10 research outputs found

    2019 HRS/EHRA/APHRS/LAHRS expert consensus statement on catheter ablation of ventricular arrhythmias

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    Ventricular arrhythmias are an important cause of morbidity and mortality and come in a variety of forms, from single premature ventricular complexes to sustained ventricular tachycardia and fibrillation. Rapid developments have taken place over the past decade in our understanding of these arrhythmias and in our ability to diagnose and treat them. The field of catheter ablation has progressed with the development of new methods and tools, and with the publication of large clinical trials. Therefore, global cardiac electrophysiology professional societies undertook to outline recommendations and best practices for these procedures in a document that will update and replace the 2009 EHRA/HRS Expert Consensus on Catheter Ablation of Ventricular Arrhythmias. An expert writing group, after reviewing and discussing the literature, including a systematic review and meta-analysis published in conjunction with this document, and drawing on their own experience, drafted and voted on recommendations and summarized current knowledge and practice in the field. Each recommendation is presented in knowledge byte format and is accompanied by supportive text and references. Further sections provide a practical synopsis of the various techniques and of the specific ventricular arrhythmia sites and substrates encountered in the electrophysiology lab. The purpose of this document is to help electrophysiologists around the world to appropriately select patients for catheter ablation, to perform procedures in a safe and efficacious manner, and to provide follow-up and adjunctive care in order to obtain the best possible outcomes for patients with ventricular arrhythmias

    Arrhythmogenic sarcoplasmic reticulum calcium leak in isolated ventricular cardiomyocytes - changes in heart failure and mechanisms of pharmacological modulation

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    Cardiomyocyte contraction involves sarcolemmal depolarization causing a small influx of Ca2+ which is then amplified via a larger release from the sarcoplasmic reticulum (SR). Under certain conditions SR Ca2+ is released in the absence of depolarization - so called SR Ca2+ leak. This is thought to be a key cause of arrhythmogenesis in heart failure (HF). The aims of this thesis were to assess how SR leak changes in a rat model of HF induced by chronic myocardial infarction (MI) and the mechanism of modulation using INa blockers. Several novel methodologies were developed to do this including the use of hierarchical statistical analysis which reduced the chance of type I errors in comparison to standard techniques. Detailed assessment of the HF model showed that there was fluid retention and eccentric hypertrophic remodelling of an impaired left ventricle by 16 weeks post MI which were more marked compared with earlier timepoints. Although under basal conditions Ca2+ leak was similar in HF and control cells, leak enhancement in response to isoprenaline was more marked in HF cells and there were significant heterogeneities in leak when comparing the borderzone to remote regions. At an earlier stage (8 weeks post MI) we found more frequent Ca2+ waves even under basal conditions. Analysis of Ca2+ leak in 3-D for the first time using a novel microscopy technique showed that arrhythmogenic waves originate from regions of preserved t-tubules. Finally we explored the use of flecainide to inhibit SR leak and showed that it acts via reduction of INa, which enhances Ca2+ efflux via the Na+/Ca2+ exchanger. In conclusion this thesis has drawn on several novel methodologies to gain a deeper understanding of SR leak, both in terms of how it changes in HF and by exploring a novel mechanism by which it can be reduced.Open Acces

    Computational modelling of the human heart and multiscale simulation of its electrophysiological activity aimed at the treatment of cardiac arrhythmias related to ischaemia and Infarction

