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Translational applications of computational modelling for patients with cardiac arrhythmias
  1. Savannah F Bifulco1,
  2. Nazem Akoum2,
  3. Patrick M Boyle1,3,4
  1. 1 Department of Bioengineering, University of Washington, Seattle, Washington, USA
  2. 2 Department of Cardiology, University of Washington, Seattle, Washington, USA
  3. 3 Center for Cardiovascular Biology, University of Washington, Seattle, WA, USA
  4. 4 Institute for Stem Cell & Regenerative Medicine, University of Washington, Seattle, WA, USA
  1. Correspondence to Dr Patrick M Boyle, Bioengineering, University of Washington, Seattle, Washington, USA; pmjboyle{at}uw.edu

Abstract

Cardiac arrhythmia is associated with high morbidity, and its underlying mechanisms are poorly understood. Computational modelling and simulation approaches have the potential to improve standard-of-care therapy for these disorders, offering deeper understanding of complex disease processes and sophisticated translational tools for planning clinical procedures. This review provides a clinician-friendly summary of recent advancements in computational cardiology. Organ-scale models automatically generated from clinical-grade imaging data are used to custom tailor our understanding of arrhythmia drivers, estimate future arrhythmogenic risk and personalise treatment plans. Recent mechanistic insights derived from atrial and ventricular arrhythmia simulations are highlighted, and the potential avenues to patient care (eg, by revealing new antiarrhythmic drug targets) are covered. Computational approaches geared towards improving outcomes in resynchronisation therapy have used simulations to elucidate optimal patient selection and lead location. Technology to personalise catheter ablation procedures are also covered, specifically preliminary outcomes form early-stage or pilot clinical studies. To conclude, future developments in computational cardiology are discussed, including improving the representation of patient-specific fibre orientations and fibrotic remodelling characterisation and how these might improve understanding of arrhythmia mechanisms and provide transformative tools for patient-specific therapy.

  • arrhythmias
  • cardiac
  • atrial fibrillation
  • tachycardia
  • ventricular
  • computer simulation
  • magnetic resonance imaging

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Footnotes

  • Twitter @SfBifulco, @NazemAkoum, @pmjboyle

  • Contributors All authors reviewed the relevant literature and wrote the article.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

  • Patient consent for publication Not required.

  • Provenance and peer review Commissioned; externally peer reviewed.

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