@article{005770cb2b0b46c5a0104b9824d68514,
title = "A rule-based method for predicting the electrical activation of the heart with cardiac resynchronization therapy from non-invasive clinical data",
abstract = "Background: Cardiac Resynchronization Therapy (CRT) is one of the few effective treatments for heart failure patients with ventricular dyssynchrony. The pacing location of the left ventricle is indicated as a determinant of CRT outcome.Objective: Patient specific computational models allow the activation pattern following CRT implant to be predicted and this may be used to optimize CRT lead placement.Methods: In this study, the effects of heterogeneous cardiac substrate (scar, fast endocardial conduction, slow septal conduction, functional block) on accurately predicting the electrical activation of the LV epicardium were tested to determine the minimal detail required to create a rule based model of cardiac electrophysiology. Non-invasive clinical data (CT or CMR images and 12 lead ECG) from eighteen patients from two centers were used to investigate the models.Results: Validation with invasive electro-anatomical mapping data identified that computer models with fast endocardial conduction were able to predict the electrical activation with a mean distance errors of 9.2 +/- 0.5 mm (CMR data) or (CT data) 7.5 +/- 0.7 mm.Conclusion: This study identified a simple rule-based fast endocardial conduction model, built using non-invasive clinical data that can be used to rapidly and robustly predict the electrical activation of the heart. Pre-procedural prediction of the latest electrically activating region to identify the optimal LV pacing site could potentially be a useful clinical planning tool for CRT procedures. (C) 2019 The Authors. Published by Elsevier B.V.",
keywords = "Cardiac resynchronization therapy, Electrophysiology, Computational models, Patient-specific simulations, VENTRICULAR LEAD PLACEMENT, DIFFUSION TENSOR MRI, CONDUCTION-VELOCITY, FIBER ARCHITECTURE, HISTOLOGICAL VALIDATION, TRABECULAR MUSCLE, QRS DURATION, PACING SITE, OPTIMIZATION, MODEL",
author = "Lee, {A. W. C.} and Nguyen, {U. C.} and O. Razeghi and J. Gould and Sidhu, {B. S.} and B. Sieniewicz and J. Behar and M. Mafi-Rad and G. Plank and Prinzen, {F. W.} and Rinaldi, {C. A.} and K. Vernooy and S. Niederer",
note = "Funding Information: S. Niederer acknowledges support from the UK Engineering and Physical Sciences Research Council ( EP/M012492/1 , NS/A000049/1 and EP/P01268X/1 ), the British Heart Foundation ( PG/15/91/31812 , PG/13/37/30280 ) and Kings Health Partners London National Institute for Health Research (NIHR) Biomedical Research Centre . Funding Information: U. C. Nguyen received a Kootstra Talent Fellowship research grant from Maastricht University and was additionally funded by a research grant the Dutch Heart Foundation (grant #2015T61). S. Niederer acknowledges support from the UK Engineering and Physical Sciences Research Council (EP/M012492/1, NS/A000049/1 and EP/P01268X/1), the British Heart Foundation (PG/15/91/31812, PG/13/37/30280) and Kings Health Partners London National Institute for Health Research (NIHR) Biomedical Research Centre. S. Niederer has received support from St. Jude Medical, Boston Scientific, Abbott, Roche, Pfizer and Siemens. F. W. Prinzen has received research grants from Medtronic, St. Jude Medical, LivaNova, Biosense Webster, MSD, and Biotronik. K. Vernooy has received research grants from Medtronic and St. Jude Medical. Funding Information: K. Vernooy has received research grants from Medtronic and St. Jude Medical . Funding Information: U. C. Nguyen received a Kootstra Talent Fellowship research grant from Maastricht University and was additionally funded by a research grant the Dutch Heart Foundation (grant #2015T61 ). Publisher Copyright: {\textcopyright} 2019 The Authors",
year = "2019",
month = oct,
doi = "10.1016/j.media.2019.06.017",
language = "English",
volume = "57",
pages = "197--213",
journal = "Medical Image Analysis",
issn = "1361-8415",
publisher = "Elsevier B.V.",
}