Publications by authors named "Steven A Niederer"

78 Publications

Computational modeling identifies embolic stroke of undetermined source patients with potential arrhythmic substrate.

Elife 2021 May 4;10. Epub 2021 May 4.

Bioengineering, University of Washington, Seattle, United States.

Cardiac magnetic resonance imaging (MRI) has revealed fibrosis in embolic stroke of undetermined source (ESUS) patients comparable to levels seen in atrial fibrillation (AFib). We used computational modeling to understand the absence of arrhythmia in ESUS despite the presence of putatively pro-arrhythmic fibrosis. MRI-based atrial models were reconstructed for 45 ESUS and 45 AFib patients. The fibrotic substrate's arrhythmogenic capacity in each patient was assessed computationally. Reentrant drivers were induced in 24/45 (53%) ESUS and 22/45 (49%) AFib models. Inducible models had more fibrosis (16.7±5.45%) than non-inducible models (11.07±3.61%; P<0.0001); however, inducible subsets of ESUS and AFib models had similar fibrosis levels (P=0.90), meaning the of fibrosis in ESUS and AFib are indistinguishable. This suggests some ESUS patients have latent pre-clinical fibrotic substrate that could be a future source of arrhythmogenicity. Thus, our work prompts the hypothesis that ESUS patients with fibrotic atria are spared from AFib due to an absence of arrhythmia triggers.
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http://dx.doi.org/10.7554/eLife.64213DOI Listing
May 2021

Linking statistical shape models and simulated function in the healthy adult human heart.

PLoS Comput Biol 2021 Apr 15;17(4):e1008851. Epub 2021 Apr 15.

Cardiac Electromechanics Research Group, Biomedical Engineering Department, King´s College London, London, United Kingdom.

Cardiac anatomy plays a crucial role in determining cardiac function. However, there is a poor understanding of how specific and localised anatomical changes affect different cardiac functional outputs. In this work, we test the hypothesis that in a statistical shape model (SSM), the modes that are most relevant for describing anatomy are also most important for determining the output of cardiac electromechanics simulations. We made patient-specific four-chamber heart meshes (n = 20) from cardiac CT images in asymptomatic subjects and created a SSM from 19 cases. Nine modes captured 90% of the anatomical variation in the SSM. Functional simulation outputs correlated best with modes 2, 3 and 9 on average (R = 0.49 ± 0.17, 0.37 ± 0.23 and 0.34 ± 0.17 respectively). We performed a global sensitivity analysis to identify the different modes responsible for different simulated electrical and mechanical measures of cardiac function. Modes 2 and 9 were the most important for determining simulated left ventricular mechanics and pressure-derived phenotypes. Mode 2 explained 28.56 ± 16.48% and 25.5 ± 20.85, and mode 9 explained 12.1 ± 8.74% and 13.54 ± 16.91% of the variances of mechanics and pressure-derived phenotypes, respectively. Electrophysiological biomarkers were explained by the interaction of 3 ± 1 modes. In the healthy adult human heart, shape modes that explain large portions of anatomical variance do not explain equivalent levels of electromechanical functional variation. As a result, in cardiac models, representing patient anatomy using a limited number of modes of anatomical variation can cause a loss in accuracy of simulated electromechanical function.
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http://dx.doi.org/10.1371/journal.pcbi.1008851DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8049237PMC
April 2021

OpenEP: A Cross-Platform Electroanatomic Mapping Data Format and Analysis Platform for Electrophysiology Research.

Front Physiol 2021 26;12:646023. Epub 2021 Feb 26.

Imperial College London, London, United Kingdom.

Background: Electroanatomic mapping systems are used to support electrophysiology research. Data exported from these systems is stored in proprietary formats which are challenging to access and storage-space inefficient. No previous work has made available an open-source platform for parsing and interrogating this data in a standardized format. We therefore sought to develop a standardized, open-source data structure and associated computer code to store electroanatomic mapping data in a space-efficient and easily accessible manner.

Methods: A data structure was defined capturing the available anatomic and electrical data. OpenEP, implemented in MATLAB, was developed to parse and interrogate this data. Functions are provided for analysis of chamber geometry, activation mapping, conduction velocity mapping, voltage mapping, ablation sites, and electrograms as well as visualization and input/output functions. Performance benchmarking for data import and storage was performed. Data import and analysis validation was performed for chamber geometry, activation mapping, voltage mapping and ablation representation. Finally, systematic analysis of electrophysiology literature was performed to determine the suitability of OpenEP for contemporary electrophysiology research.

Results: The average time to parse clinical datasets was 400 ± 162 s per patient. OpenEP data was two orders of magnitude smaller than compressed clinical data (OpenEP: 20.5 ± 8.7 Mb, vs clinical: 1.46 ± 0.77 Gb). OpenEP-derived geometry metrics were correlated with the same clinical metrics (Area: = 0.7726, < 0.0001; Volume: = 0.5179, < 0.0001). Investigating the cause of systematic bias in these correlations revealed OpenEP to outperform the clinical platform in recovering accurate values. Both activation and voltage mapping data created with OpenEP were correlated with clinical values (mean voltage = 0.8708, < 0.001; local activation time = 0.8892, < 0.0001). OpenEP provides the processing necessary for 87 of 92 qualitatively assessed analysis techniques (95%) and 119 of 136 quantitatively assessed analysis techniques (88%) in a contemporary cohort of mapping studies.

Conclusions: We present the OpenEP framework for evaluating electroanatomic mapping data. OpenEP provides the core functionality necessary to conduct electroanatomic mapping research. We demonstrate that OpenEP is both space-efficient and accurately representative of the original data. We show that OpenEP captures the majority of data required for contemporary electroanatomic mapping-based electrophysiology research and propose a roadmap for future development.
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http://dx.doi.org/10.3389/fphys.2021.646023DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7952326PMC
February 2021

Comparison of Left Atrial Ablation Techniques That Target the Anatomical, Structural, and Electrical Substrates of Atrial Fibrillation.

Front Physiol 2020 16;11:1145. Epub 2020 Sep 16.

School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.

