Atrial Models with Personalized Effective Refractory Period

Impact of Effective Refractory Period Personalization on Prediction of Atrial Fibrillation Vulnerability   Authors: Patricia Martínez Díaz, Christian Goetz, Albert Dasi, Laura Anna Unger, Annika Haas, Olaf Dössel, Armin Luik, Axel Loewe patricia.martinez@kit.edu / publications@ibt.kit.edu doi:10.1093/europace/euad122.542   This dataset contains 7 atrial meshes, 6 left atria and 1 right atrium, derived from electroanatomical mapping and measurements of the effective refractory period (ERP), bipolar voltage (bi) and local activation times (lat). The meshes include annotations and fibers and are ready for simulations in the cardiac electrophysiology simulator openCARP. We also provide the code to reproduce 272 reentries by reading the selected parameters.par and state.roe files. The meshes were generated using AugmentA code and the simulated reentries were induced following the PEERP protocol by Azzolin et al.    Folder structure The code is located in the src folder, the meshes in the data folder and the reentries in the results folder.   src/    |-- run.py    |-- induceReentry.py    |-- getStimPoints.py    |-- element_tag.csv    |-- al_mk_H.par    |-- requirements.txt    |-- reproduceReentry.py data/    |-- meshes/        |-- P1/                 |-- P1_with_erp_lat_bi.vtk                |-- ERP.pts               |-- ERP_values.txt               |-- ablation.pts               |-- LA_stim_points_2cm.pts               |-- bilayer/               |-- nodal_adjustment/                  |--PARAMETER_SCENARIO.adj (e.g. Gto_continuous.adj) . . .        |-- P7    results/        |-- MESH_SCENARIO_CV/ (e.g P1_continuous_0.3)                |-- point_X_beat_Y        |-- MESH_SCENARIO_CV_PERTURBATION_SET/ (e.g P1_continuous_0.7_2_1)                |-- point_X_beat_Y                                            README.md src: contains the source files needed to run PEERP protocol run.py This is the main function to run the pacing protocol (not needed to run if reentries are only reproduced, check reproduceReentry.py) induceReentry.py Contains a list of pacing protocols. The PEERP protocol is included here getStimPoints.py Extract the stimulation points element_tag.csv Region tag numbering al_mk_H.par Par file with ionic scaling factors for three states; H:Healthy, M:Mild, S:Severe requirements.txt Packages to create the virtual enviroment. (This was my output of pip3 list> requirements.txt) reproduceReentry.py Reentries can be reproduced given a selected folder where the .par and .roe files are stored. data: contains the meshes folder with the bilayer meshes in openCARP (.elem, .lon and .pts) and .vtk format. Synthetic fibrotic distributions are included in the the .regele files. meshes/P1/P1_with_erp_lat_bi.vtk Mesh with ERP, LAT and bipolar voltage data meshes/P1/ERP_values.txt/ measured ERP data meshes/P1/ERP.pts/ electrode coordinates where ERP data was measured meshes/P1/ablation.pts/ electrode coordinates where tissue was ablated meshes/P1/LA_stim_points_2cm.pts Stimulation points for the PEERP protocol  meshes/P1/bilayer/LA_bilayer_with_fiber_slow_conductive.regele Element ids corresponding to regions of low voltage (< 0.5mV) meshes/P1/bilayer/LA_bilayer_with_fiber_scar.regele Element ids corresponding to regions of low voltage (< 0.1mV) meshes/P1/bilayer/LA_bilayer_with_erp_regions_um.vtk Bilayer mesh with a discrete split where each region has a single ERP value meshes/P1/bilayer/LA_bilayer_with_fiber_with_fibrosis.vtk Bilayer mesh with fibrosis informed by low voltage areas meshes/P1/bilayer/LA_bilayer_with_erp_continuous_um.vtk Bilayer mesh with a continuous ERP distribution by interpolation of measured ERP data meshes/P1/bilayer/LA_bilayer_with_erp_continuous_2ms_um.vtk Bilayer mesh with a continuous ERP distribution by interpolation of measured ERP data with +- 2ms perturbation We studied 7 different scenarios:  Monoregion scenario with no ERP personalization, where all nodes had the same ERP Control scenario with no ERP personalization, where ERP nodes of certain defined anatomical regions where modified as reported in Loewe et al. 2015   Regional scenario with ERP personalization, where each region had a single ERP value derived from clinical measurement Continuous scenario with ERP personalization, where the ERP distribution was generated by interpolation of measured ERP data Control scenario with fibrosis, where elements corresponding to regions of low voltage (bi<0.5 mV) where set as slow or non conducing elements Continuous scenario with fibrosis, where elements corresponding to regions of low voltage (bi<0.5 mV) where set as slow or non conducing elements Continuous scenario where ERP measurements with additional perturbation draw from a uniform distribution. The perturbations were 2,5,10 and 20 ms, and we repeated this set 5 times for P6 In summary, we provide the following data:  7 meshes for openCARP simulations 7 meshes in vtk format with continuous ERP distribution 27 meshes in vtk format with continuous ERP distribution with perturbed ERP with 2,5,10 and 20ms from a random uniform distribution 7 meshes in vtk format with regional ERP 7 meshes in vtk format with ERP, LAT and bipolar voltage 7 ablation set points 7 ERP set points with their corresponding values 209 reentries generated under 4 ERP scenarios (monoregion, control,regional,continuous) run with a conduction velocity of 0.7 0.5 and 0.3 m/s 26 reentries generated under 2 scenarios ERP+Fibrosis (control + continuous) run with a conduction velocity 0.3 m/s 37 reentries induced with continuous ERP for patient P3 @CV 0.3 for the sensitivity analysis  Create a dynamic Courtemanche model As we will modify the ionic parameters on a nodel basis you will need to create a dynamic Courtemanche model and