Nothing Special   »   [go: up one dir, main page]

skip to main content
research-article

ForCEPSS—A framework for cardiac electrophysiology simulations standardization

Published: 18 July 2024 Publication History

Abstract

Background and Objective:

Simulation of cardiac electrophysiology (CEP) is an important research tool that is increasingly being adopted in industrial and clinical applications. Typical workflows for CEP simulation consist of a sequence of processing stages starting with building an anatomical model and then calibrating its electrophysiological properties to match observable data. While the calibration stages are common and generalizable, most CEP studies re-implement these steps in complex and highly variable workflows. This lack of standardization renders the execution of computational CEP studies in an efficient, robust, and reproducible manner a significant challenge. Here, we propose ForCEPSS as an efficient and robust, yet flexible, software framework for standardizing CEP simulation studies.

Methods and Results:

Key processing stages of CEP simulation studies are identified and implemented in a standardized workflow that builds on openCARP https://opencarp.org/. Plank et al. (2021) and the Python-based carputils https://git.opencarp.org/openCARP/carputils. framework. Stages include (i) the definition and initialization of action potential phenotypes, (ii) the tissue scale calibration of conduction properties, (iii) the functional initialization to approximate a limit cycle corresponding to the dynamic reference state according to an experimental protocol, and, (iv) the execution of the CEP study where the electrophysiological response to a perturbation of the limit cycle is probed. As an exemplar application, we employ ForCEPSS to prepare a CEP study according to the Virtual Arrhythmia Risk Prediction protocol used for investigating the arrhythmogenic risk of developing infarct-related ventricular tachycardia (VT) in ischemic cardiomyopathy patients. We demonstrate that ForCEPSS enables a fully automated execution of all stages of this complex protocol.

Conclusion:

ForCEPSS offers a novel comprehensive, standardized, and automated CEP simulation workflow. The high degree of automation accelerates the execution of CEP simulation studies, reduces errors, improves robustness, and makes CEP studies reproducible. Verification of simulation studies within the CEP modeling community is thus possible. As such, ForCEPSS makes an important contribution towards increasing transparency, standardization, and reproducibility of in silico CEP experiments.

Highlights

Open-source framework for standardizing cardiac electrophysiology simulations.
Built upon cardiac electrophysiology modeling community standard software, the openCARP simulator and the carputils framework.
Simplifies model calibration, avoids calibration errors, leading to valid and reproducible cardiac electrophysiology studies.
Efficient streamlining and automation of cardiac electrophysiology modeling studies, ensuring quality and credibility of in silico studies.