Catheter ablation therapy for persistent atrial fibrillation (AF) typically includes pulmonary vein isolation (PVI) and may include additional ablation lesions that target patient-specific anatomical, electrical, or structural features. Clinical centers employ different ablation strategies, which use imaging data together with electroanatomic mapping data, depending on data availability. The aim of this study was to compare ablation techniques across a virtual cohort of AF patients. We constructed 20 paroxysmal and 30 persistent AF patient-specific left atrial (LA) bilayer models incorporating fibrotic remodeling from late-gadolinium enhancement (LGE) MRI scans. AF was simulated and post-processed using phase mapping to determine electrical driver locations over 15 s. Six different ablation approaches were tested: (i) PVI alone, modeled as wide-area encirclement of the pulmonary veins; PVI together with: (ii) roof and inferior lines to model posterior wall box isolation; (iii) isolating the largest fibrotic area (identified by LGE-MRI); (iv) isolating all fibrotic areas; (v) isolating the largest driver hotspot region [identified as high simulated phase singularity (PS) density]; and (vi) isolating all driver hotspot regions. Ablation efficacy was assessed to predict optimal ablation therapies for individual patients. We subsequently trained a random forest classifier to predict ablation response using (a) imaging metrics alone, (b) imaging and electrical metrics, or (c) imaging, electrical, and ablation lesion metrics. The optimal ablation approach resulting in termination, or if not possible atrial tachycardia (AT), varied among the virtual patient cohort: (i) 20% PVI alone, (ii) 6% box ablation, (iii) 2% largest fibrosis area, (iv) 4% all fibrosis areas, (v) 2% largest driver hotspot, and (vi) 46% all driver hotspots. Around 20% of cases remained in AF for all ablation strategies. The addition of patient-specific and ablation pattern specific lesion metrics to the trained random forest classifier improved predictive capability from an accuracy of 0.73 to 0.83. The trained classifier results demonstrate that the surface areas of pre-ablation driver regions and of fibrotic tissue not isolated by the proposed ablation strategy are both important for predicting ablation outcome. Overall, our study demonstrates the need to select the optimal ablation strategy for each patient. It suggests that both patient-specific fibrosis properties and driver locations are important for planning ablation approaches, and the distribution of lesions is important for predicting an acute response.
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http://dx.doi.org/10.3389/fphys.2020.572874DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7526475PMC
September 2020

Using cardiac ionic cell models to interpret clinical data.

Wiley Interdiscip Rev Syst Biol Med 2020 Oct 7:e1508. Epub 2020 Oct 7.

King's College London, London, UK.

For over 100 years cardiac electrophysiology has been measured in the clinic. The electrical signals that can be measured span from noninvasive ECG and body surface potentials measurements through to detailed invasive measurements of local tissue electrophysiology. These electrophysiological measurements form a crucial component of patient diagnosis and monitoring; however, it remains challenging to quantitatively link changes in clinical electrophysiology measurements to biophysical cellular function. Multi-scale biophysical computational models represent one solution to this problem. These models provide a formal framework for linking cellular function through to emergent whole organ function and routine clinical diagnostic signals. In this review, we describe recent work on the use of computational models to interpret clinical electrophysiology signals. We review the simulation of human cardiac myocyte electrophysiology in the atria and the ventricles and how these models are being used to link organ scale function to patient disease mechanisms and therapy response in patients receiving implanted defibrillators, \cardiac resynchronisation therapy or suffering from atrial fibrillation and ventricular tachycardia. There is a growing use of multi-scale biophysical models to interpret clinical data. This allows cardiologists to link clinical observations with cellular mechanisms to better understand cardiopathophysiology and identify novel treatment strategies. This article is categorized under: Cardiovascular Diseases > Computational Models Cardiovascular Diseases > Biomedical Engineering Cardiovascular Diseases > Molecular and Cellular Physiology.
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http://dx.doi.org/10.1002/wsbm.1508DOI Listing
October 2020

Tracking the motion of intracardiac structures aids the development of future leadless pacing systems.

J Cardiovasc Electrophysiol 2020 09 15;31(9):2431-2439. Epub 2020 Jul 15.

Department of Biomedical Engineering, Cardiac Electromechanics Research Group, School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK.

Background: Leadless pacemakers preclude the need for permanent leads to pace endocardium. However, it is yet to be determined whether a leadless pacemaker of a similar design to those manufactured for the right ventricle (RV) fits within the left ventricle (LV), without interfering with intracardiac structures.

Methods: Cardiac computed tomography scans were obtained from 30 patients indicated for cardiac resynchronisation therapy upgrade. The mitral valve annulus, chordae tendineae, papillary muscles and LV endocardial wall were marked in the end-diastolic frame. Intracardiac structures motions were tracked through the cardiac cycle. Two pacemaker designs similar to commercially manufactured leadless systems (Abbott's Nanostim LCP and Medtronic's Micra TPS) as well as theoretical designs with calculated optimal dimensions were evaluated. Pacemakers were virtually placed across the LV endocardial surface and collisions between them and intracardiac structures were detected throughout the cycle.

Results: Probability maps of LV intracardiac structures collisions on a 16-segment AHA model indicated possible placement for the Nanostim LCP, Micra TPS, and theoretical designs. Thresholding these maps at a 20% chance of collision revealed only about 36% of the endocardial surface remained collision-free with the deployment of Micra TPS design. The same threshold left no collision-free surface in the case of the Nanostim LCP. To reach at least half of the LV endocardium, the volume of Micra TPS, which is the smaller design, needed to be decreased by 41%.

Conclusion: Due to the presence of intracardiac structures, placement of leadless pacemakers with dimensions similar to commercially manufactured RV systems would be limited to apical regions.
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http://dx.doi.org/10.1111/jce.14657DOI Listing
September 2020

His-bundle and left bundle pacing with optimized atrioventricular delay achieve superior electrical synchrony over endocardial and epicardial pacing in left bundle branch block patients.

Heart Rhythm 2020 11 27;17(11):1922-1929. Epub 2020 Jun 27.

School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.

Background: His-bundle pacing (HBP) and left bundle pacing (LBP) are emerging as novel delivery methods for cardiac resynchronization therapy (CRT) in heart failure patients with left bundle branch block (LBBB). HBP and LBP have never been compared to biventricular endocardial (BiV-endo) pacing. Furthermore, there are indications of negative effects of LBP on right ventricular (RV) activation times (ATs), but these effects have not been quantified.

Objective: The purpose of this study was to compare changes in ventricular activation induced by HBP, LBP, left ventricular (LV) septal pacing, BiV-endo, and biventricular epicardial (BiV-epi) pacing using computer simulations.

Methods: We simulated ventricular activation on 24 four-chamber heart meshes inclusive of the His-Purkinje network in the presence of LBBB. We simulated BiV-epi pacing, BiV-endo pacing with left ventricular (LV) lead at the lateral wall, BiV-endo pacing with LV lead at the LV septum, HBP, and LBP.

