Predicting future atrial fibrillation: risk factors, proteomics and beyond

Abstract

The term ‘epidemic’ is increasingly used to describe the rising global prevalence of atrial fibrillation (AF). Recent estimates suggest that AF accounts for between 0.9% and 1.6% of total healthcare expenditure in the UK, forecast to rise to 4% over the next two decades.1 This trend—which is also anticipated internationally—underpins efforts to identify individuals at high risk of future AF, in addition to those with AF without manifest symptoms, in the hope of targeted prevention and early treatment. Indeed, numerous studies are currently investigating the impact of such approaches on clinical outcomes and healthcare utilisation. The association between AF and various conditions—including hypertension, heart failure, sleep apnoea and chronic kidney disease—is well-described, highlighting that AF is often a multisystem disorder. Accordingly, the management of AF has shifted towards a holistic and integrated approach, targeting comorbidities and risk factors, itself associated with improved outcomes.2 Before the actual onset of AF, some focus has been directed toward the identification of patients at high risk of incident AF. Various clinical risk scores have been proposed, such as the simple C2HEST score (ie, C2: Coronary artery disease/Chronic obstructive pulmonary disease (1 point each); H: Hypertension (1 point); E: Elderly (age ≥ 75 years, 2 points); S: Systolic heart failure (2 points); and T: Thyroid disease (hyperthyroidism, 1 point)).3 More complicated clinical risk scores have also been described for incident AF prediction, including the CHARGE-AF, Framingham and HARMS2-AF scores, as well as the CHADS2 and CHA2DS2-VASc scores (although the latter two were designed for stroke risk stratification, not for prediction of incident AF).4 Unsurprisingly, more complicated clinical risk scores will improve …