crozier1,2, Bojan Blazevic1, Pablo Lamata1, Gernot Plank2, Matthew Ginks3, Simon Duckett3, Manav Sohal3, Anoop Shetty3, Christopher A Rinaldi3, Reza Razavi1, Steven A Niederer1, Nicolas P Smith4,5. 1. Division of Imaging Sciences and Biomedical Engineering, King's College London, St Thomas Hospital, London SE1 7EH, UK. 2. Institute of Biophysics, Medical University of Graz, Harrachgasse 21/IV, 8010 Graz, Austria. 3. Department of Cardiology, Guy's and St. Thomas' Hospital, London SE1 7EH, London, UK. 4. Division of Imaging Sciences and Biomedical Engineering, King's College London, St Thomas Hospital, London SE1 7EH, UK np.smith@auckland.ac.nz. 5. Faculty of Engineering, University of Auckland, 20 Symonds St, Auckland 1010, New Zealand.
Abstract
AIMS: The efficacy of cardiac resynchronization therapy (CRT) is known to vary considerably with pacing location, however the most effective set of metrics by which to select the optimal pacing site is not yet well understood. Computational modelling offers a powerful methodology to comprehensively test the effect of pacing location in silico and investigate how to best optimize therapy using clinically available metrics for the individual patient. METHODS AND RESULTS: Personalized computational models of cardiac electromechanics were used to perform an in silico left ventricle (LV) pacing site optimization study as part of biventricular CRT in three patient cases. Maps of response to therapy according to changes in total activation time (ΔTAT) and acute haemodynamic response (AHR) were generated and compared with preclinical metrics of electrical function, strain, stress, and mechanical work to assess their suitability for selecting the optimal pacing site. In all three patients, response to therapy was highly sensitive to pacing location, with laterobasal locations being optimal. ΔTAT and AHR were found to be correlated (ρ < -0.80), as were AHR and the preclinical activation time at the pacing site (ρ ≥ 0.73), however pacing in the last activated site did not result in the optimal response to therapy in all cases. CONCLUSION: This computational modelling study supports pacing in laterobasal locations, optimizing pacing site by minimizing paced QRS duration and pacing in regions activated late at sinus rhythm. Results demonstrate information content is redundant using multiple preclinical metrics. Of significance, the correlation of AHR with ΔTAT indicates that minimization of QRSd is a promising metric for optimization of lead placement. Published on behalf of the European Society of Cardiology. All rights reserved.
AIMS: The efficacy of cardiac resynchronization therapy (CRT) is known to vary considerably with pacing location, however the most effective set of metrics by which to select the optimal pacing site is not yet well understood. Computational modelling offers a powerful methodology to comprehensively test the effect of pacing location in silico and investigate how to best optimize therapy using clinically available metrics for the individual patient. METHODS AND RESULTS: Personalized computational models of cardiac electromechanics were used to perform an in silico left ventricle (LV) pacing site optimization study as part of biventricular CRT in three patient cases. Maps of response to therapy according to changes in total activation time (ΔTAT) and acute haemodynamic response (AHR) were generated and compared with preclinical metrics of electrical function, strain, stress, and mechanical work to assess their suitability for selecting the optimal pacing site. In all three patients, response to therapy was highly sensitive to pacing location, with laterobasal locations being optimal. ΔTAT and AHR were found to be correlated (ρ < -0.80), as were AHR and the preclinical activation time at the pacing site (ρ ≥ 0.73), however pacing in the last activated site did not result in the optimal response to therapy in all cases. CONCLUSION: This computational modelling study supports pacing in laterobasal locations, optimizing pacing site by minimizing paced QRS duration and pacing in regions activated late at sinus rhythm. Results demonstrate information content is redundant using multiple preclinical metrics. Of significance, the correlation of AHR with ΔTAT indicates that minimization of QRSd is a promising metric for optimization of lead placement. Published on behalf of the European Society of Cardiology. All rights reserved.
Authors: William T Abraham; Westby G Fisher; Andrew L Smith; David B Delurgio; Angel R Leon; Evan Loh; Dusan Z Kocovic; Milton Packer; Alfredo L Clavell; David L Hayes; Myrvin Ellestad; Robin J Trupp; Jackie Underwood; Faith Pickering; Cindy Truex; Peggy McAtee; John Messenger Journal: N Engl J Med Date: 2002-06-13 Impact factor: 91.245
Authors: Panos E Vardas; Angelo Auricchio; Jean-Jacques Blanc; Jean-Claude Daubert; Helmut Drexler; Hugo Ector; Maurizio Gasparini; Cecilia Linde; Francisco Bello Morgado; Ali Oto; Richard Sutton; Maria Trusz-Gluza Journal: Europace Date: 2007-08-28 Impact factor: 5.214
Authors: Pablo Lamata; Steven Niederer; David Nordsletten; David C Barber; Ishani Roy; D Rod Hose; Nic Smith Journal: Med Image Anal Date: 2011-07-06 Impact factor: 8.545
Authors: Sander G Molhoek; Lieselot VAN Erven; Marianne Bootsma; Paul Steendijk; Ernst E Van Der Wall; Martin J Schalij Journal: Pacing Clin Electrophysiol Date: 2004-03 Impact factor: 1.976
Authors: Nicolas Derval; Pierre Bordachar; Han S Lim; Frederic Sacher; Sylvain Ploux; Julien Laborderie; Paul Steendijk; Antoine Deplagne; Philippe Ritter; Stephane Garrigue; Arnaud Denis; Mélèze Hocini; Michel Haissaguerre; Jacques Clementy; Pierre Jaïs Journal: J Cardiovasc Electrophysiol Date: 2014-07-24
Authors: Leonard M Rademakers; Roeland van Kerckhoven; Caroline J M van Deursen; Marc Strik; Arne van Hunnik; Marion Kuiper; Anniek Lampert; Catherine Klersy; Francisco Leyva; Angelo Auricchio; Jos G Maessen; Frits W Prinzen Journal: Circ Arrhythm Electrophysiol Date: 2010-05-21