Personalized virtual-heart technology for guiding the ablation of infarct-related ventricular tachycardia

Prakosa, Adityo; Arevalo, Hermenegild; Deng, Dongdong; Boyle, Patrick; Nikolov, Plamen; Ashikaga, Hiroshi; Blauer, Joshua; Ghafoori, Elyar; Park, Carolyn; Blake, Robert C.; Han, Frederick T.; MacLeod, Robert S.; Halperin, Henry R.; Callans, David J.; Ranjan, Ravi; Chrispin, Jonathan; Nazarian, Saman; Trayanova, Natalia A.

doi:10.1038/s41551-018-0282-2

Cited by 220 publications

(244 citation statements)

References 42 publications

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“…Ultimately, with a view towards precision cardiology, our approach has the potential to combine personalized the block-concentration characteristics and personalized cardiac geometries towards identifying the optimal course of care for each individual patient 41,42 .…”

Section: Kr and I Cal Modulate The Onset Of Torsades De Pointesmentioning

confidence: 99%

Classifying drugs by their arrhythmogenic risk using machine learning

Costabal¹,

Seo

Ashley

et al. 2019

Preprint

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All medications have adverse effects. Among the most serious of these are cardiac arrhythmias. Current paradigms for drug safety evaluation are costly, lengthy, conservative, and impede efficient drug development. Here we combine multiscale experiment and simulation, high-performance computing, and machine learning to create a risk estimator to stratify new and existing drugs according to their pro-arrhythmic potential. We capitalize on recent developments in machine learning and integrate information across ten orders of magnitude in space and time to provide a holistic picture of the effects of drugs, either individually or in combination with other drugs. We show, both experimentally and computationally, that drug-induced arrhythmias are dominated by the interplay between two currents with opposing effects: the rapid delayed rectifier potassium current and the L-type calcium current. Using Gaussian process classification, we create a classifier that stratifies drugs into safe and arrhythmic domains for any combinations of these two currents. We demonstrate that our classifier correctly identifies the risk categories of 23 common drugs, exclusively on the basis of their concentrations at 50% current block. Our new risk assessment tool explains under which conditions blocking the L-type calcium current can delay or even entirely suppress arrhythmogenic events. Using machine learning in drug safety evaluation can provide a more accurate and comprehensive mechanistic assessment of the pro-arrhythmic potential of new drugs. Our study paves the way towards establishing science-based criteria to accelerate drug development, design safer drugs, and reduce heart rhythm disorders.

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Section: Kr and I Cal Modulate The Onset Of Torsades De Pointesmentioning

confidence: 99%

Classifying drugs by their arrhythmogenic risk using machine learning

Costabal¹,

Seo

Ashley

et al. 2019

Preprint

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Section: Application Of Virtual‐heart Models To Vt Ablation In Post‐imentioning

confidence: 99%

“…Non‐injured and scar tissues are shown in red and yellow, respectively. (Reprinted from Prakosa et al (). Copyright 2018 Springer Nature.Permission exempt per https://www.nature.com/nature‐research/reprints‐and‐permissions/permissions‐requests)…”

Section: Application Of Virtual‐heart Models To Vt Ablation In Post‐imentioning

confidence: 99%

How personalized heart modeling can help treatment of lethal arrhythmias: A focus on ventricular tachycardia ablation strategies in post‐infarction patients

Trayanova

Doshi

Prakosa

2020

WIREs Mechanisms of Disease

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Precision Cardiology is a targeted strategy for cardiovascular disease prevention and treatment that accounts for individual variability. Computational heart modeling is one of the novel approaches that have been developed under the umbrella of Precision Cardiology. Personalized computational modeling of patient hearts has made strides in the development of models that incorporate the individual geometry and structure of the heart as well as other patient‐specific information. Of these developments, one of the potentially most impactful is the research aimed at noninvasively predicting the targets of ablation of lethal arrhythmia, ventricular tachycardia (VT), using patient‐specific models. The approach has been successfully applied to patients with ischemic cardiomyopathy in proof‐of‐concept studies. The goal of this paper is to review the strategies for computational VT ablation guidance in ischemic cardiomyopathy patients, from model developments to the intricacies of the actual clinical application. To provide context in describing the road these computational modeling applications have undertaken, we first review the state of the art in VT ablation in the clinic, emphasizing the benefits that personalized computational prediction of ablation targets could bring to the clinical electrophysiology practice. This article is characterized under: Analytical and Computational Methods > Computational Methods Models of Systems Properties and Processes > Organ, Tissue, and Physiological Models Translational, Genomic, and Systems Medicine > Translational Medicine