References

[1]
Campos F.O., Shiferaw Y., Prassl A.J., Boyle P.M., Vigmond E.J., Plank G., Stochastic spontaneous calcium release events trigger premature ventricular complexes by overcoming electrotonic load, Cardiovasc. Res. 107 (1) (2015) 175–183,.
[2]
Swenson D.J., Taepke R.T., Blauer J.J., Kwan E., Ghafoori E., Plank G., Vigmond E., MacLeod R.S., DeGroot P., Ranjan R., Direct comparison of a novel antitachycardia pacing algorithm against present methods using virtual patient modeling, Hear. Rhythm 17 (9) (2020) 1602–1608,.
[3]
Monaci S., Strocchi M., Rodero C., Gillette K., Whitaker J., Rajani R., Rinaldi C.A., O’Neill M., Plank G., King A., Bishop M.J., In-silico pace-mapping using a detailed whole torso model and implanted electronic device electrograms for more efficient ablation planning, Comput. Biol. Med. 125 (September) (2020),. http://www.ncbi.nlm.nih.gov/pubmed/32971325.
[4]
Strocchi M., Augustin C.M., Gsell M.A.F., Karabelas E., Neic A., Gillette K., Razeghi O., Prassl A.J., Vigmond E.J., Behar J.M., Gould J., Sidhu B., Rinaldi C.A., Bishop M.J., Plank G., Niederer S.A., A publicly available virtual cohort of four-chamber heart meshes for cardiac electro-mechanics simulations, PLoS One 15 (6) (2020),.
[5]
Arevalo H.J., Vadakkumpadan F., Guallar E., Jebb A., Malamas P., Wu K.C., Trayanova N.A., Arrhythmia risk stratification of patients after myocardial infarction using personalized heart models, Nature Commun. 7 (2016) 11437,.
[6]
Prakosa A., Arevalo H.J., Deng D., Boyle P.M., Nikolov P.P., Ashikaga H., Blauer J.J.E., Ghafoori E., Park C.J., Blake R.C., Han F.T., MacLeod R.S., Halperin H.R., Callans D.J., Ranjan R., Chrispin J., Nazarian S., Trayanova N.A., Personalized virtual-heart technology for guiding the ablation of infarct-related ventricular tachycardia, Nat. Biomed. Eng. (2018),.
[7]
Corral-Acero J., Margara F., Marciniak M., Rodero C., Loncaric F., Feng Y., Gilbert A., Fernandes J.F., Bukhari H.A., Wajdan A., Martinez M.V., Santos M.S., Shamohammdi M., Luo H., Westphal P., Leeson P., DiAchille P., Gurev V., Mayr M., Geris L., Pathmanathan P., Morrison T., Cornelussen R., Prinzen F., Delhaas T., Doltra A., Sitges M., Vigmond E.J., Zacur E., Grau V., Rodriguez B., Remme E.W., Niederer S., Mortier P., McLeod K., Potse M., Pueyo E., Bueno-Orovio A., Lamata P., The ’digital twin’ to enable the vision of precision cardiology, Eur. Heart J. (2020),.
[8]
Sung E., Prakosa A., Zhou S., Berger R.D., Chrispin J., Nazarian S., Trayanova N.A., Fat infiltration in the infarcted heart: A new paradigm for ventricular arrhythmias, Nat. Cardiovasc. Res. 1 (Accepted for Publication) (2022),.
[9]
openCARP consortium E., Augustin C., Bayer J., Bishop M., Caforio F., Campos F., Costa C.M., Crozier A., Fastl T., Gillette K., Gsell M., Huang Y.-L., Karabelas E., Karabelas E., Loewe A., Marx L., Neic A., Nothstein M., Plank G., Prassl A., Seemann G., Sánchez J., Vigmond E., Wülfers E.M., Opencarp (v5.0), 2021,.
[10]
Clerc L., Directional differences of impulse spread in trabecular muscle from mammalian heart, J. Physiol. 255 (1976) 335–346.
[11]
Roberts D.E., Hersh L.T., Scher a.M., Influence of cardiac fiber orientation on wavefront voltage, conduction velocity, and tissue resistivity in the dog, Circ. Res. 44 (5) (1979) 701–712. URL http://www.ncbi.nlm.nih.gov/pubmed/428066.
[12]
Roberts D.E., Scher A.M., Effect of tissue anisotropy on extracellular potential fields in canine myocardium in situ, Circ. Res. 50 (3) (1982) 342–351,. URL http://www.ncbi.nlm.nih.gov/pubmed/7060230.
[13]
Gillette K., Gsell M., Prassl A., Karabelas E., Reiter U., Reiter G., Grandits T., Stern D., Urschler M., Bayer J., Augustin C., Neic A., Pock T., Vigmond E., Plank G., A framework for the generation of digital twins of cardiac electrophysiology from clinical 12-leads ECGs, Med. Imag. Anal. (2021),.
[14]
ten Tusscher K.H.W.J., Panfilov A.V., Alternans and spiral breakup in a human ventricular tissue model, Am. J. Physiol. Heart Circ. Physiol. 291 (3) (2006) H1088–100,.
[15]
Plank G., Loewe A., Neic A., Augustin C., Huang Y.-L., Gsell M.A., Karabelas E., Nothstein M., Prassl A.J., Sánchez J., Seemann G., Vigmond E.J., The openCARP simulation environment for cardiac electrophysiology, Comput. Methods Programs Biomed. 208 (2021),. URL https://linkinghub.elsevier.com/retrieve/pii/S0169260721002972.
[16]
