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, 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 almost finalised a comprehensive analysis of atrial tissue and will in the near future conduct a large analysis of blood biomarkers to identify potential mechanisms of AF. We aim to identify a principal set of markers which stratify AF types and predict AF presence. Prediction models which integrate the major drivers of AF will be robustly developed and externally validated. As such, CATCH ME provides an important foundation to enable the future development of personalised strategies for management of AF in patients, e.g. laying the foundation to identify patients suitable for different AF treatments or prevention strategies.
To achieve these goals, our first aim is to describe the major alterations in atrial tissue found in patients with AF. Secondly, 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 measures such as biomarkers, ECG parameters, echocardiography values, etc. Thirdly, we aim to identify parameters that group patients with similar causes of AF and outcomes. We will take advantage of the databases and blood samples of 8 patient cohorts with or at risk of AF. Outcomes from these atrial tissue, clinical, and blood biomarker analyses will be combined and a prognostic modelling approach will yield clinical prediction models with markers which best predict AF incidence, prevalence, and adverse 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.