then declare the variables (ionic conductances) you need to modify. In your openCARP installation folder, go to the limpet copy the Courtemanche.model file cd openCARP/physics/model/limpetcp Courtemanche.model Courtemanche_nodal.modelvim Courtemanche_nodal.model Then add on top the parameters that need to be modified on a nodal-basis: group {  GK1           ;  Gto           ;  GKr           ;  GKs           ;  GCaL          ;  factorGKur    ;  maxINaCa      ;  maxIpCa       ;} .nodal(); Then you would need to recompile openCARP. In the terminal, go to your openCARP's top level folder:cd openCARP/ Configure CMake with updated imp_list.txt via:cmake -S. -B_build -DUPDATE_IMPLIST=ONRun the CMake building process:cmake --build _build This will generate the .h and .cc files for your dynamic model inside physics/limpet/src/imps_src Note: If you want to add or modify a model file after openCARP was compiled, it is possible to first clean the previous generated files during compilation by running make clean before recompiling openCARP. If you compile your own version of openCARP, then you can modify the settings.yaml file, to point to your openCARP version with the dynamic model.cd .config/carputilssubl settings.yamlAdd the build name: CARP_EXE_DIR:    CPU: /Users/lm104/Documents/OpenCARP/opencarp/_build/bin    NODAL: /Users/lm104/Documents/OpenCARP/openCARP_nodal_adj/_build/binYou can check that the new dynamic model is there by calling benchbench -—list-impsbench —-imp Courtemanche_nodal  --imp-info You can find additional information about dynamic models here. Reproduce the reentries  You can generate the .igb file of a specific reentry by selecting the corresponding folder in the results directory. An example is given to reproduce the reentry in P1_bi_M_LA/point_0_beat_2/reproduce_reentry.igb. Select the folder --par_file_directoryand set --tend to define the duration of the simulation in miliseconds. HINT: We recommend keeping the folder structure so that the other parameters, such as: mesh, scenario, state and chamber, can be read from the --par_file_directory. Otherwise, the meshes and results directories need to be modified. cd src/ reproduceReentry.py  --par_file_directory ../results/P1_bi_M_LA/point_0_beat_2 --tend 1500   Preparation before running the PEERP pacing protocol Follow the next steps if you want to run the PEERP pacing protocol, either for the provided meshes or for your own meshes. To run the PEERP protocol in a controlled environment, it is recommended, before running the run.py, to create a virtual environment. Go to your terminal and type:  cd src/ python3 -m venv ./myEnv source ./myEnv/bin/activate pip3 install -r requirements.txt   You need to add carputils to your PATH. You can run the code in the terminal or use and IDE to debug the code.  Note: I am using PyCharm 2020.3. and in Settings --> Python interpreter --> show all and then in the (+) symbol, add the path to carputils there:   Otherwise you can add this extra lines at the beginning of run.py``: ``` # Replace '/path/to/carputils' with the actual path to your carputils package carputils_path = '/path/to/carputils'   # Add the carputils path to sys.path sys.path.append(carputils_path) ``` Run the PEERP protocol   The following example runs the PEERP from a single stimulation point. If you want to run PEERP over all the points, simply add the flag --run_all_points 1  ``` cd src/ python3 run.py --giL 0.4166 --geL 1.458 --cv 0.8 --mesh monoatrial --protocol PEERP --pacing 122718 --stim_file LA_stim_points.txt --geometry LA_bilayer_with_fiber_um --cell_bcl 500 --model Courtemanche --ionic_prop_file al_mk_S.par --max_n_beats_PEERP 1 --overwrite-behaviour overwrite ```   Running your own experiment and making your own changes Extract the stimulation points on your mesh, where the PEERP protocol will be run:  ``` python3 getStimPoints.py   --mesh monoatrial --tolerance 20000 --stim_file LA_stim_points.txt --chamber LA ```   Tune conduction velocity (CV) and conductivites. The code expects the intracellular end extracellular longitudinal conductivity values as an input. We usedtuneCVto fit CV=0.7m/s with dx=0.4mm and dt=20us If you want to adjust the values, run in the terminal: ``` tuneCV --resolution 400 --model Courtemanche --velocity 0.7 --converge True --sourceModel monodomain --surf True --dt 20 ``` You can provide the location of the start of the activation by selecting the desired point ID: - Load the mesh in Paraview (or Meshalyzer) - click on the ? symbol - save the ID and change the--pacingargument    Callrun.pywith a new mesh. The protocol starts by prepacing the mesh and then using the last beat as initial condition tu run the PEERP. Be aware that for a monoatrial mesh you might need to give the new id for the location of the earliest activation. Change12345to your desired point ID. ``` python3 run.py --mesh newMesh --pacing 12345 --protocol prepace --stim_file LA_stim_points.txt ```   Run the protocol with different electrical remodelling stage. You can change the .par file or select one file from the three provided:  ``` python3 run.py --mesh newMesh --pacing 12345 --protocol PEERP --stim_file LA_stim_points.txt --args.ionic_prop 'l_mk_M.par' ```   You can also try to run a biatrial example. The biatrial mesh is also provided. You need to extract the points on the RA surface usinggetStimPoints.pyto run the RA experiment: cd src python3 getStimPoints.py   --mesh biatrial --tolerance 20000 --stim_file RA_stim_points.txt --chamber RA Then run PEERP twice, one per each chamber: python3 run.py --mesh biatrial --geometry LA_RA_bilayer_with_fiber --pacing 12345 --stim_file LA_endo_2cm.txt --args.ionic_prop 'l_mk_M.par' python3 run.py --mesh biatrial --geometry LA_RA_bilayer_with_fiber --stim_file LA_stim_points.txt --args.ionic_prop 'l_mk_M.par'