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[EN] Optimization of lead placement and interventricular delay settings in patients under cardiac resynchronization therapy is a complex task that might benefit from prior information based on models. Biophysical models can be used to predict the sequence of electrical heart activation in a patient given a set of parameters which should be personalized to the patient. In this paper, we use electroanatomical maps to personalize the endocardial activation of the right ventricle, and the different tissue conductivities in a pig model with left bundle branch block, to reproduce personalized biventricular activations. Following, we tested the personalized heart model by virtually simulating cardiac resynchronization therapy ; This work was supported by the "Plan Estatal de Investigacion Cientifica y Tecnica y de Innovacion 2013-2016" from the Ministerio de Economia, Industria y Competitividad of Spain and Fondo Europeo de Desarrollo Regional (FEDER) DPI2016-75799-R (AEI/FEDER, UE), and by the Direccion General de Politica Cientifica de la Generalitat Valenciana (PROMETEU 2016/088) ; Gómez García, JF.; Trenor Gomis, BA.; Sebastián Aguilar, R. (2020). Prediction of CRT Activation Sequence by Personalization of Biventricular Models from Electroanatomical Maps. Lecture Notes in Computer Science. 12009:342-351. https://doi.org/10.1007/978-3-030-39074-7_36 ; S ; 342 ; 351 ; 12009 ; Auricchio, A., et al.: Characterization of left ventricular activation in patients with heart failure and left bundle-branch block. Circulation 109(9), 1133–9 (2004). https://doi.org/10.1161/01.CIR.0000118502.91105.F6 ; Bristow, M.R., et al.: Comparison of medical therapy, pacing, and defibrillation in heart failure (COMPANION) investigators: cardiac-resynchronization therapy with or without an implantable defibrillator in advanced chronic heart failure. N. Engl. J. Med. 350(21), 2140–50 (2004). https://doi.org/10.1056/NEJMoa032423 ; Carpio, E.F., et al.: Optimization of lead placement in the right ventricle during cardiac resynchronization ...

[EN] During atrial fibrillation, cardiac tissue undergoes different remodeling processes at different scales from the molecular level to the tissue level. One central player that contributes to both electrical and structural remodeling is the myofibroblast. Based on recent experimental evidence on myofibroblasts' ability to contract, we extended a biophysical myofibroblast model with Ca2+ handling components and studied the effect on cellular and tissue electrophysiology. Using genetic algorithms, we fitted the myofibroblast model parameters to the existing in vitro data. In silico experiments showed that Ca2+ currents can explain the experimentally observed variability regarding the myofibroblast resting membrane potential. The presence of an L-type Ca2+ current can trigger automaticity in the myofibroblast with a cycle length of 799.9 ms. Myocyte action potentials were prolonged when coupled to myofibroblasts with Ca2+ handling machinery. Different spatial myofibroblast distribution patterns increased the vulnerable window to induce arrhythmia from 12 ms in non-fibrotic tissue to 22 & PLUSMN; 2.5 ms and altered the reentry dynamics. Our findings suggest that Ca2+ handling can considerably affect myofibroblast electrophysiology and alter the electrical propagation in atrial tissue composed of myocytes coupled with myofibroblasts. These findings can inform experimental validation experiments to further elucidate the role of myofibroblast Ca2+ handling in atrial arrhythmogenesis. ; We gratefully acknowledge financial support by the Deutsche Forschungsgemeinschaft (DFG) through DO637/22-3 and LO2093/1-1 and by the KIT-Publication Fund of the Karlsruhe Institute of Technology. This work was supported by the European High-Performance Computing Joint Undertaking EuroHPC under grant agreement No 955495 (MICROCARD) co-funded by the Horizon 2020 programme of the European Union (EU), the French National Research Agency ANR, the German Federal Ministry of Education and Research, and the Research Council of Norway. ...

[EN] Drug-induced proarrhythmicity is a major concern for regulators and pharmaceutical companies. For novel drug candidates, the standard assessment involves the evaluation of the potassium hERG channels block and the in vivo prolongation of the QT interval. However, this method is known to be too restrictive and to stop the development of potentially valuable therapeutic drugs. The aim of this work is to create an in silico tool for early detection of drug-induced proarrhythmic risk. The system is based on simulations of how different compounds affect the action potential duration (APD) of isolated endocardial, midmyocardial, and epicardial cells as well as the QT prolongation in a virtual tissue. Multiple channel-drug interactions and state-of-the-art human ventricular action potential models (O'Hara, T., et al. PLos Comput. Biol. 2011, 7, e1002061) were used in our simulations. Specifically, 206.766 cellular and 7072 tissue simulations were performed by blocking the slow and the fast components of the delayed rectifier current (I-Ks and I-Kr, respectively) and the L-type calcium current (I-CaL) at different levels. The performance of our system was validated by classifying the proarrhythmic risk of 84 compounds, 40 of which present torsadogenic properties. On the basis of these results, we propose the use of a new index (Tx) for discriminating torsadogenic compounds, defined as the ratio of the drug concentrations producing 10% prolongation of the cellular endocardial, midmyocardial, and epicardial APDs and the QT interval, over the maximum effective free therapeutic plasma concentration (EFTPC). Our results show that the Tx index outperforms standard methods for early identification of torsadogenic compounds. Indeed, for the analyzed compounds, the Tx tests accuracy was in the range of 87-88% compared with a 73% accuracy of the hERG IC50 based test. ; L.R, J.C., J.G., B.T., J.S.: This work was partially supported by the Direccion General de Politica Cientifica de la Generalitat Valenciana (PROMETEU2016/088) ...

[EN] In patients with atrial fibrillation, intracardiac electrogram signal amplitude is known to decrease with increased structural tissue remodeling, referred to as fibrosis. In addition to the isolation of the pulmonary veins, fibrotic sites are considered a suitable target for catheter ablation. However, it remains an open challenge to find fibrotic areas and to differentiate their density and transmurality. This study aims to identify the volume fraction and transmurality of fibrosis in the atrial substrate. Simulated cardiac electrograms, combined with a generalized model of clinical noise, reproduce clinically measured signals. Our hybrid dataset approach combines in silico and clinical electrograms to train a decision tree classifier to characterize the fibrotic atrial substrate. This approach captures different in vivo dynamics of the electrical propagation reflected on healthy electrogram morphology and synergistically combines it with synthetic fibrotic electrograms from in silico experiments. The machine learning algorithm was tested on five patients and compared against clinical voltage maps as a proof of concept, distinguishing non-fibrotic from fibrotic tissue and characterizing the patient's fibrotic tissue in terms of density and transmurality. The proposed approach can be used to overcome a single voltage cut-off value to identify fibrotic tissue and guide ablation targeting fibrotic areas. ; We gratefully acknowledge financial support by the Deutsche Forschungsgemeinschaft (DFG) through DO637/22-3, LO2093/1-1 and LU 2294/1-1, by the European Union's Horizon 2020 programme (grant agreement No.766082, MY-ATRIA project), by the KIT-Publication Fund of the Karlsruhe Institute of Technology and by the Plan Estatal de Investigacion Cientifica y Tecnica y de Innovacion 2017-2020 from the Ministerio de Ciencia e Innovacion y Universidades (PID2019-104356RB-C41/AEI/10.13039/501100011033) ; Sánchez Arciniegas, JP.; Luongo, G.; Nothstein, M.; Unger, LA.; Saiz Rodríguez, FJ.; Trenor Gomis, BA.; Luik, A. ...