Community Research and Development Information Service - CORDIS

H2020

CATCH ME Report Summary

Project ID: 633196
Funded under: H2020-EU.3.1.1.

Periodic Reporting for period 1 - CATCH ME (Characterizing Atrial fibrillation by Translating its Causes into Health Modifiers in the Elderly)

Reporting period: 2015-05-01 to 2016-10-31

Summary of the context and overall objectives of the project

Atrial fibrillation (AF), the most common cardiac arrhythmia, is a major threat to healthy ageing mediated by stroke, dementia, unplanned hospitalisations, heart failure, and premature death. The scientific community has identified important insights into the mechanisms that can cause AF, but the current management of AF patients and prevention of AF are not informed by these mechanisms. As a result, we can only partially prevent AF-related morbidity and mortality, and lack effective ways to prevent AF.

The CATCH ME Consortium will bridge the present disconnect between our understanding of the mechanisms of AF and the current unstructured approach to its prevention and treatment. By combining clinical, molecular, ECG, engineering, bioinformatics, and statistical expertise, we will identify and integrate the main drivers of prevalent and incident AF in patients, and translate them into clinically useful markers. We have already started analysis of atrial tissue and blood biosamples to identify mechanism-based clinical markers for different types of AF. A principal set of markers which stratify AF types and predict AF presence and a statistical prediction model which integrates the major drivers of AF will be developed. The model will be validated in external cohorts to assess the power of the major clinical factors to identify types of AF with different outcomes and progression rates. These clinical markers for different types of AF will then be made available as tools to underpin the development of personalised strategies for management of AF in patients.

To achieve these goals, our first aim is to describe the major alterations in atrial tissue found in patients with AF and in control patients in sinus rhythm. In parallel, we use existing patient datasets to identify drivers of AF based on alterations detectable by clinical parameters, ECG, or from peripheral blood. These alterations are associated with a wide range of biomarkers, including ECG information, telomere length, and microRNAs. Thirdly, we aim to identify parameters that stratify AF patients with similar causes and outcomes. We will take advantage of the databases of 20 patient cohorts with or at risk of AF. Outcomes from the atrial tissue, clinical, and blood biomarker analyses will be appended and a prognostic modelling approach will yield clinical prediction models with markers which best predict AF incidence, prevalence, and outcomes. The model will be externally validated with a view to incident and recurrent AF as well as to AF-related complications.

Finally, we aim to transform the prevention and treatment of AF by providing IT tools based on evidence-based AF management at the beginning of the project, and by providing tools to discern patients with different types of AF to health care professionals and the general public in later stages of the project. To achieve this, we will develop two mobile applications targeted at healthcare professionals and patients, develop a training programme to implement our project outcomes in routine clinical care, and carry out a health economic review, alongside presenting results at academic conferences, publishing papers in high impact journals and continually communicating about the project via social media and our website.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

We have been working for 18 months since May 2015, and many of the major outcomes are currently work in progress. So far, we have collected more than 250 atrial tissue samples that currently undergo standardised mRNA sequencing and histological assessment. Clinical information is available from all patients. In addition, we have performed targeted studies on the relation between some aspects of the atrial tissue properties and clinical characteristics of the patients or biomarkers (e.g. PITX2-dependent gene regulation, fatty infiltration, autonomic tone). Diverse experiments were conducted to establish if ECG based complexity of AF predicted the outcome after AF ablation. Furthermore, we studied the effects of fibro-fatty infiltration of the atria and obesity as well as the effect of different levels of physical exercise on AF. Telomere length was analysed in relation to ECG based PR interval and incident AF. MicroRNAs were also studied in various settings. Significant differences in microRNA expressions were noted for specific combinations of individuals with and without AF, and in the presence or absence of cardiomyopathies. We have defined a core dataset and ancillary items in all 20 databases, and are trialling novel tools to semi-automatically collect, standardise, and merge databases from the different cohorts as a critical step to further prognostic modelling and external validation analyses.

We have contributed to academic as well as non-academic events, engaged via social media, press releases, and websites. Details can be found on our web site www.catch-me.info. Finally, a mobile platform which contains two applications for health care professionals and patients has been developed to improve clinical management and quality of care. A first version of these tools has been integrated in the ESC AF Clinical Guidelines App and promoted during the ESC Congress 2016 in Rome which welcomed more than 33,000 health care professionals (https://www.escardio.org/Research/Research-Funding/catch-me-tools-in-the-esc-pocket-guidelines-app).

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

The novel IT tools that have been published, and the upcoming integrated patient and health care professional apps, will support integrated care of AF patients. In August 2016, CATCH ME tools for aiding clinical decision-making were released at the ESC Annual Congress with the ESC Pocket Guidelines Application for management of AF based on the 2016 ESC AF guidelines.

The project is progressing on track to provide one of the largest atrial tissue collections analysed so far. Processing of the tissue is standardised and includes newly-developed histological high throughput assessments and paired end mRNA sequencing, allowing for comprehensive analysis of pathophysiological alterations in atrial tissue of AF patients. Fifteen papers were already published in high impact journals.

In parallel, we are developing a novel system that efficiently merges and integrates clinical databases from very different data sources. Our combined database will provide in-depth information on one of the largest, most varied, and comprehensive AF population, allowing us analysis of health modifiers, AF prevalence, and prognostic analysis at a scale and depth which was not possible before.

Come January 2017, the Joint AFNET/EHRA Consensus Conference supported by CATCH ME will bring together the world’s leading AF specialists for open discussion on the best way forward to improve prevention, diagnosis and treatment of AF (http://www.kompetenznetz-vorhofflimmern.de/en/6th-joint-afnetehra-consensus-conference-17-19-january-2017). Education will be also the gateway as the findings from the project will be translated into a training programme which will target the vast European Cardiovascular Community of approximately 80,000 individuals.

Related information

Record Number: 194968 / Last updated on: 2017-02-17