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    [ES] Las enfermedades cardiovasculares constituyen la principal causa de morbilidad y mortalidad a nivel mundial, causando en torno a 18 millones de muertes cada año. De entre ellas, la más común es la enfermedad isquémica cardíaca, habitualmente denominada como infarto de miocardio (IM). Tras superar un IM, un considerable número de pacientes desarrollan taquicardias ventriculares (TV) potencialmente mortales durante la fase crónica del IM, es decir, semanas, meses o incluso años después la fase aguda inicial. Este tipo concreto de TV normalmente se origina por una reentrada a través de canales de conducción (CC), filamentos de miocardio superviviente que atraviesan la cicatriz del infarto fibrosa y no conductora. Cuando los fármacos anti-arrítmicos resultan incapaces de evitar episodios recurrentes de TV, la ablación por radiofrecuencia (ARF), un procedimiento mínimamente invasivo realizado mediante cateterismo en el laboratorio de electrofisiología (EF), se usa habitualmente para interrumpir de manera permanente la propagación eléctrica a través de los CCs responsables de la TV. Sin embargo, además de ser invasivo, arriesgado y requerir mucho tiempo, en casos de TVs relacionadas con IM crónico, hasta un 50% de los pacientes continúa padeciendo episodios recurrentes de TV tras el procedimiento de ARF. Por tanto, existe la necesidad de desarrollar nuevas estrategias pre-procedimiento para mejorar la planificación de la ARF y, de ese modo, aumentar esta tasa de éxito relativamente baja. En primer lugar, realizamos una revisión exhaustiva de la literatura referente a los modelos cardiacos 3D existentes, con el fin de obtener un profundo conocimiento de sus principales características y los métodos usados en su construcción, con especial atención sobre los modelos orientados a simulación de EF cardíaca. Luego, usando datos clínicos de un paciente con historial de TV relacionada con infarto, diseñamos e implementamos una serie de estrategias y metodologías para (1) generar modelos computacionales 3D específicos de paciente de ventrículos infartados que puedan usarse para realizar simulaciones de EF cardíaca a nivel de órgano, incluyendo la cicatriz del infarto y la región circundante conocida como zona de borde (ZB); (2) construir modelos 3D de torso que permitan la obtención del ECG simulado; y (3) llevar a cabo estudios in-silico de EF personalizados y pre-procedimiento, tratando de replicar los verdaderos estudios de EF realizados en el laboratorio de EF antes de la ablación. La finalidad de estas metodologías es la de localizar los CCs en el modelo ventricular 3D para ayudar a definir los objetivos de ablación óptimos para el procedimiento de ARF. Por último, realizamos el estudio retrospectivo por simulación de un caso, en el que logramos inducir la TV reentrante relacionada con el infarto usando diferentes configuraciones de modelado para la ZB. Validamos nuestros resultados mediante la reproducción, con una precisión razonable, del ECG del paciente en TV, así como en ritmo sinusal a partir de los mapas de activación endocárdica obtenidos invasivamente mediante sistemas de mapeado electroanatómico en este último caso. Esto permitió encontrar la ubicación y analizar las características del CC responsable de la TV clínica. Cabe destacar que dicho estudio in-silico de EF podría haberse efectuado antes del procedimiento de ARF, puesto que nuestro planteamiento está completamente basado en datos clínicos no invasivos adquiridos antes de la intervención real. Estos resultados confirman la viabilidad de la realización de estudios in-silico de EF personalizados y pre-procedimiento de utilidad, así como el potencial del abordaje propuesto para llegar a ser en un futuro una herramienta de apoyo para la planificación de la ARF en casos de TVs reentrantes relacionadas con infarto. No obstante, la metodología propuesta requiere de notables mejoras y validación por medio de es[CA] Les malalties cardiovasculars constitueixen la principal causa de morbiditat i mortalitat a nivell mundial, causant entorn a 18 milions de morts cada any. De elles, la més comuna és la malaltia isquèmica cardíaca, habitualment denominada infart de miocardi (IM). Després de superar un IM, un considerable nombre de pacients desenvolupen taquicàrdies ventriculars (TV) potencialment mortals durant la fase crònica de l'IM, és a dir, setmanes, mesos i fins i tot anys després de la fase aguda inicial. Aquest tipus concret de TV normalment s'origina per una reentrada a través dels canals de conducció (CC), filaments de miocardi supervivent que travessen la cicatriu de l'infart fibrosa i no conductora. Quan els fàrmacs anti-arítmics resulten incapaços d'evitar episodis recurrents de TV, l'ablació per radiofreqüència (ARF), un procediment mínimament invasiu realitzat mitjançant cateterisme en el laboratori de electrofisiologia (EF), s'usa habitualment per a interrompre de manera permanent la propagació elèctrica a través dels CCs responsables de la TV. No obstant això, a més de ser invasiu, arriscat i requerir molt de temps, en casos de TVs relacionades amb IM crònic fins a un 50% dels pacients continua patint episodis recurrents de TV després del procediment d'ARF. Per tant, existeix la necessitat de desenvolupar noves estratègies pre-procediment per a millorar la planificació de l'ARF i, d'aquesta manera, augmentar la taxa d'èxit, que es relativament baixa. En primer lloc, realitzem una revisió exhaustiva de la literatura referent als models cardíacs 3D existents, amb la finalitat d'obtindre un profund coneixement de les seues principals característiques i els mètodes usats en la seua construcció, amb especial atenció sobre els models orientats a simulació de EF cardíaca. Posteriorment, usant dades clíniques d'un pacient amb historial de TV relacionada amb infart, dissenyem i implementem una sèrie d'estratègies i metodologies per a (1) generar models computacionals 3D específics de pacient de ventricles infartats