Results: HBP was superior to BiV-endo and BiV-epi in terms of reduction in LV ATs and interventricular dyssynchrony (P <.05). LBP reduced LV ATs but not interventricular dyssynchrony compared to BiV-epi and BiV-endo pacing. RV latest AT was higher with LBP than with HBP (141.3 ± 10.0 ms vs 111.8 ± 10.4 ms). Optimizing AV delay during LBP reduced RV latest AT (104.7 ± 8.7 ms) and led to comparable response to HBP. In case of complete AV block, BiV-endo septal pacing was equivalent to LBP.

Conclusion: HBP is superior to BiV-epi and BiV-endo. To achieve comparable response to HBP, AV delay optimization during LBP is required in order to reduce RV ATs.
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http://dx.doi.org/10.1016/j.hrthm.2020.06.028DOI Listing
November 2020

A publicly available virtual cohort of four-chamber heart meshes for cardiac electro-mechanics simulations.

PLoS One 2020 26;15(6):e0235145. Epub 2020 Jun 26.

School of Biomedical Engineering and Imaging Sciences, King's College London, London, City of London, United Kingdom.

Computational models of the heart are increasingly being used in the development of devices, patient diagnosis and therapy guidance. While software techniques have been developed for simulating single hearts, there remain significant challenges in simulating cohorts of virtual hearts from multiple patients. To facilitate the development of new simulation and model analysis techniques by groups without direct access to medical data, image analysis techniques and meshing tools, we have created the first publicly available virtual cohort of twenty-four four-chamber hearts. Our cohort was built from heart failure patients, age 67±14 years. We segmented four-chamber heart geometries from end-diastolic (ED) CT images and generated linear tetrahedral meshes with an average edge length of 1.1±0.2mm. Ventricular fibres were added in the ventricles with a rule-based method with an orientation of -60° and 80° at the epicardium and endocardium, respectively. We additionally refined the meshes to an average edge length of 0.39±0.10mm to show that all given meshes can be resampled to achieve an arbitrary desired resolution. We ran simulations for ventricular electrical activation and free mechanical contraction on all 1.1mm-resolution meshes to ensure that our meshes are suitable for electro-mechanical simulations. Simulations for electrical activation resulted in a total activation time of 149±16ms. Free mechanical contractions gave an average left ventricular (LV) and right ventricular (RV) ejection fraction (EF) of 35±1% and 30±2%, respectively, and a LV and RV stroke volume (SV) of 95±28mL and 65±11mL, respectively. By making the cohort publicly available, we hope to facilitate large cohort computational studies and to promote the development of cardiac computational electro-mechanics for clinical applications.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0235145PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7319311PMC
September 2020

Constructing a Human Atrial Fibre Atlas.

Ann Biomed Eng 2021 Jan 26;49(1):233-250. Epub 2020 May 26.

School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.

Atrial anisotropy affects electrical propagation patterns, anchor locations of atrial reentrant drivers, and atrial mechanics. However, patient-specific atrial fibre fields and anisotropy measurements are not currently available, and consequently assigning fibre fields to atrial models is challenging. We aimed to construct an atrial fibre atlas from a high-resolution DTMRI dataset that optimally reproduces electrophysiology simulation predictions corresponding to patient-specific fibre fields, and to develop a methodology for automatically assigning fibres to patient-specific anatomies. We extended an atrial coordinate system to map the pulmonary veins, vena cava and appendages to standardised positions in the coordinate system corresponding to the average location across the anatomies. We then expressed each fibre field in this atrial coordinate system and calculated an average fibre field. To assess the effects of fibre field on patient-specific modelling predictions, we calculated paced activation time maps and electrical driver locations during AF. In total, 756 activation time maps were calculated (7 anatomies with 9 fibre maps and 2 pacing locations, for the endocardial, epicardial and bilayer surface models of the LA and RA). Patient-specific fibre fields had a relatively small effect on average paced activation maps (range of mean local activation time difference for LA fields: 2.67-3.60 ms, and for RA fields: 2.29-3.44 ms), but had a larger effect on maximum LAT differences (range for LA 12.7-16.6%; range for RA 11.9-15.0%). A total of 126 phase singularity density maps were calculated (7 anatomies with 9 fibre maps for the LA and RA bilayer models). The fibre field corresponding to anatomy 1 had the highest median PS density map correlation coefficient for LA bilayer simulations (0.44 compared to the other correlations, ranging from 0.14 to 0.39), while the average fibre field had the highest correlation for the RA bilayer simulations (0.61 compared to the other correlations, ranging from 0.37 to 0.56). For sinus rhythm simulations, average activation time is robust to fibre field direction; however, maximum differences can still be significant. Patient specific fibres are more important for arrhythmia simulations, particularly in the left atrium. We propose using the fibre field corresponding to DTMRI dataset 1 for LA simulations, and the average fibre field for RA simulations as these optimally predicted arrhythmia properties.
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http://dx.doi.org/10.1007/s10439-020-02525-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7773625PMC
January 2021

The fickle heart: uncertainty quantification in cardiac and cardiovascular modelling and simulation.

Philos Trans A Math Phys Eng Sci 2020 06 25;378(2173):20200119. Epub 2020 May 25.

Computer Science, University of Sheffield, Regent Court, 211 Portobello, Sheffield S1 4DP, UK.

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http://dx.doi.org/10.1098/rsta.2020.0119DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7287327PMC
June 2020

Gaussian process manifold interpolation for probabilistic atrial activation maps and uncertain conduction velocity.

Philos Trans A Math Phys Eng Sci 2020 Jun 25;378(2173):20190345. Epub 2020 May 25.

School of Mathematics and Statistics, University of Sheffield, Sheffield, UK.

In patients with atrial fibrillation, local activation time (LAT) maps are routinely used for characterizing patient pathophysiology. The gradient of LAT maps can be used to calculate conduction velocity (CV), which directly relates to material conductivity and may provide an important measure of atrial substrate properties. Including uncertainty in CV calculations would help with interpreting the reliability of these measurements. Here, we build upon a recent insight into reduced-rank Gaussian processes (GPs) to perform probabilistic interpolation of uncertain LAT directly on human atrial manifolds. Our Gaussian process manifold interpolation (GPMI) method accounts for the topology of the atrium, and allows for calculation of statistics for predicted CV. We demonstrate our method on two clinical cases, and perform validation against a simulated ground truth. CV uncertainty depends on data density, wave propagation direction and CV magnitude. GPMI is suitable for probabilistic interpolation of other uncertain quantities on non-Euclidean manifolds. This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'.
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http://dx.doi.org/10.1098/rsta.2019.0345DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7287339PMC
June 2020

Left ventricular endocardial pacing is less arrhythmogenic than conventional epicardial pacing when pacing in proximity to scar.