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“…Further, it has been shown that ionic flow 15 through cell junctions can take up to 50% of the total conduction time in cultured 16 strands of myocytes with normal coupling levels [4], and that conduction velocity is 17 predominantly controlled by the level of gap-junctional communication [5], which 18 highlights the key physiological relevance of gap-junction conductivity and coupling in 19 tissue electrical conduction. 20 Cardiac modeling and simulation has strongly motivated the development of 21 tissue-level mathematical models of electrophysiology, as they have the ability to 22 connect subcellular mechanisms to whole-organ behavior [6]. To date, the vast majority 23 of continuum models assume a linear conduction model of spatial communication, based 24 on the assumption that electrical current in cardiac tissue follows Ohm's law, i.e, that 25 current is linearly proportional to gradients in the intracellular potential [7,8].…”

mentioning

confidence: 99%

“…Finally, the applicability of the NOM model should tested in the simulation of 179 conduction in the whole heart during diseased conditions [22]. Schematic of the multiscale model of cardiac conduction.…”

mentioning

confidence: 99%

Non-ohmic tissue conduction in cardiac electrophysiology: upscaling the non-linear voltage-dependent conductance of gap junctions

Hurtado

Jilberto

Panasenko

2019

Preprint

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Gap junctions are key mediators of the intercellular communication in cardiac tissue, and their function is vital to sustain normal cardiac electrical activity. Conduction through gap junctions strongly depends on the hemichannel arrangement and transjunctional voltage, rendering the intercellular conductance highly non-Ohmic.Despite this marked non-linear behavior, current tissue-level models of cardiac conduction are rooted on the assumption that gap-junctions conductance is constant (Ohmic), which results in inaccurate predictions of electrical propagation, particularly in the low junctional-coupling regime observed under pathological conditions. In this work, we present a novel non-Ohmic multiscale (NOM) model of cardiac conduction that is suitable for tissue-level simulations. Using non-linear homogenization theory, we develop June 28, 2019 1/17 a conductivity model that seamlessly upscales the voltage-dependent conductance of gap junctions, without the need of explicitly modeling gap junctions. The NOM model allows for the simulation of electrical propagation in tissue-level cardiac domains that accurately resemble that of cell-based microscopic models for a wide range of junctional coupling scenarios, recovering key conduction features at a fraction of the computational complexity. A unique feature of the NOM model is the possibility of upscaling the response of non-symmetric gap-junction conductance distributions, which result in conduction velocities that strongly depend on the direction of propagation, thus allowing to model the normal and retrograde conduction observed in certain regions of the heart. We envision that the NOM model will enable organ-level simulations that are informed by sub-and inter-cellular mechanisms, delivering an accurate and predictive in-silico tool for understanding the heart function. Author summaryThe heart relies on the propagation of electrical impulses that are mediated gap junctions, whose conduction properties vary depending on the transjunctional voltage. Despite this non-linear feature, current mathematical models assume that cardiac tissue behaves like an Ohmic (linear) material, thus delivering inaccurate results when simulated in a computer. Here we present a novel mathematical multiscale model that explicitly includes the non-Ohmic response of gap junctions in its predictions. Our results show that the proposed model recovers important conduction features modulated by gap junctions at a fraction of the computational complexity. This contribution represents an important step towards constructing computer models of a whole heart that can predict organ-level behavior in reasonable computing times. 33 cable (monodomain) model of cardiac electrophysiology [9].34 Using two-scale asymptotic homogenization techniques, analytic expressions have 35 been obtained for the effective conductivity tensor, which is then used to model the 36 electrical current in an average macroscopic sense [10-12]. To this end, periodicity at 37 June 28, 2019 3/17 65 compared with those obtained...

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Personalized virtual-heart technology for guiding the ablation of infarct-related ventricular tachycardia

Cited by 220 publications

References 42 publications

Classifying drugs by their arrhythmogenic risk using machine learning

Classifying drugs by their arrhythmogenic risk using machine learning

How personalized heart modeling can help treatment of lethal arrhythmias: A focus on ventricular tachycardia ablation strategies in post‐infarction patients

Non-ohmic tissue conduction in cardiac electrophysiology: upscaling the non-linear voltage-dependent conductance of gap junctions

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