Right ventricular vs left bundle branch pacing-induced changes in ECG depolarization and repolarization

Patients suffering from bradycardia are indicated for pacemaker implantation. Right ventricular pacing (RVP)has been conventionally used for this purpose, but it can increase the risk of atrial fibrillation and heart failure. Left bundle branch area pacing (LBBAP) has been proposed as a new physiological pacing technique. The aim of this study was to compare changes induced by RVP and LBBAP in the ECG. 10-minute 12-lead ECG recordings were acquired at baseline and after pacemaker implantation from 83 patients (31 RVP, 52 LBBAP). Median beats were calculated for each patient at baseline and post-implantation states. ECG markers including QRS duration (dQRS)and area (aQRS) and heart rate-corrected QT (QTc) and Tpeak-to-Tend (Tpec) intervals were measured. dQRS and aQRS decreased significantly at post-implantation with respect to baseline, both being significantly lower for LBBAP than RVP after pacemaker implantation. QTc was significantly reduced at post-implantation for both pacing techniques with no differences between them. Tpec did not change either between states or techniques. In conclusion, LBBAP led to more synchronized ventricular depolarization, supporting potentially improved clinical outcomes with LBBAP as compared to RVP for anti-bradycardia therap

Inference of ventricular activation properties from non-invasive electrocardiography

The realisation of precision cardiology requires novel techniques for the non-invasive characterisation of individual patients' cardiac function to inform therapeutic and diagnostic decision-making. The electrocardiogram (ECG) is the most widely used clinical tool for cardiac diagnosis. Its interpretation is, however, confounded by functional and anatomical variability in heart and torso. In this study, we develop new computational techniques to estimate key ventricular activation properties for individual subjects by exploiting the synergy between non-invasive electrocardiography and image-based torso-biventricular modelling and simulation. More precisely, we present an efficient sequential Monte Carlo approximate Bayesian computation-based inference method, integrated with Eikonal simulations and torso-biventricular models constructed based on clinical cardiac magnetic resonance (CMR) imaging. The method also includes a novel strategy to treat combined continuous (conduction speeds) and discrete (earliest activation sites) parameter spaces, and an efficient dynamic time warping-based ECG comparison algorithm. We demonstrate results from our inference method on a cohort of twenty virtual subjects with cardiac volumes ranging from 74 cm3 to 171 cm3 and considering low versus high resolution for the endocardial discretisation (which determines possible locations of the earliest activation sites). Results show that our method can successfully infer the ventricular activation properties from non-invasive data, with higher accuracy for earliest activation sites, endocardial speed, and sheet (transmural) speed in sinus rhythm, rather than the fibre or sheet-normal speeds.Comment: Submitted to Medical Image Analysi

A content analysis of thinspiration, fitspiration, and bonespiration imagery on social media

Background: On social media, images such as thinspiration, fitspiration, and bonespiration, are shared to inspire certain body ideals. Previous research has demonstrated that exposure to these groups of content is associated with increased body dissatisfaction and decreased self-esteem. It is therefore important that the bodies featured within these groups of content are more fully understood so that effective interventions and preventative measures can be informed, developed, and implemented. Method: A content analysis was conducted on a sample of body-focussed images with the hashtags thinspiration, fitspiration, and bonespiration from three social media platforms. Results: The analyses showed that thinspiration and bonespiration content contained more thin and objectified bodies, compared to fitspiration which featured a greater prevalence of muscles and muscular bodies. In addition, bonespiration content contained more bone protrusions and fewer muscles than thinspiration content. Conclusions: The findings suggest fitspiration may be a less unhealthy type of content; however, a subgroup of imagery was identified which idealised the extremely thin body type and as such this content should also be approached with caution. Future research should utilise qualitative methods to further develop understandings of the body ideals that are constructed within these groups of content and the motivations behind posting this content

Human-based approaches to pharmacology and cardiology: an interdisciplinary and intersectorial workshop.