Heart Rhythm 2020 08 6;17(8):1262-1270. Epub 2020 Apr 6.

King's College London, London, United Kingdom.

Background: Epicardial pacing increases risk of ventricular tachycardia (VT) in patients with ischemic cardiomyopathy (ICM) when pacing in proximity to scar. Endocardial pacing may be less arrhythmogenic as it preserves the physiological sequences of activation and repolarization.

Objective: The purpose of this study was to determine the relative arrhythmogenic risk of endocardial compared to epicardial pacing, and the role of the transmural gradient of action potential duration (APD) and pacing location relative to scar on arrhythmogenic risk during endocardial pacing.

Methods: Computational models of ICM patients (n = 24) were used to simulate left ventricular (LV) epicardial and endocardial pacing 0.2-3.5 cm from a scar. Mechanisms were investigated in idealized models of the ventricular wall and scar. Simulations were run with/without a 20-ms transmural APD gradient in the physiological direction and with the gradient inverted. Dispersion of repolarization was computed as a surrogate of VT risk.

Results: Patient-specific models with a physiological APD gradient predict that endocardial pacing decreases VT risk (34%; P <.05) compared to epicardial pacing when pacing in proximity to scar (0.2 cm). Endocardial pacing location does not significantly affect VT risk, but epicardial pacing at 0.2 cm compared to 3.5 cm from scar increases it (P <.05). Inverting the transmural APD gradient reverses this trend. Idealized models predict that propagation in the direction opposite to APD gradient decreases VT risk.

Conclusion: Endocardial pacing is less arrhythmogenic than epicardial pacing when pacing proximal to scar and is less susceptible to pacing location relative to scar. The physiological repolarization sequence during endocardial pacing mechanistically explains reduced VT risk compared to epicardial pacing.
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http://dx.doi.org/10.1016/j.hrthm.2020.03.021DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7397521PMC
August 2020

Hypokalemia Promotes Arrhythmia by Distinct Mechanisms in Atrial and Ventricular Myocytes.

Circ Res 2020 03 19;126(7):889-906. Epub 2020 Feb 19.

From the Institute for Experimental Medical Research, Oslo University Hospital (K.T., M.F., M.L., D.B.L., O.M., J.S., J.M.A., O.M.S., I.S., W.E.L.), University of Oslo, Norway.

Rationale: Hypokalemia occurs in up to 20% of hospitalized patients and is associated with increased incidence of ventricular and atrial fibrillation. It is unclear whether these differing types of arrhythmia result from direct and perhaps distinct effects of hypokalemia on cardiomyocytes.

Objective: To investigate proarrhythmic mechanisms of hypokalemia in ventricular and atrial myocytes.

Methods And Results: Experiments were performed in isolated rat myocytes exposed to simulated hypokalemia conditions (reduction of extracellular [K] from 5.0 to 2.7 mmol/L) and supported by mathematical modeling studies. Ventricular cells subjected to hypokalemia exhibited Ca overload and increased generation of both spontaneous Ca waves and delayed afterdepolarizations. However, similar Ca-dependent spontaneous activity during hypokalemia was only observed in a minority of atrial cells that were observed to contain t-tubules. This effect was attributed to close functional pairing of the Na-K ATPase and Na-Ca exchanger proteins within these structures, as reduction in Na pump activity locally inhibited Ca extrusion. Ventricular myocytes and tubulated atrial myocytes additionally exhibited early afterdepolarizations during hypokalemia, associated with Ca overload. However, early afterdepolarizations also occurred in untubulated atrial cells, despite Ca quiescence. These phase-3 early afterdepolarizations were rather linked to reactivation of nonequilibrium Na current, as they were rapidly blocked by tetrodotoxin. Na current-driven early afterdepolarizations in untubulated atrial cells were enabled by membrane hyperpolarization during hypokalemia and short action potential configurations. Brief action potentials were in turn maintained by ultra-rapid K current (I); a current which was found to be absent in tubulated atrial myocytes and ventricular myocytes.

Conclusions: Distinct mechanisms underlie hypokalemia-induced arrhythmia in the ventricle and atrium but also vary between atrial myocytes depending on subcellular structure and electrophysiology.
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http://dx.doi.org/10.1161/CIRCRESAHA.119.315641DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7098435PMC
March 2020

Simulating ventricular systolic motion in a four-chamber heart model with spatially varying robin boundary conditions to model the effect of the pericardium.

J Biomech 2020 03 21;101:109645. Epub 2020 Jan 21.

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

The pericardium affects cardiac motion by limiting epicardial displacement normal to the surface. In computational studies, it is important for the model to replicate realistic motion, as this affects the physiological fidelity of the model. Previous computational studies showed that accounting for the effect of the pericardium allows for a more realistic motion simulation. In this study, we describe the mechanism through which the pericardium causes improved cardiac motion. We simulated electrical activation and contraction of the ventricles on a four-chamber heart in the presence and absence of the effect of the pericardium. We simulated the mechanical constraints imposed by the pericardium by applying normal Robin boundary conditions on the ventricular epicardium. We defined a regional scaling of normal springs stiffness based on image-derived motion from CT images. The presence of the pericardium reduced the error between simulated and image-derived end-systolic configurations from 12.8±4.1 mm to 5.7±2.5 mm. First, the pericardium prevents the ventricles from spherising during isovolumic contraction, reducing the outward motion of the free walls normal to the surface and the upwards motion of the apex. Second, by restricting the inward motion of the free and apical walls of the ventricles the pericardium increases atrioventricular plane displacement by four folds during ejection. Our results provide a mechanistic explanation of the importance of the pericardium in physiological simulations of electromechanical cardiac function.
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http://dx.doi.org/10.1016/j.jbiomech.2020.109645DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7677892PMC
March 2020

KBTBD13 and the ever-expanding sarcomeric universe.

J Clin Invest 2020 02;130(2):593-594

Department of Biomedical Engineering, Kings' College London, London, United Kingdom.

KBTBD13 is a protein expressed in striated muscle whose precise function is unknown. Work by de Winter et al. in this issue of the JCI provides evidence that KBTBD13 localizes to the sarcomere and can directly bind actin. A mutation in KBTBD13 that is associated with nemaline myopathy alters the protein's effects on actin, apparently increasing thin-filament stiffness and ultimately depressing contractile force and relaxation rate. We discuss here the implications of this new sarcomeric protein, some alternate explanations for the effects of KBTBD13R408C, and the advantages of using computational models to interpret functional data from muscle.
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http://dx.doi.org/10.1172/JCI132954DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6994145PMC
February 2020

The impact of wall thickness and curvature on wall stress in patient-specific electromechanical models of the left atrium.

