RT Journal Article
T1 Constructing bilayer and volumetric atrial models at scale
A1 Roney, Caroline H.
A1 Solis Lemus, Jose Alonso
A1 López Barrera, Carlos
A1 Zolotarev, Alexander
A1 Ulgen, Onur
A1 Kerfoot, Eric
A1 Bevis, Laura
A1 Misghina, Semhar
A1 Vidal Horrach, Caterina
A1 Jaffery, Ovais A.
A1 Ehnesh, Mahmoud
A1 Rodero, Cristobal
A1 Dharmaprani, Dhani
A1 Ríos Muñoz, Gonzalo Ricardo
A1 Ganesan, Anand
A1 Good, Wilson W
A1 Neic, Aurel
A1 Planck, Gernot
A1 Hopman, Luuk H G A
A1 Götte, Marco J. W.
A1 Honarbakhsh, Shohreh
A1 Narayan, Sanjiv M
A1 Vigmond, Edward
A1 Niederer, Steven
AB To enable large in silico trials and personalized model predictions on clinical timescales, it is imperative that models can be constructed quickly and reproducibly. First, we aimed to overcome the challenges of constructing cardiac models at scale through developing a robust, open-source pipeline for bilayer and volumetric atrial models. Second, we aimed to investigate the effects of fibres, fibrosis and model representation on fibrillatory dynamics. To construct bilayer and volumetric models, we extended our previously developed coordinate system to incorporate transmurality, atrial regions and fibres (rule-based or data driven diffusion tensor magnetic resonance imaging (MRI)). We created a cohort of 1000 biatrial bilayer and volumetric models derived from computed tomography (CT) data, as well as models from MRI, and electroanatomical mapping. Fibrillatory dynamics diverged between bilayer and volumetric simulations across the CT cohort (correlation coefficient for phase singularity maps: left atrial (LA) 0.27 ± 0.19, right atrial (RA) 0.41 ± 0.14). Adding fibrotic remodelling stabilized re-entries and reduced the impact of model type (LA: 0.52 ± 0.20, RA: 0.36 ± 0.18). The choice of fibre field has a small effect on paced activation data (less than 12 ms), but a larger effect on fibrillatory dynamics. Overall, we developed an open-source user-friendly pipeline for generating atrial models from imaging or electroanatomical mapping data enabling in silico clinical trials at scale (https://github.com/pcmlab/atrialmtk).
PB The Royal Society
SN 2042-8898
YR 2023
FD 2023-12-15
LK https://hdl.handle.net/10016/39813
UL https://hdl.handle.net/10016/39813
LA eng
NO C.H.R. acknowledges support from a UKRI Future LeadersFellowship (grant no. MR/W004720/1). The team acknowledge Archer2 simulation funding. O.A.J. acknowledges funding for his PhD studentship from Acutus Medical. W.W.G. is an employee and shareholder of Acutus Medical. The research described herein was not influenced by this employment and no conflict of interest exists. C.R. receives funding from the British Heart Foundation (grant no. RG/ 20/4/34 803). This work was also supported by the Wellcome ESPRC Centre for Medical Engineering at King’s College London (grant no.WT 203148/Z/16/Z). G.P. received financial support from the Austrian Science Fund (FWF) grant no. I6476-B. C.L.B. acknowledges CONACYTfor a research scholarship. D.D. and A.G. acknowledge funding as follows: Cardiovascular Health Mission Grant from the MedicalResearch Future Fund, and Heart Health Innovation Grant from The Hospital Research Foundation of South Australia. G.R.R.-M. acknowledges support from the Instituto de Salud Carlos III, Madrid, Spain (grant nos. PI18/01895, DTS21/00064 and PI22/01619). E.V. received financial support from the French Government as part of the ‘Investments of the Future’ programme managed by the National ResearchAgency (ANR), grant reference ANR-10-IAHU-04.
DS e-Archivo
RD 17 jul. 2024