capaços de realitzar simulacions de EF cardíaca a nivell d'òrgan, incloent la cicatriu de l'infart i la regió circumdant coneguda com a zona de vora (ZV); (2) construir models 3D de tors que permeten l'obtenció del ECG simulat; i (3) dur a terme estudis in-silico de EF personalitzats i pre-procediment, tractant de replicar els vertaders estudis de EF realitzats en el laboratori de EF abans de l'ablació. La finalitat d'aquestes metodologies és la de localitzar els CCs en el model ventricular 3D per a ajudar a definir els objectius d'ablació òptims per al procediment d'ARF. Finalment, a manera de prova de concepte, realitzem l'estudi retrospectiu per simulació d'un cas, en el qual aconseguim induir la TV reentrant relacionada amb l'infart usant diferents configuracions de modelatge per a la ZV. Validem els nostres resultats mitjançant la reproducció, amb una precisió raonable, del ECG del pacient en TV, així com en ritme sinusal a partir dels mapes d'activació endocardíac obtinguts invasivament mitjançant sistemes de mapatge electro-anatòmic en aquest últim cas. Això va permetre trobar la ubicació i analitzar les característiques del CC responsable de la TV clínica. Cal destacar que aquest estudi in-silico de EF podria haver-se efectuat abans del procediment d'ARF, ja que el nostre plantejament està completament basat en dades clíniques no invasius adquirits abans de la intervenció real. Aquests resultats confirmen la viabilitat de la realització d'estudis in-silico de EF personalitzats i pre-procediment d'utilitat, així com el potencial de l'abordatge proposat per a arribar a ser en un futur una eina de suport per a la planificació de l'ARF en casos de TVs reentrants relacionades amb infart. No obstant això, la metodologia proposada requereix de notables millores i validació per mitjà d'estudis de simulació amb grans cohorts de pacients.[EN] Cardiovascular diseases represent the main cause of morbidity and mortality worldwide, causing around 18 million deaths every year. Among these diseases, the most common one is the ischaemic heart disease, usually referred to as myocardial infarction (MI). After surviving to a MI, a considerable number of patients develop life-threatening ventricular tachycardias (VT) during the chronic stage of the MI, that is, weeks, months or even years after the initial acute phase. This particular type of VT is typically sustained by reentry through slow conducting channels (CC), which are filaments of surviving myocardium that cross the non-conducting fibrotic infarct scar. When anti-arrhythmic drugs are unable to prevent recurrent VT episodes, radiofrequency ablation (RFA), a minimally invasive procedure performed by catheterization in the electrophysiology (EP) laboratory, is commonly used to interrupt the electrical conduction through the CCs responsible for the VT permanently. However, besides being invasive, risky and time-consuming, in the cases of VTs related to chronic MI, up to 50% of patients continue suffering from recurrent VT episodes after the RFA procedure. Therefore, there exists a need to develop novel pre-procedural strategies to improve RFA planning and, thereby, increase this relatively low success rate. First, we conducted an exhaustive review of the literature associated with the existing 3D cardiac models in order to gain a deep knowledge about their main features and the methods used for their construction, with special focus on those models oriented to simulation of cardiac EP. Later, using a clinical dataset of a chronically infarcted patient with a history of infarct-related VT, we designed and implemented a number of strategies and methodologies to (1) build patient-specific 3D computational models of infarcted ventricles that can be used to perform simulations of cardiac EP at the organ level, including the infarct scar and the surrounding region known as border zone (BZ); (2) construct 3D torso models that enable to compute the simulated ECG; and (3) carry out pre-procedural personalized in-silico EP studies, trying to replicate the actual EP studies conducted in the EP laboratory prior to the ablation. The goal of these methodologies is to allow locating the CCs into the 3D ventricular model in order to help in defining the optimal ablation targets for the RFA procedure. Lastly, as a proof-of-concept, we performed a retrospective simulation case study, in which we were able to induce an infarct-related reentrant VT using different modelling configurations for the BZ. We validated our results by reproducing with a reasonable accuracy the patient's ECG during VT, as well as in sinus rhythm from the endocardial activation maps invasively recorded via electroanatomical mapping systems in this latter case. This allowed us to find the location and analyse the features of the CC responsible for the clinical VT. Importantly, such in-silico EP study might have been conducted prior to the RFA procedure, since our approach is completely based on non-invasive clinical data acquired before the real intervention. These results confirm the feasibility of performing useful pre-procedural personalized in-silico EP studies, as well as the potential of the proposed approach to become a helpful tool for RFA planning in cases of infarct-related reentrant VTs in the future. Nevertheless, the developed methodology requires further improvements and validation by means of simulation studies including large cohorts of patients.During the carrying out of this doctoral thesis, the author Alejandro Daniel López Pérez was financially supported by the Ministerio de Economía, Industria y Competitividad of Spain through the program Ayudas para contratos predoctorales para la formación de doctores, with the grant number BES-2013-064089.López Pérez, AD. (2019). Computational modelling of the human heart and multiscale simulation of its electrophysiological activity aimed at the treatment of cardiac arrhythmias related to ischaemia and Infarction [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/124973TESI