Biomech Model Mechanobiol 2020 Jun 4;19(3):1015-1034. Epub 2019 Dec 4.

Department of Biomedical Engineering, King's College London, London, UK.

The left atrium (LA) has a complex anatomy with heterogeneous wall thickness and curvature. The anatomy plays an important role in determining local wall stress; however, the relative contribution of wall thickness and curvature in determining wall stress in the LA is unknown. We have developed electromechanical finite element (FE) models of the LA using patient-specific anatomical FE meshes with rule-based myofiber directions. The models of the LA were passively inflated to 10mmHg followed by simulation of the contraction phase of the atrial cardiac cycle. The FE models predicted maximum LA volumes of 156.5 mL, 99.3 mL and 83.4 mL and ejection fractions of 36.9%, 32.0% and 25.2%. The median wall thickness in the 3 cases was calculated as [Formula: see text] mm, [Formula: see text] mm, and [Formula: see text] mm. The median curvature was determined as [Formula: see text] [Formula: see text], [Formula: see text], and [Formula: see text]. Following passive inflation, the correlation of wall stress with the inverse of wall thickness and curvature was 0.55-0.62 and 0.20-0.25, respectively. At peak contraction, the correlation of wall stress with the inverse of wall thickness and curvature was 0.38-0.44 and 0.16-0.34, respectively. In the LA, the 1st principal Cauchy stress is more dependent on wall thickness than curvature during passive inflation and both correlations decrease during active contraction. This emphasizes the importance of including the heterogeneous wall thickness in electromechanical FE simulations of the LA. Overall, simulation results and sensitivity analyses show that in complex atrial anatomy it is unlikely that a simple anatomical-based law can be used to estimate local wall stress, demonstrating the importance of FE analyses.
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http://dx.doi.org/10.1007/s10237-019-01268-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7203597PMC
June 2020

Standardised Framework for Quantitative Analysis of Fibrillation Dynamics.

Sci Rep 2019 11 13;9(1):16671. Epub 2019 Nov 13.

National Heart and Lung Institute, Hammersmith Campus, Imperial College London, 72 Du Cane Rd, London, W120UQ, UK.

The analysis of complex mechanisms underlying ventricular fibrillation (VF) and atrial fibrillation (AF) requires sophisticated tools for studying spatio-temporal action potential (AP) propagation dynamics. However, fibrillation analysis tools are often custom-made or proprietary, and vary between research groups. With no optimal standardised framework for analysis, results from different studies have led to disparate findings. Given the technical gap, here we present a comprehensive framework and set of principles for quantifying properties of wavefront dynamics in phase-processed data recorded during myocardial fibrillation with potentiometric dyes. Phase transformation of the fibrillatory data is particularly useful for identifying self-perpetuating spiral waves or rotational drivers (RDs) rotating around a phase singularity (PS). RDs have been implicated in sustaining fibrillation, and thus accurate localisation and quantification of RDs is crucial for understanding specific fibrillatory mechanisms. In this work, we assess how variation of analysis parameters and thresholds in the tracking of PSs and quantification of RDs could result in different interpretations of the underlying fibrillation mechanism. These techniques have been described and applied to experimental AF and VF data, and AF simulations, and examples are provided from each of these data sets to demonstrate the range of fibrillatory behaviours and adaptability of these tools. The presented methodologies are available as an open source software and offer an off-the-shelf research toolkit for quantifying and analysing fibrillatory mechanisms.
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http://dx.doi.org/10.1038/s41598-019-52976-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6853901PMC
November 2019

Sex-Dependent QRS Guidelines for Cardiac Resynchronization Therapy Using Computer Model Predictions.

Biophys J 2019 12 28;117(12):2375-2381. Epub 2019 Aug 28.

School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK. Electronic address:

Cardiac resynchronization therapy (CRT) is an important treatment for heart failure. Low female enrollment in clinical trials means that current CRT guidelines may be biased toward males. However, females have higher response rates at lower QRS duration (QRSd) thresholds. Sex differences in the left ventricle (LV) size could provide an explanation for the improved female response at lower QRSd. We aimed to test if sex differences in CRT response at lower QRSd thresholds are explained by differences in LV size and hence predict sex-specific guidelines for CRT. We investigated the effect that LV size sex difference has on QRSd between male and females in 1093 healthy individuals and 50 CRT patients using electrophysiological computer models of the heart. Simulations on the healthy mean shape models show that LV size sex difference can account for 50-100% of the sex difference in baseline QRSd in healthy individuals. In the CRT patient cohort, model simulations predicted female-specific guidelines for CRT, which were 9-13 ms lower than current guidelines. Sex differences in the LV size are able to account for a significant proportion of the sex difference in QRSd and provide a mechanistic explanation for the sex difference in CRT response. Simulations accounting for the smaller LV size in female CRT patients predict 9-13 ms lower QRSd thresholds for female CRT guidelines.
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http://dx.doi.org/10.1016/j.bpj.2019.08.025DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6990372PMC
December 2019

Balance of Active, Passive, and Anatomical Cardiac Properties in Doxorubicin-Induced Heart Failure.

Biophys J 2019 12 29;117(12):2337-2348. Epub 2019 Jul 29.

Department of Biomedical Engineering, St Thomas's Hospital, King's College London, London, United Kingdom. Electronic address:

Late-onset heart failure (HF) is a known side effect of doxorubicin chemotherapy. Typically, patients are diagnosed when already at an irreversible stage of HF, which allows few or no treatment options. Identifying the causes of compromised cardiac function in this patient group may improve early patient diagnosis and support treatment selection. To link doxorubicin-induced changes in cardiac cellular and tissue mechanical properties to overall cardiac function, we apply a multiscale biophysical biomechanics model of the heart to measure the plausibility of changes in model parameters representing the passive, active, or anatomical properties of the left ventricle for reproducing measured patient phenotypes. We create representative models of healthy controls (N = 10) and patients with HF induced by (N = 22) or unrelated to (N = 25) doxorubicin therapy. The model predicts that HF in the absence of doxorubicin is characterized by a 2- to 3-fold stiffness increase, decreased tension (0-20%), and ventricular dilation (of order 10-30%). HF due to doxorubicin was similar but showed stronger bias toward reduced active contraction (10-30%) and less dilation (0-20%). We find that changes in active, passive, and anatomical properties all play a role in doxorubicin-induced cardiotoxicity phenotypes. Differences in parameter changes between patient groups are consistent with doxorubicin cardiotoxicity having a greater dependence on reduced cellular contraction and less anatomical remodeling than HF not caused by doxorubicin.
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http://dx.doi.org/10.1016/j.bpj.2019.07.033DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6990149PMC
December 2019

A comprehensive multi-index cardiac magnetic resonance-guided assessment of atrial fibrillation substrate prior to ablation: Prediction of long-term outcomes.

