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Review
. 2019 Feb;16(2):100-111.
doi: 10.1038/s41569-018-0104-y.

Computational models in cardiology

Steven A Niederer  1 , Joost Lumens  2   3 , Natalia A Trayanova  4
Affiliations
Review

Computational models in cardiology

Steven A Niederer et al. Nat Rev Cardiol. 2019 Feb.

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.

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Conflict of interest statement

Competing interests

N.A.T. declares no competing interests.

Figures

Fig. 1
Fig. 1. How can computational models improve current cardiology care?
A computational model of the human heart and circulation enables synergistic integration of multiple diagnostic data obtained with the use of different clinical modalities (such as echocardiography, MRI, electrocardiography, genetics and blood-pressure measurements) in one personalized heart simulation on the basis of widely accepted physical and physiological principles. The personalized integrative nature of such a virtual-patient simulation adds value to the existing clinical workflow by offering more quantitative and objective insight in the underlying disease substrates of a patient. In addition, the model provides a platform for virtual evaluation and optimization of a therapy.
Fig. 2
Fig. 2. Modelling cardiac cells, channel mutations and drug response.
a | Schematic showing the level of detail in a cardiac cell model. The model is separated into the transverse (T)-tubule, subsarcolemma, cytosol, junctional sarcoplasmic reticulum (JSR) and network sarcoplasmic reticulum (NSR). The model shows the different ionic currents: Na+ current (INa), K+ currents (transient outward K+ current (Ito), rapid component of the delayed rectifier K+ current (IKr), slow component of the delayed rectifier K+ current (IKs) and inward rectifier K+ current (IK1)), Na+–K+ exchange current (INaK), Ca2+ currents from L-type Ca2+ channels (ICaL, ICaNa and ICaK), current from the membrane-bound Ca2+ pump (ICa), cytosol and subsarcolemmal Na+–Ca2+ exchanger currents (INaCa) and Ca2+ currents (ICa) from the ryanodine receptor and sarcoplasmic reticulum Ca2+ pump. The model includes intracellular Ca2+ fluxes within the sarcoplasmic reticulum, fluxes of Na+, K+ (IK) and Ca2+ between the subsarcolemmal space and the cytosol, and Ca2+ buffers (calsequestrin (CASQ), calmodulin (CALM), troponin C1 (TNNC1), Ca2+/calmodulin-dependent protein kinase (CaMK), anionic sarcoplasmic reticulum binding site for Ca2+ (BSR) and anionic sarcolemmal binding site for Ca2+ (BSL)). b | Schematic of the model of the structure of a wild-type Na+ channel in its three possible states: closed, open (O), and inactivated (fast (F) or slow (S)). The structure of the channel is changed with the addition with the ΔKPQ mutation. c | Effects of a channel inhibitor compound on the Na+ channel current. The current is calculated by: the channel conductance (gNa); proportion of channels in the open (Po), fast-inactivated or slow-inactivated states; voltage across the cell membrane (Vm); Nernst potential for Na+ (ENa); and the proportion of channels that are not inhibited by the compound (γ). The proportion of channels that are not inhibited by the compound is defined by a Hill equation with cooperativity (n) and the half-inhibition concentration (IC50).
Fig. 3
Fig. 3. Linking clinical data to computer models of the heart with increasing levels of anatomical detail.
a | Left to right: echocardiography strain measurements are used to constrain a simplified anatomical model, linked to a closed-loop cardiovascular system representation that can generate model predictions of tissue properties. b | Left to right: multimodality MRI data (cine, late gadolinium enhancement, anatomical) can be used to create anatomical models of the ventricles, highlighting structural remodelling (shown in the middle image with labels for viable tissue (pink), border zone (green) and infarct (yellow)); fibre fields can be added to the model and simulations of arrhythmia performed. c | Left to right: cardiac CT data segmented to label ventricular and atrial myocardium; fibre fields are generated across the four cardiac chambers, and simulations of atrial and ventricular physiology are performed. Max, maximum; Min, minimum. Part b is adapted from REF., Springer Nature Limited.
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