    Personalized Cardiac Computational Models: From Clinical Data to Simulation of Infarct-Related Ventricular Tachycardia

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    In the chronic stage of myocardial infarction, a significant number of patients develop life-threatening ventricular tachycardias (VT) due to the arrhythmogenic nature of the remodeled myocardium. Radiofrequency ablation (RFA) is a common procedure to isolate reentry pathways across the infarct scar that are responsible for VT. Unfortunately, this strategy show relatively low success rates; up to 50% of patients experience recurrent VT after the procedure. In the last decade, intensive research in the field of computational cardiac electrophysiology (EP) has demonstrated the ability of three-dimensional (3D) cardiac computational models to perform in-silico EP studies. However, the personalization and modeling of certain key components remain challenging, particularly in the case of the infarct border zone (BZ). In this study, we used a clinical dataset from a patient with a history of infarct-related VT to build an image-based 3D ventricular model aimed at computational simulation of cardiac EP, including detailed patient-specific cardiac anatomy and infarct scar geometry. We modeled the BZ in eight different ways by combining the presence or absence of electrical remodeling with four different levels of image-based patchy fibrosis (0, 10, 20, and 30%). A 3D torso model was also constructed to compute the ECG. Patient-specific sinus activation patterns were simulated and validated against the patient's ECG. Subsequently, the pacing protocol used to induce reentrant VTs in the EP laboratory was reproduced in-silico. The clinical VT was induced with different versions of the model and from different pacing points, thus identifying the slow conducting channel responsible for such VT. Finally, the real patient's ECG recorded during VT episodes was used to validate our simulation results and to assess different strategies to model the BZ. Our study showed that reduced conduction velocities and heterogeneity in action potential duration in the BZ are the main factors in promoting reentrant activity. Either electrical remodeling or fibrosis in a degree of at least 30% in the BZ were required to initiate VT. Moreover, this proof-of-concept study confirms the feasibility of developing 3D computational models for cardiac EP able to reproduce cardiac activation in sinus rhythm and during VT, using exclusively non-invasive clinical data

    Sympathetic innervation controls cardiac trophism and physiology

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    In this thesis I investigated the characteristics of SN-CM interaction, to elucidate if the control of CM activity by SNs occur through a direct cell to cell interaction. I studied SN-CM interaction at the level of the working myocardium, to assess if SN innervation is able to modulate locally CM structural properties. Moreover, using an in vitro model of SNs and CMs coculture I tested the hypothesis of the direct interaction between SN and CM and, evaluated the functional effects of this interaction, in vivo, at the level of the sinoatrial node, exploting the advantages of a novel optogenetic approach. To reach this aim, I implemented cardiac optogenetics on CM, Purkinje fibers and SNs. Finally, I inquired possible translational applications of cardiac optogenetics for clinically relevant situations. The understanding of the mechanism of SN-CM interaction is of great clinical relevance since cardiac innervation impairment has been associated to a growing amount of pathological situations, such as myocardial infarction, diabetes and different types of cardiomyopathies