J Cardiovasc Electrophysiol 2019 10 22;30(10):1894-1903. Epub 2019 Aug 22.

Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.

Introduction: Multiple cardiac magnetic resonance (CMR)-derived indices of atrial fibrillation (AF) substrate have been shown in isolation to predict long-term outcome following catheter ablation. Left atrial (LA) fibrosis, LA volume, LA ejection fraction (EF), left ventricular ejection fraction (LVEF), LA shape (sphericity) and pulmonary vein anatomy have all been shown to correlate with late AF recurrence. This study aimed to validate and assess the relative contribution of multiple indices in a long-term single-center study.

Methods And Results: Eighty-nine patients (53% paroxysmal AF, 73% male) underwent comprehensive CMR study before first-time AF ablation (median follow-up 726 days [IQR: 418-1010 days]). The 3D late gadolinium-enhanced acquisition (1.5T, 1.3 × 1.3 × 2 mm) was quantified for fibrosis; LA volume and sphericity were assessed on manual segmentation at atrial diastole; LAEF and LVEF were quantified on multislice cine imaging. AF recurred in 43 patients (48%) overall (31 at 1 year). In the recurrence group, LA fibrosis was higher (42% vs 29%; hazard ratio [HR]: 1.032; P = .002), left atrial ejection fraction (LAEF) lower (25% vs 34%; HR: 0.063; P = .016) and LVEF lower (57% vs 63%; HR: 0.011; P = .008). LA volume (135 vs 124 mL) and sphericity (0.819 vs 0.822) were similar. Multivariate Cox regression analysis was adjusted for age and sex (Model 1), additionally AF type (Model 2) and combined (Model 3). In Models 1 and 2, LA fibrosis, LAEF, and LVEF were independently associated with outcome, but only LA fibrosis was independent in Model 3 (HR: 1.021; P = .022).

Conclusions: LAEF, LVEF, and LA fibrosis differed significantly in the AF recurrence cohort. However, on combined multivariate analysis only LA fibrosis remained independently associated with outcome.
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http://dx.doi.org/10.1111/jce.14111DOI Listing
October 2019

Generation of a cohort of whole-torso cardiac models for assessing the utility of a novel computed shock vector efficiency metric for ICD optimisation.

Comput Biol Med 2019 09 24;112:103368. Epub 2019 Jul 24.

Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK. Electronic address:

Implanted cardiac defibrillators (ICDs) seek to automatically detect and terminate potentially lethal ventricular arrhythmias by applying strong internal electric shocks across the heart. However, the optimisation of the specific electrode design and configurations represents an intensive area of research in the pursuit of reduced shock strengths and fewer device complications and risks. Computational whole-torso simulations play an important role in this endeavour, although knowing which specific metric should be used to assess configuration efficacy and assessing the impact of different patient anatomies and pathologies, and the corresponding effect this may have on different metrics has not been investigated. We constructed a cohort of CT-derived high-resolution whole torso-cardiac computational models, including variants of cardiomyopathies and patients with differing torso dimensions. Simulations of electric shock application between electrode configurations corresponding to transveneous (TV-ICD) and subcutaneous (S-ICD) ICDs were modelled and conventional metrics such as defibrillation threshold (DFT) and impedance computed. In addition, we computed a novel metric termed the shock vector efficiency (η), which quantifies the fraction of electrical energy dissipated in the heart relative to the rest of the torso. Across the cohort, S-ICD configurations showed higher DFTs and impedances than TV-ICDs, as expected, although little consistent difference was seen between healthy and cardiomyopathy variants. η was consistently <2% for S-ICD configurations, becoming as high as 13% for TV-ICD setups. Simulations also suggested that a total torso height of approximately 20 cm is required for convergence in η. Overall, η was seen to be approximately negatively correlated with both DFT and impedance. However, important scenarios were identified in which certain values of DFT (or impedance) were associated with a range of η values, and vice-versa, highlighting the heterogeneity introduced by the different torsos and pathologies modelled. In conclusion, the shock vector efficiency represents a useful additional metric to be considered alongside DFT and impedance in the optimisation of ICD electrode configurations, particularly in the context of differing torso anatomies and cardiac pathologies, which can induce significant heterogeneity in conventional metrics of ICD efficacy.
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http://dx.doi.org/10.1016/j.compbiomed.2019.103368DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6873640PMC
September 2019

Evaluation of a real-time magnetic resonance imaging-guided electrophysiology system for structural and electrophysiological ventricular tachycardia substrate assessment.

Europace 2019 Sep;21(9):1432-1441

School of Biomedical Engineering and Imaging Sciences, King's College London, 4th Floor, North Wing, St Thomas' Hospital, London, UK.

Aims: Potential advantages of real-time magnetic resonance imaging (MRI)-guided electrophysiology (MR-EP) include contemporaneous three-dimensional substrate assessment at the time of intervention, improved procedural guidance, and ablation lesion assessment. We evaluated a novel real-time MR-EP system to perform endocardial voltage mapping and assessment of delayed conduction in a porcine ischaemia-reperfusion model.

Methods And Results: Sites of low voltage and slow conduction identified using the system were registered and compared to regions of late gadolinium enhancement (LGE) on MRI. The Sorensen-Dice similarity coefficient (DSC) between LGE scar maps and voltage maps was computed on a nodal basis. A total of 445 electrograms were recorded in sinus rhythm (range: 30-186) using the MR-EP system including 138 electrograms from LGE regions. Pacing captured at 103 sites; 47 (45.6%) sites had a stimulus-to-QRS (S-QRS) delay of ≥40 ms. Using conventional (0.5-1.5 mV) bipolar voltage thresholds, the sensitivity and specificity of voltage mapping using the MR-EP system to identify MR-derived LGE was 57% and 96%, respectively. Voltage mapping had a better predictive ability in detecting LGE compared to S-QRS measurements using this system (area under curve: 0.907 vs. 0.840). Using an electrical threshold of 1.5 mV to define abnormal myocardium, the total DSC, scar DSC, and normal myocardium DSC between voltage maps and LGE scar maps was 79.0 ± 6.0%, 35.0 ± 10.1%, and 90.4 ± 8.6%, respectively.

