Literature DB >> 29654125

Electrical Substrates Driving Response to Cardiac Resynchronization Therapy: A Combined Clinical-Computational Evaluation.

Peter R Huntjens1, Sylvain Ploux2, Marc Strik2, John Walmsley2, Philippe Ritter2, Michel Haissaguerre2, Frits W Prinzen2, Tammo Delhaas2, Joost Lumens2, Pierre Bordachar2.   

Abstract

BACKGROUND: The predictive value of interventricular versus intraventricular dyssynchrony for response to cardiac resynchronization therapy (CRT) remains unclear. We investigated the relative importance of both ventricular electrical substrate components for left ventricular (LV) hemodynamic function. METHODS AND
RESULTS: First, we used the cardiovascular computational model CircAdapt to characterize the isolated effect of intrinsic interventricular and intraventricular activation on CRT response (ΔLVdP/dtmax). Simulated ΔLVdP/dtmax (range: 1.3%-26.5%) increased considerably with increasing interventricular dyssynchrony. In contrast, the isolated effect of intraventricular dyssynchrony in either the LV or right ventricle was limited (ΔLVdP/dtmax range: 12.3%-18.3% and 14.1%-15.7%, respectively). Effects of activation during biventricular pacing on ΔLVdP/dtmax were small. Second, electrocardiographic imaging-derived activation characteristics of 51 CRT candidates were used to personalize ventricular activation in CircAdapt. The individualized models were subsequently used to assess the accuracy of ΔLVdP/dtmax prediction based on the electrical data. The model-predicted ΔLVdP/dtmax was close to the actual value in patients with left bundle branch block (measured-simulated: 2.7±9.0%) when only intrinsic interventricular dyssynchrony was personalized. Among patients without left bundle branch block, ΔLVdP/dtmax was systematically overpredicted by CircAdapt (measured-simulated: 9.2±7.1%). Adding intraventricular activation to the model did not improve the accuracy of the response prediction.
CONCLUSIONS: Computer simulations revealed that intrinsic interventricular dyssynchrony is the dominant component of the electrical substrate driving the response to CRT. Intrinsic intraventricular dyssynchrony and any dyssynchrony during biventricular pacing play a minor role in this respect. This may facilitate patient-specific modeling for prediction of CRT response. CLINICAL TRIAL REGISTRATION: URL: https://www.clinicaltrials.gov. Unique identifier: NCT01270646.
© 2018 American Heart Association, Inc.

Entities:  

Keywords:  bundle branch block; cardiac resynchronization therapy; computer simulation; hemodynamics; patient-specific modeling

Mesh:

Year:  2018        PMID: 29654125     DOI: 10.1161/CIRCEP.117.005647

Source DB:  PubMed          Journal:  Circ Arrhythm Electrophysiol        ISSN: 1941-3084


  6 in total

Review 1.  Non-invasive cardiac mapping for non-response in cardiac resynchronization therapy.

Authors:  Marc Strik; Sylvain Ploux; Lior Jankelson; Pierre Bordachar
Journal:  Ann Med       Date:  2019-05-23       Impact factor: 4.709

2.  Effects of Heart Rate and Ventricular Wall Thickness on Non-invasive Mapping: An in silico Study.

Authors:  Erick Andres Perez Alday; Dominic G Whittaker; Alan P Benson; Michael A Colman
Journal:  Front Physiol       Date:  2019-04-05       Impact factor: 4.566

3.  Adaptive Cardiac Resynchronization Therapy Effect on Electrical Dyssynchrony (aCRT-ELSYNC): A randomized controlled trial.

Authors:  Kazi T Haq; Nichole M Rogovoy; Jason A Thomas; Christopher Hamilton; Katherine J Lutz; Ashley Wirth; Aron B Bender; David M German; Ryle Przybylowicz; Peter van Dam; Thomas A Dewland; Khidir Dalouk; Eric Stecker; Babak Nazer; Peter M Jessel; Karen S MacMurdy; Ignatius Gerardo E Zarraga; Bassel Beitinjaneh; Charles A Henrikson; Merritt Raitt; Cristina Fuss; Maros Ferencik; Larisa G Tereshchenko
Journal:  Heart Rhythm O2       Date:  2021-06-29

4.  Intracardiac conduction time as a predictor of cardiac resynchronization therapy response: Results of the BIO|SELECT pilot study.

Authors:  Kyoko Soejima; Yusuke Kondo; Shingo Sasaki; Kazumasa Adachi; Ritsushi Kato; Nobuhisa Hagiwara; Tomoo Harada; Kengo Kusano; Fumiharu Miura; Itsuro Morishima; Kazuyasu Yoshitani; Akihiko Yotsukura; Manabu Fujimoto; Nobuhiro Nishii; Kenji Shimeno; Masatsugu Ohe; Hiroshi Tasaka; Hiroto Sasaki; Juergen Schrader; Kenji Ando
Journal:  Heart Rhythm O2       Date:  2021-09-28

5.  Machine Learning Prediction of Cardiac Resynchronisation Therapy Response From Combination of Clinical and Model-Driven Data.

Authors:  Svyatoslav Khamzin; Arsenii Dokuchaev; Anastasia Bazhutina; Tatiana Chumarnaya; Stepan Zubarev; Tamara Lyubimtseva; Viktoria Lebedeva; Dmitry Lebedev; Viatcheslav Gurev; Olga Solovyova
Journal:  Front Physiol       Date:  2021-12-14       Impact factor: 4.566

Review 6.  Computational models in cardiology.

Authors:  Steven A Niederer; Joost Lumens; Natalia A Trayanova
Journal:  Nat Rev Cardiol       Date:  2019-02       Impact factor: 32.419

  6 in total

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