Both biomedical research and clinical practice rely on complex datasets for the physiological and genetic characterization of human hearts in health and disease. Given the complexity and variety of approaches and recordings, there is now growing recognition of the need to embed computational methods in cardiovascular medicine and science for analysis, integration and prediction. This paper describes a Workshop on Computational Cardiovascular Science that created an international, interdisciplinary and inter-sectorial forum to define the next steps for a human-based approach to disease supported by computational methodologies. The main ideas highlighted were (i) a shift towards human-based methodologies, spurred by advances in new in silico, in vivo, in vitro, and ex vivo techniques and the increasing acknowledgement of the limitations of animal models. (ii) Computational approaches complement, expand, bridge, and integrate in vitro, in vivo, and ex vivo experimental and clinical data and methods, and as such they are an integral part of human-based methodologies in pharmacology and medicine. (iii) The effective implementation of multi- and interdisciplinary approaches, teams, and training combining and integrating computational methods with experimental and clinical approaches across academia, industry, and healthcare settings is a priority. (iv) The human-based cross-disciplinary approach requires experts in specific methodologies and domains, who also have the capacity to communicate and collaborate across disciplines and cross-sector environments. (v) This new translational domain for human-based cardiology and pharmacology requires new partnerships supported financially and institutionally across sectors. Institutional, organizational, and social barriers must be identified, understood and overcome in each specific setting

Detection of body position changes from the ECG using a Laplacian noise model

Body position changes (BPCs) are manifested as shifts in the electrical axis of the heart, which may lead to ST changes in the ECG, misclassified as ischemic events. This paper presents a novel BPC detector based on a Laplacian noise model. It is assumed that a BPC can be modelled as a step-like change in the two coefficient series that result from the Karhunen-Loeve transform of the QRS complex and the ST-T segment. The generalized likelihood ratio test is explored for detection, where the statistical parameters of the Laplacian model are subject to estimation. Two databases are studied: one for assessing detection performance in healthy subjects who perform BPCs, and another for assessing the false alarm rate in ECGs recorded during percutaneous transluminal coronary angiography. The resulting probability of detection (P-D) and probability of false alarm (P-F) are 0.94 and 0.00, respectively, whereas the false alarm rate in ischemic recordings is 1 event/h. The proposed detector outperforms an existing detector based on the Gaussian noise model which achieved a P-D/P-F of 0.90/0.01 and a false alarm rate of 2 events/h. Analysis of the log-likelihood function for the Gaussian and Laplacian noise models show that latter model is more adequate. (C) 2014 Published by Elsevier Ltd

Machine learning in the electrocardiogram

The electrocardiogram is the most widely used diagnostic tool that records the electrical activity of the heart and, therefore, its use for identifying markers for early diagnosis and detection is of paramount importance. In the last years, the huge increase of electronic health records containing a systematised collection of different type of digitalised medical data, together with new tools to analyse this large amount of data in an efficient way have re-emerged the field of machine learning in healthcare innovation. This review describes the most recent machine learning-based systems applied to the electrocardiogram as well as pros and cons in the use of these techniques. Machine learning, including deep learning, have shown to be powerful tools for aiding clinicians in patient screening and risk stratification tasks. However, they do not provide the physiological basis of classification outcomes. Computational modelling and simulation can help in the interpretation and understanding of key physiologically meaningful ECG biomarkers extracted from machine learning techniques. (C) 2019 Elsevier Inc. All rights reserved

Repository for modelling acute myocardial ischemia: simulation scripts and torso-heart mesh

Data published for reproducibility purposes of our study on modelling acute myocardial ischemia in humans. High arrhythmic risk in antero-septal acute myocardial ischemia is explained by increased transmural reentry occurrence. (Hector Martinez-Navarro, Ana Minchole, Alfonso Bueno-Orovio, Blanca Rodriguez

Repository for modelling acute myocardial ischemia: simulation scripts and torso-heart mesh

Data published for reproducibility purposes of our study on modelling acute myocardial ischemia in humans. High arrhythmic risk in antero-septal acute myocardial ischemia is explained by increased transmural reentry occurrence. (Hector Martinez-Navarro, Ana Minchole, Alfonso Bueno-Orovio, Blanca Rodriguez