Predicting Activation Patterns in Cardiac Resynchronization Therapy Patients

Angela W.C. Lee; Uyen C. Nguyen; Justin Gould; Baldeep Sidhu; Benjamin Sieniewicz; Caroline Mendonca Costa; Fritz Prinzen; Gernot Plank; Christopher A. Rinaldi; Kevin Vernooy; Steven A. Niederer

doi:10.22489/CinC.2018.079

Predicting Activation Patterns in Cardiac Resynchronization Therapy Patients

Angela W.C. Lee^*, Uyen C. Nguyen, Justin Gould, Baldeep Sidhu, Benjamin Sieniewicz, Caroline Mendonca Costa, Fritz Prinzen, Gernot Plank, Christopher A. Rinaldi, Kevin Vernooy, Steven A. Niederer

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

Abstract

Cardiac resynchronization therapy (CRT) is an effective treatment for heart failure patients. Suboptimal pacing timings and locations have been identified as causes for nonresponse to CRT. Patient specific computer models allow for the prediction of the electrical activation pattern on the ventricles, which can then be used to optimise CRT lead location. The electrical activation of the heart depends on the underlying cardiac substrate. The electrical properties of the heart have been found to be heterogeneous, with scar, functional block, septal slowing, and fast endocardial conduction impacting the electrical activation across the ventricles. Non-Invasive data from 14 patients were used to create computer models of the heart. The patient specific models were then used to assess the importance of the heterogeneous cardiac substrates on accurately predicting the electrical activation pattern of the ventricles. Fast endocardial conduction was found to be the most important factor in accurately predicting the electrical activation of the heart in CRT patients.

Original language	English
Title of host publication	Computing in Cardiology Conference, CinC 2018
Publisher	IEEE Computer Society
Volume	2018-September
ISBN (Electronic)	9781728109589
DOIs	https://doi.org/10.22489/CinC.2018.079
Publication status	Published - 1 Sept 2018
Event	45th Computing in Cardiology Conference, CinC 2018 - Maastricht, Netherlands Duration: 23 Sept 2018 → 26 Sept 2018

Conference

Conference	45th Computing in Cardiology Conference, CinC 2018
Country/Territory	Netherlands
City	Maastricht
Period	23/09/2018 → 26/09/2018

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10.22489/CinC.2018.079

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Lee, A. W. C., Nguyen, U. C., Gould, J., Sidhu, B., Sieniewicz, B., Mendonca Costa, C., Prinzen, F., Plank, G., Rinaldi, C. A., Vernooy, K., & Niederer, S. A. (2018). Predicting Activation Patterns in Cardiac Resynchronization Therapy Patients. In Computing in Cardiology Conference, CinC 2018 (Vol. 2018-September). Article 8743761 IEEE Computer Society. https://doi.org/10.22489/CinC.2018.079

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title = "Predicting Activation Patterns in Cardiac Resynchronization Therapy Patients",

abstract = "Cardiac resynchronization therapy (CRT) is an effective treatment for heart failure patients. Suboptimal pacing timings and locations have been identified as causes for nonresponse to CRT. Patient specific computer models allow for the prediction of the electrical activation pattern on the ventricles, which can then be used to optimise CRT lead location. The electrical activation of the heart depends on the underlying cardiac substrate. The electrical properties of the heart have been found to be heterogeneous, with scar, functional block, septal slowing, and fast endocardial conduction impacting the electrical activation across the ventricles. Non-Invasive data from 14 patients were used to create computer models of the heart. The patient specific models were then used to assess the importance of the heterogeneous cardiac substrates on accurately predicting the electrical activation pattern of the ventricles. Fast endocardial conduction was found to be the most important factor in accurately predicting the electrical activation of the heart in CRT patients.",

author = "Lee, {Angela W.C.} and Nguyen, {Uyen C.} and Justin Gould and Baldeep Sidhu and Benjamin Sieniewicz and {Mendonca Costa}, Caroline and Fritz Prinzen and Gernot Plank and Rinaldi, {Christopher A.} and Kevin Vernooy and Niederer, {Steven A.}",

year = "2018",

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doi = "10.22489/CinC.2018.079",

language = "English",

volume = "2018-September",

booktitle = "Computing in Cardiology Conference, CinC 2018",

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note = "45th Computing in Cardiology Conference, CinC 2018 ; Conference date: 23-09-2018 Through 26-09-2018",

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Lee, AWC, Nguyen, UC, Gould, J, Sidhu, B, Sieniewicz, B, Mendonca Costa, C, Prinzen, F, Plank, G, Rinaldi, CA, Vernooy, K & Niederer, SA 2018, Predicting Activation Patterns in Cardiac Resynchronization Therapy Patients. in Computing in Cardiology Conference, CinC 2018. vol. 2018-September, 8743761, IEEE Computer Society, 45th Computing in Cardiology Conference, CinC 2018, Maastricht, Netherlands, 23/09/2018. https://doi.org/10.22489/CinC.2018.079

TY - CHAP

T1 - Predicting Activation Patterns in Cardiac Resynchronization Therapy Patients

AU - Lee, Angela W.C.

AU - Nguyen, Uyen C.

AU - Gould, Justin

AU - Sidhu, Baldeep

AU - Sieniewicz, Benjamin

AU - Mendonca Costa, Caroline

AU - Prinzen, Fritz

AU - Plank, Gernot

AU - Rinaldi, Christopher A.

AU - Vernooy, Kevin

AU - Niederer, Steven A.

PY - 2018/9/1

Y1 - 2018/9/1

N2 - Cardiac resynchronization therapy (CRT) is an effective treatment for heart failure patients. Suboptimal pacing timings and locations have been identified as causes for nonresponse to CRT. Patient specific computer models allow for the prediction of the electrical activation pattern on the ventricles, which can then be used to optimise CRT lead location. The electrical activation of the heart depends on the underlying cardiac substrate. The electrical properties of the heart have been found to be heterogeneous, with scar, functional block, septal slowing, and fast endocardial conduction impacting the electrical activation across the ventricles. Non-Invasive data from 14 patients were used to create computer models of the heart. The patient specific models were then used to assess the importance of the heterogeneous cardiac substrates on accurately predicting the electrical activation pattern of the ventricles. Fast endocardial conduction was found to be the most important factor in accurately predicting the electrical activation of the heart in CRT patients.

AB - Cardiac resynchronization therapy (CRT) is an effective treatment for heart failure patients. Suboptimal pacing timings and locations have been identified as causes for nonresponse to CRT. Patient specific computer models allow for the prediction of the electrical activation pattern on the ventricles, which can then be used to optimise CRT lead location. The electrical activation of the heart depends on the underlying cardiac substrate. The electrical properties of the heart have been found to be heterogeneous, with scar, functional block, septal slowing, and fast endocardial conduction impacting the electrical activation across the ventricles. Non-Invasive data from 14 patients were used to create computer models of the heart. The patient specific models were then used to assess the importance of the heterogeneous cardiac substrates on accurately predicting the electrical activation pattern of the ventricles. Fast endocardial conduction was found to be the most important factor in accurately predicting the electrical activation of the heart in CRT patients.

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DO - 10.22489/CinC.2018.079

M3 - Conference paper

AN - SCOPUS:85068764766

VL - 2018-September

BT - Computing in Cardiology Conference, CinC 2018

PB - IEEE Computer Society

T2 - 45th Computing in Cardiology Conference, CinC 2018

Y2 - 23 September 2018 through 26 September 2018

ER -

Predicting Activation Patterns in Cardiac Resynchronization Therapy Patients

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