    Cardiac Arrhythmias

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    Cardiac arrhythmias are common triggers of emergency admission to cardiology or high-dependency departments. Most cases are easy to diagnose and treat, while others may present a challenge to healthcare professionals. A translational approach to arrhythmias links molecular and cellular scientific research with clinical diagnostics and therapeutic methods, which may include both pharmacological and non-pharmacologic treatments. This book presents a comprehensive overview of specific cardiac arrhythmias and discusses translational approaches to their diagnosis and treatment

    A Computational Based Approach for Non-invasive Localization of Atrial ectopic foci

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    Las arritmias auriculares son las arritmias cardı́acas más comunes, afectan a seis millones de personas en Europa e imponen una enorme carga sanitaria en la sociedad. Las nuevas tecnologı́as médicas están ayudando a los electrofisiólogos a adaptar el tratamiento a cada paciente de diferentes maneras. Por ejemplo, la resonancia magnética (MRI) permite evaluar la distribución espacial de la fibrosis auricular; los mapas electroanatómicos (EAM) permiten obtener una caracterización eléctrica de los tejidos en tiempo real; Las imágenes electrocardiográficas (ECGI) permiten estudiar la actividad eléctrica cardı́aca de forma no invasiva; y la ablación por radiofrecuencia (RFA), permite eliminar el tejido patológico en el corazón que desencadena o mantiene una arritmia. A pesar del acceso a tecnologı́as avanzadas y de la existencia de guı́as clı́nicas bien desarrolladas para el tratamiento de las arritmias auriculares, las tasas de éxito del tratamiento a largo plazo siguen siendo bajas, debido a la complejidad de la enfermedad. Por lo tanto, existe una necesidad imperiosa de mejorar los resultados clı́nicos en beneficio de los pacientes y el sistema de salud. Se podrı́an emplear modelos biofı́sicos detallados de las aurı́culas y el torso para integrar todos los datos del paciente en un solo modelo 3D de referencia capaz de reproducir los complejos patrones de activación eléctrica observados en experimentos y la clı́nica. Sin embargo, existen algunas limitaciones relacionadas con la dificultad de construir tales modelos para cada paciente o realizar un número considerable de simulaciones para planificar la terapia óptima de RFA. Teniendo en cuenta todas esas limitaciones, proponemos utilizar modelos biofı́sicos detallados y simulaciones como una herramienta para entrenar sistemas de aprendizaje automático, para lo cual dispondrı́amos de todos los datos y variables del problema, que serı́an imposibles de obtener en un entorno clı́nico real. Por lo tanto, podemos realizar cientos de simulaciones electrofisiológicas, considerando una variedad de escenarios y patologı́as comunes, y entrenar un sistema que deberı́a ser capaz de reconocerlos a partir de un conjunto limitado de datos no invasivos del paciente, como un electrocardiograma (ECG), o mapa de potencial de superficie corporal (BSPM).Abstract Atrial arrhythmias are the most common cardiac arrhythmia, affecting six million people in Europe and imposing a huge healthcare bur- den on society. New technologies are helping electrophysiologists to tailor the treatment to each patient in different ways. For instance, magnetic resonance imaging (MRI) allows to assess the spatial distribution of atrial fibrosis; electro-anatomical maps (EAM) permit to obtain an electrical char- acterization of tissue in real-time; electrocardiographic imaging (ECGI) al- lows to study cardiac electrical activity non-invasively; and radiofrequency ablation (RFA), allows to eliminate pathological tissue in the heart that is triggering or sustaining an arrhythmia. Despite the access to advanced technologies and well-developed clinical guidelines for the management of atrial arrhythmia, long-term treatment success rates remain low, due to the complexity of the disease. Therefore, there is a compelling need to improve clinical outcomes for the benefit of patients and the healthcare system. Detailed biophysical models of the atria and torso could be employed to integrate all the patient data into a single reference 3D model able to re- produce the complex electrical activation patterns observed in experiments and clinics. However, there are some limitations related to the difficulty of building such models for each patient, or performing a substantial number of simulations to plan the optimal RFA therapy. Considering all those lim- itations, we propose to use detailed biophysical models and simulations as a tool to train machine learning systems, for which we have all the ground- truth data which would be impossible to obtain in a real clinical setting. Therefore, we can perform hundreds of electrophysiology simulations, con- sidering a variety of common scenarios and pathologies, and train a system that should be able to recognize them from a limited set of non-invasive pa- tient data, such as an electrocardiogram (ECG), or a body surface potential map (BSPM)
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