Conclusion: Low-voltage zones and regions of delayed conduction determined using a real-time MR-EP system are moderately associated with LGE areas identified on MRI.
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http://dx.doi.org/10.1093/europace/euz165DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6735875PMC
September 2019

Universal atrial coordinates applied to visualisation, registration and construction of patient specific meshes.

Med Image Anal 2019 07 17;55:65-75. Epub 2019 Apr 17.

Institute of Electrophysiology and Heart Modeling (IHU Liryc), Foundation Bordeaux University, Pessac-Bordeaux, France; IMB Bordeaux Institute of Mathematics, University of Bordeaux, 351 cours de la Libération, Talence 33405, France.

Integrating spatial information about atrial physiology and anatomy in a single patient from multimodal datasets, as well as generalizing these data across patients, requires a common coordinate system. In the atria, this is challenging due to the complexity and variability of the anatomy. We aimed to develop and validate a Universal Atrial Coordinate (UAC) system for the following applications: combination and assessment of multimodal data; comparison of spatial data across patients; 2D visualization; and construction of patient specific geometries to test mechanistic hypotheses. Left and right atrial LGE-MRI data were segmented and meshed. Two coordinates were calculated for each atrium by solving Laplace's equation, with boundary conditions assigned using five landmark points. The coordinate system was used to map spatial information between atrial meshes, including scalar fields measured using different mapping modalities, and atrial anatomic structures and fibre directions from a reference geometry. Average error in point transfer from a source mesh to a destination mesh and back again was less than 0.1 mm for the left atrium and 0.02 mm for the right atrium. Patient specific meshes were constructed using the coordinate system and phase singularity density maps from arrhythmia simulations were visualised in 2D. In conclusion, we have developed a universal atrial coordinate system allowing automatic registration of imaging and electroanatomic mapping data, 2D visualisation, and patient specific model creation.
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http://dx.doi.org/10.1016/j.media.2019.04.004DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6543067PMC
July 2019

Probabilistic Interpolation of Uncertain Local Activation Times on Human Atrial Manifolds.

IEEE Trans Biomed Eng 2020 01 9;67(1):99-109. Epub 2019 Apr 9.

Objective: Local activation time (LAT) mapping of the atria is important for targeted treatment of atrial arrhythmias, but current methods do not interpolate on the atrial manifold and neglect uncertainties associated with LAT observations. In this paper, we describe novel methods to, first, quantify uncertainties in LAT arising from bipolar electrogram analysis and assignment of electrode recordings to the anatomical mesh, second, interpolate uncertain LAT measurements directly on left atrial manifolds to obtain complete probabilistic activation maps, and finally, interpolate LAT jointly across both the manifold and different S1-S2 pacing protocols.

Methods: A modified center of mass approach was used to process bipolar electrograms, yielding a LAT estimate and error distribution from the electrogram morphology. An error distribution for assigning measurements to the anatomical mesh was estimated. Probabilistic LAT maps were produced by interpolating on a left atrial manifold using Gaussian Markov random fields, taking into account observation errors and characterizing LAT predictions by their mean and standard deviation. This approach was extended to interpolate across S1-S2 pacing protocols.

Results: We evaluated our approach using recordings from three patients undergoing atrial ablation. Cross-validation showed consistent and accurate prediction of LAT observations both at different locations on the left atrium and for different S1-S2 intervals.

Significance: Interpolation of scalar and vector fields across anatomical structures from point measurements is a challenging problem in biomedical engineering, compounded by uncertainties in measurements and meshes. New methods and approaches are required, and in this paper, we have demonstrated an effective method for probabilistic interpolation of uncertain LAT.
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http://dx.doi.org/10.1109/TBME.2019.2908486DOI Listing
January 2020

Pacing in proximity to scar during cardiac resynchronization therapy increases local dispersion of repolarization and susceptibility to ventricular arrhythmogenesis.

Heart Rhythm 2019 10 29;16(10):1475-1483. Epub 2019 Mar 29.

King's College London, London, United Kingdom.

Background: Cardiac resynchronization therapy (CRT) increases the risk of ventricular tachycardia (VT) in patients with ischemic cardiomyopathy (ICM) when the left ventricular (LV) epicardial lead is implanted in proximity to scar.

Objective: The purpose of this study was to determine the mechanisms underpinning this risk by investigating the effects of pacing on local electrophysiology (EP) in relation to scar that provides a substrate for VT in ICM patients undergoing CRT.

Methods: Imaging data from ICM patients (n = 24) undergoing CRT were used to create patient-specific LV anatomic computational models including scar morphology. Simulations of LV epicardial pacing at 0.2-4.5 cm from the scar were performed using EP models of chronic infarct and heart failure (HF). Dispersion of repolarization and the vulnerable window were computed as surrogates for VT risk.

Results: Simulations predict that pacing in proximity to scar (0.2 cm) compared to more distant pacing to a scar (4.5 cm) significantly (P <.01) increased dispersion of repolarization in the vicinity of the scar and widened (P <.01) the vulnerable window, increasing the likelihood of unidirectional block. Moreover, slow conduction during HF further increased dispersion (∼194%). Analysis of variance and post hoc tests show significantly (P <.01) reduced repolarization dispersion when pacing ≥3.5 cm from the scar compared to pacing at 0.2 cm.

Conclusion: Increased dispersion of repolarization in the vicinity of the scar and widening of the vulnerable window when pacing in proximity to scar provides a mechanistic explanation for VT induction in ICM-CRT with lead placement proximal to scar. Pacing 3.5 cm or more from scar may avoid increasing VT risk in ICM-CRT patients.
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http://dx.doi.org/10.1016/j.hrthm.2019.03.027DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6774764PMC
October 2019

A short history of the development of mathematical models of cardiac mechanics.

J Mol Cell Cardiol 2019 02 29;127:11-19. Epub 2018 Nov 29.

Departments of Biomedical Engineering and Cellular and Molecular Physiology, Yale University, New Haven, USA.

