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Computational models in cardiology

Nature Reviews Cardiology volume 16, pages 100–111 (2019)Cite this article

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Abstract

The treatment of individual patients in cardiology practice increasingly relies on advanced imaging, genetic screening and devices. As the amount of imaging and other diagnostic data increases, paralleled by the greater capacity to personalize treatment, the difficulty of using the full array of measurements of a patient to determine an optimal treatment seems also to be paradoxically increasing. Computational models are progressively addressing this issue by providing a common framework for integrating multiple data sets from individual patients. These models, which are based on physiology and physics rather than on population statistics, enable computational simulations to reveal diagnostic information that would have otherwise remained concealed and to predict treatment outcomes for individual patients. The inherent need for patient-specific models in cardiology is clear and is driving the rapid development of tools and techniques for creating personalized methods to guide pharmaceutical therapy, deployment of devices and surgical interventions.

Key points

  • Computational models of the heart have an important and growing role in cardiology, enabling patients to be diagnosed and treated on the basis of their specific pathophysiology.

  • Simulations provide the link between the effects of genetic mutations, physiological regulations or drugs on protein function and emergent cellular and tissue function or clinical phenotypes.

  • Models representing an individual patient or a specific pathology are now used to identify the mechanisms underpinning a disease, improve patient selection and predict clinical outcomes.

  • Predictive modelling also contributes to the development of new diagnostics and devices and to the tailoring of therapies for individual patients.

  • Translational barriers remain regarding model personalization, speed and detail of the simulations and how to communicate model predictions to cardiologists within a clinical environment.

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Fig. 1: How can computational models improve current cardiology care?
Fig. 2: Modelling cardiac cells, channel mutations and drug response.
Fig. 3: Linking clinical data to computer models of the heart with increasing levels of anatomical detail.

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Acknowledgements

S.A.N. acknowledges support from the UK Engineering and Physical Sciences Research Council (EP/M012492/1, NS/A000049/1 and EP/P01268X/1), the British Heart Foundation (PG/15/91/31812 and PG/13/37/30280) and King’s Health Partners London National Institute for Health Research (NIHR) Biomedical Research Centre. J.L. acknowledges support from the Dr. Dekker Program of the Dutch Heart Foundation (grant 2015T082) and the Netherlands Organisation for Scientific Research (NWO-ZonMw, VIDI grant 016.176.340). N.A.T. acknowledges support from Leducq Foundation and from the NIH (grants DP1-HL123271 and R01HL116280). M. Strocchi (King’s College London, UK) provided assistance with creating the four-chamber heart images in figure 3.

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Nature Reviews Cardiology thanks B. Rodriguez and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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Authors and Affiliations

  1. Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK

    Steven A. Niederer

  2. Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Center, Maastricht, Netherlands

    Joost Lumens

  3. IHU LIRYC, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Pessac, France

    Joost Lumens

  4. Department of Biomedical Engineering and the Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA

    Natalia A. Trayanova

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All the authors researched data for the article, contributed substantially to the discussion of the content, and wrote, reviewed and edited the manuscript before submission.

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S.A.N. has received support from Abbott, Boston Scientific, Edwards Lifesciences, Pfizer, Roche and Siemens. J.L. has received support from Medtronic. N.A.T. declares no competing interests.

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Niederer, S.A., Lumens, J. & Trayanova, N.A. Computational models in cardiology. Nat Rev Cardiol 16, 100–111 (2019). https://doi.org/10.1038/s41569-018-0104-y

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