Bayer J., Prassl A.J., Pashaei A., Gomez J.F., Frontera A., Neic A., Plank G., Vigmond E.J., Universal ventricular coordinates: A generic framework for describing position within the heart and transferring data, Med. Image Anal. 45 (2018) 83–93,.
[17]
Roney C.H., Pashaei A., Meo M., Dubois R., Boyle P.M., Trayanova N.A., Cochet H., Niederer S.A., Vigmond E.J., Universal atrial coordinates applied to visualisation, registration and construction of patient specific meshes, Med. Image Anal. 55 (2019) 65–75,. arXiv:1810.06630.
[18]
Pezoa F., Reutter J.L., Suarez F., Ugarte M., Vrgoč D., Foundations of JSON schema, in: Proceedings of the 25th International Conference on World Wide Web, WWW ’16, International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 2016, pp. 263–273,.
[19]
Johnston B.M., Six conductivity values to use in the bidomain model of cardiac tissue, IEEE Trans. Biomed. Eng. 63 (7) (2016) 1525–1531,.
[20]
Potse M., Dubé B., Richer J., Vinet A., Gulrajani R.M., A comparison of monodomain and bidomain reaction-diffusion models for action potential propagation in the human heart, IEEE Trans. Biomed. Eng. 53 (12) (2006) 2425–2435,.
[21]
Bishop M.J., Plank G., Representing cardiac bidomain bath-loading effects by an augmented monodomain approach: Application to complex ventricular models, IEEE Trans. Biomed. Eng. 58 (4) (2011) 1066–1075,.
[22]
O’Hara T., Virag L., Varro A., Rudy Y., Simulation of the undiseased human cardiac ventricular action potential: Model formulation and experimental validation, PLoS Comput. Biol. 7 (5) (2011),.
[23]
Kalb S.S., Dobrovolny H.M., Tolkacheva E.G., Idriss S.F., Krassowska W., Gauthier D.J., The restitution portrait: A new method for investigating rate-dependent restitution, J. Cardiovasc.ular Electrophysiol. 15 (6) (2004) 698–709,. URL http://www.ncbi.nlm.nih.gov/pubmed/15175067.
[24]
Roth B.J., Electrical conductivity values used with the bidomain model of cardiac tissue, IEEE Trans. Bio-Med. Eng. 44 (4) (1997) 326–328,. URL http://www.ncbi.nlm.nih.gov/pubmed/9125816.
[25]
Plank G., Leon L.J., Kimber S., Vigmond E.J., Defibrillation depends on conductivity fluctuations and the degree of disorganization in reentry patterns, J. Cardiovasc. Electrophysiol. 16 (2) (2005) 205–216,.
[26]
Gillette K., Gsell M.A., Bouyssier J., Prassl A.J., Neic A., Vigmond E.J., Plank G., Automated framework for the inclusion of a his–purkinje system in cardiac digital twins of ventricular electrophysiology, Ann. Biomed. Eng. 49 (12) (2021) 3143–3153.
[27]
Roth B.J., How to explain why “unequal anisotropy ratios” is important using pictures but no mathematics, vol. 1 (2006) 580–583,. URL http://www.ncbi.nlm.nih.gov/pubmed/17946406.
[28]
Sepulveda N.G., Roth B.J., Wikswo J.P., Current injection into a two-dimensional anisotropic bidomain, Biophys. J. 55 (5) (1989) 987–999,. URL http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1330535&tool=pmcentrez&rendertype=abstract.
[29]
Latimer D.C., Roth B.J., Electrical stimulation of cardiac tissue by a bipolar electrode in a conductive bath, IEEE Trans. Bio-Med. Eng. 45 (12) (1998) 1449–1458,. http://www.ncbi.nlm.nih.gov/pubmed/9835193. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=730438.
[30]
Neic A., Campos F.O., Prassl A.J., Niederer S.A., Bishop M.J., Vigmond E.J., Plank G., Efficient computation of electrograms and ECGs in human whole heart simulations using a reaction-eikonal model, J. Comput. Phys. 346 (2017) 191–211,.
[31]
Gillette K., Gsell M.A., Nagel C., Bender J., Winkler B., Williams S.E., Bär M., Schäffter T., Dössel O., Plank G., et al., MedalCare-XL: 16,900 healthy and pathological synthetic 12 lead ECGs from electrophysiological simulations, Sci. Data 10 (1) (2023) 531.

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Computer Methods and Programs in Biomedicine
Computer Methods and Programs in Biomedicine  Volume 251, Issue C
Jun 2024
244 pages

Publisher

Elsevier North-Holland, Inc.

United States

Publication History

Published: 18 July 2024

Author Tags

  1. openCARP
  2. Cardiac electrophysiology
  3. Standardized workflow

Qualifiers

  • Research-article

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 0
    Total Downloads
  • Downloads (Last 12 months)0
  • Downloads (Last 6 weeks)0
Reflects downloads up to 23 Sep 2024

Other Metrics

Citations

View Options

View options

Get Access

Login options

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media