Cardiac mechanics plays a crucial role in atrial and ventricular function, in the regulation of growth and remodelling, in the progression of disease, and the response to treatment. The spatial scale of the critical mechanisms ranges from nm (molecules) to cm (hearts) with the fastest events occurring in milliseconds (molecular events) and the slowest requiring months (growth and remodelling). Due to its complexity and importance, cardiac mechanics has been studied extensively both experimentally and through mathematical models and simulation. Models of cardiac mechanics evolved from seminal studies in skeletal muscle, and developed into cardiac specific, species specific, human specific and finally patient specific calculations. These models provide a formal framework to link multiple experimental assays recorded over nearly 100 years into a single unified representation of cardiac function. This review first provides a summary of the proteins, physiology and anatomy involved in the generation of cardiac pump function. We then describe the evolution of models of cardiac mechanics starting with the early theoretical frameworks describing the link between sarcomeres and muscle contraction, transitioning through myosin-level models to calcium-driven systems, and ending with whole heart patient-specific models.
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http://dx.doi.org/10.1016/j.yjmcc.2018.11.015DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6525149PMC
February 2019

Determinants of new wavefront locations in cholinergic atrial fibrillation.

Europace 2018 Nov;20(suppl_3):iii3-iii15

LIRYC Electrophysiology and Heart Modeling Institute, Bordeaux Fondation, Avenue du Haut-Lévèque, Pessac, France.

Aims: Atrial fibrillation (AF) wavefront dynamics are complex and difficult to interpret, contributing to uncertainty about the mechanisms that maintain AF. We aimed to investigate the interplay between rotors, wavelets, and focal sources during fibrillation.

Methods And Results: Arrhythmia wavefront dynamics were analysed for four optically mapped canine cholinergic AF preparations. A bilayer computer model was tuned to experimental preparations, and varied to have (i) fibrosis in both layers or the epicardium only, (ii) different spatial acetylcholine distributions, (iii) different intrinsic action potential duration between layers, and (iv) varied interlayer connectivity. Phase singularities (PSs) were identified and tracked over time to identify rotational drivers. New focal wavefronts were identified using phase contours. Phase singularity density and new wavefront locations were calculated during AF. There was a single dominant mechanism for sustaining AF in each of the preparations, either a rotational driver or repetitive new focal wavefronts. High-density PS sites existed preferentially around the pulmonary vein junctions. Three of the four preparations exhibited stable preferential sites of new wavefronts. Computational simulations predict that only a small number of connections are functionally important in sustaining AF, with new wavefront locations determined by the interplay between fibrosis distribution, acetylcholine concentration, and heterogeneity in repolarization within layers.

Conclusion: We were able to identify preferential sites of new wavefront initiation and rotational activity, in order to determine the mechanisms sustaining AF. Electrical measurements should be interpreted differently according to whether they are endocardial or epicardial recordings.
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http://dx.doi.org/10.1093/europace/euy235DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6251188PMC
November 2018

Patient-specific simulations predict efficacy of ablation of interatrial connections for treatment of persistent atrial fibrillation.

Europace 2018 Nov;20(suppl_3):iii55-iii68

School of Biomedical Engineering & Imaging Sciences, King's College London, St. Thomas' Hospital, Westminster Bridge Road, UK.

Aims: Treatments for persistent atrial fibrillation (AF) offer limited efficacy. One potential strategy aims to return the right atrium (RA) to sinus rhythm (SR) by ablating interatrial connections (IAC) to isolate the atria, but there is limited clinical data to evaluate this ablation approach. We aimed to use simulation to evaluate and predict patient-specific suitability for ablation of IAC to treat AF.

Methods And Results: Persistent AF was simulated in 12 patient-specific geometries, incorporating electrophysiological heterogeneity and fibres, with IAC at Bachmann's bundle, the coronary sinus, and fossa ovalis. Simulations were performed to test the effect of left atrial (LA)-to-RA frequency gradient and fibrotic remodelling on IAC ablation efficacy. During AF, we simulated ablation of one, two, or all three IAC, with or without pulmonary vein isolation and determined if this altered or terminated the arrhythmia. For models without structural remodelling, ablating all IAC terminated RA arrhythmia in 83% of cases. Models with the LA-to-RA frequency gradient removed had an increased success rate (100% success). Ablation of IACs is less effective in cases with fibrotic remodelling (interstitial fibrosis 50% success rate; combination remodelling 67%). Mean number of phase singularities in the RA was higher pre-ablation for IAC failure (success 0.6 ± 0.8 vs. failure 3.2 ± 2.5, P < 0.001).

Conclusion: This simulation study predicts that IAC ablation is effective in returning the RA to SR for many cases. Patient-specific modelling approaches have the potential to stratify patients prior to ablation by predicting if drivers are located in the LA or RA. We present a platform for predicting efficacy and informing patient selection for speculative treatments.
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http://dx.doi.org/10.1093/europace/euy232DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6251187PMC
November 2018

A technique for measuring anisotropy in atrial conduction to estimate conduction velocity and atrial fibre direction.

Comput Biol Med 2019 01 1;104:278-290. Epub 2018 Nov 1.

School of Imaging Sciences and Biomedical Engineering, King's College London, London, United Kingdom.

Background: Cardiac conduction properties exhibit large variability, and affect patient-specific arrhythmia mechanisms. However, it is challenging to clinically measure conduction velocity (CV), anisotropy and fibre direction. Our aim is to develop a technique to estimate conduction anisotropy and fibre direction from clinically available electrical recordings.

Methods: We developed and validated automated algorithms for estimating cardiac CV anisotropy, from any distribution of recording locations on the atrial surface. The first algorithm is for elliptical wavefront fitting to a single activation map (method 1), which works well close to the pacing location, but decreases in accuracy further from the pacing location (due to spatial heterogeneity in the conductivity and fibre fields). As such, we developed a second methodology for measuring local conduction anisotropy, using data from two or three activation maps (method 2: ellipse fitting to wavefront propagation velocity vectors from multiple activation maps).

Results: Ellipse fitting to CV vectors from two activation maps (method 2) leads to an improved estimation of longitudinal and transverse CV compared to method 1, but fibre direction estimation is still relatively poor. Using three activation maps with method 2 provides accurate estimation, with approximately 70% of atrial fibres estimated within 20. We applied the technique to clinical activation maps to demonstrate the presence of heterogeneous conduction anisotropy, and then tested the effects of this conduction anisotropy on predicted arrhythmia dynamics using computational simulation.

Conclusions: We have developed novel algorithms for calculating CV and measuring the direction dependency of atrial activation to estimate atrial fibre direction, without the need for specialised pacing protocols, using clinically available electrical recordings.
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http://dx.doi.org/10.1016/j.compbiomed.2018.10.019DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6506689PMC
January 2019

Computational models in cardiology.

Nat Rev Cardiol 2019 02;16(2):100-111

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

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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http://dx.doi.org/10.1038/s41569-018-0104-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6556062PMC
February 2019