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MultidisciplinarY training network for ATrial fibRillation monItoring, treAtment and progression

Periodic Reporting for period 1 - MY-ATRIA (MultidisciplinarY training network for ATrial fibRillation monItoring, treAtment and progression)

Reporting period: 2017-11-01 to 2019-10-31

Cardiovascular diseases are the leading cause of death accounting for 30% of deaths worldwide, according to the World Health Organization. Among these diseases, the incidence and prevalence of pathologies related to atrial diseases, particularly atrial fibrillation (AF) and interatrial block, are today reaching pandemic proportions. Despite the high incidence of AF, the ability to treat AF is not improving, at least as assessed by age-adjusted mortality rate post-diagnosis. To improve diagnosis and therapy, a multi-disciplinary approach is needed and to develop it, MY-ATRIA put together a multisectorial consortium, to include technological, clinical and industrial knowledge. MY-ATRIA will address the challenging problems related to detecting atrial arrhythmias with novel device technology as well as to better understanding atrial disease development and response to treatment. The consortium includes 6 academic and 2 industrial Beneficiaries and 5 clinical and 4 industrial Partners to provide scientific support, secondments and training. A new figure of modern professional researchers in AF field will be trained with multidisciplinary competencies, able to transfer advances in basic science to market and clinics. From a scientific point of view, the objectives are:
1. Understanding atrial arrhythmia mechanisms using 3D simulations
2. Creating a set of new tools to characterize the progression of AF, and to detect and monitor AF for screening purposes
3. Studying the effects of treatment (pharmacological cardioversion and ablation)
Efforts have included recruitment of all 12 ESRs and establishment of monitoring processes through Supervisory Board (SB) activity. Cohesion has been fostered through regular meetings of the SB and ESRs including three network-wide Training Activities (Milan-IT, Lund-SE, and Jaca-ES) delivering soft skills to ESRs as well as technical competencies in simulation and signal processing. These events have developed collaboration between ESRs and have been used as a mechanism to review their research activity in presence of all ESRs and supervisors. In the view of the positive feedback of this experience, an additional event gathering all ESRs and supervisors has been planned in March 2020 in Valencia (Spain); the event will provide an open space for discussion and further feedback for ESRs’ research activities. The ESR research activity has been reported in deliverable D2.1 D3.1 D4.,1 whilst wider network activity has been reported in 16 deliverables, with public documents available through the project website (www.myatria.polimi.it). This includes a summary of the first year of each project. Academic dissemination is evidenced by 14 conference papers accepted within this reporting period. The main scientific results achieved by the network so far are more clearly presented in the context of the three research areas detailed below.
MY-ATRIA aims to address the challenging problems related to detecting atrial arrhythmias with novel device technology as well as to understanding the influence of atrial geometries, anatomical substrates and remodelling processes in atrial disease development and response to treatment. From a scientific point of view, there have been many advancements in atrial disease patients’ care, however several issues remain to be solved in three main areas: 1) basic knowledge on atrial functioning, 2) screening and monitoring of atrial dysfunction, and 3) patient treatment, being all of them linked and interacting

Area 1, Basic research: Research is related to the improvement of the state of the art on AF mechanisms, by combining computer models to real data coming from endocavitary as well as surface recordings. In this regard, simulations of different atria and torsos are used to obtain large datasets of ECG and BSPM signals to extract features to implement a classifier of different flutter scenarios. In addition, population of models have been used to determine how the interaction of different ionic currents may affect a number of descriptors of the Action Potential (AP) morphology and their impact on AF initiation and maintenance. Efforts are also being conducted on clarifying which atrial abnormalities, as for instance genetic mutations in IKr and IKs channels, facilitate the different types of AF. Finally, new algorithms are being developed to remove ventricular activity from recorded electrograms to facilitate the study of atrial repolarization as well as to identify if different atrial conduction patterns can be detected by endocavitary and surface recordings.

Area 2, Monitoring, progression and risk stratification: The research focuses on AF detection and monitoring of AF progression using specialised everyday sensors and new devices. In this regard, machine learning-based methods for identification of poor-quality segments of home-based ECG recordings have been proposed as these data often are of lower quality, causing large amounts of false alarms. Further developments were conducted on cardiac rhythm classifiers, where a new approach based on analysis of RR Poincarè Images has been proposed and validated to distinguish AF from normal sinus rhythm and atrial bigeminy. For the analysis of the triggers which initiates AF, unsupervised clustering algorithms based on heart rate variability features were used to classify AF trigger types on signals from subcutaneous single lead continuous monitoring device. Finally, algorithms for the analysis of f-wave characteristics in response to changes in autonomic nervous system activity were developed.

Area 3, Treatment: The research seeks to clarify which therapy is more appropriate for the single patient, based on the characteristics of the patient’s atria and electrical activity of the heart, using a combined computational and real data approach. Detailed 3D atrial models were developed, including AF-induced remodeling, atrial heterogeneity and fibrotic tissue, to study the influence of the presence of fibrotic substrate on the vulnerability to both initiate and maintain AF episodes. The autonomic effects were incorporated in already existing, in-silico cellular models and drug effects on the cellular level to evaluate the role of the autonomic activity in the initiation of AF episodes. Finally, the Omnipolar EGM (OP-EGM) method was developed to characterize atrial propagation patterns and its parameters such as conduction velocity and propagation direction during AF.

MY-ATRIA promote a constant dialogue between researchers, clinicians and industry, to increase the understanding of the problem from different viewpoints. This continuous multidisciplinary dialogue will have a great impact on the ESRs career especially in a long term prospective, as they will be able to speak a new language understood by academic, clinical and industrial people. In addition, the values of integ
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