Atrial fibrillation is a substantial health-care challenge and is considered to be a global pandemic, as prevalence rates have increased greatly 1 and atrial fibrillation-related hospitalisations outnumber those of major cardiac conditions such as heart failure and myocardial infarction. 2 Atrial fibrillation confers an increased risk of stroke and mortality; it therefore needs to be detected not only to manage the arrhythmia but also to prevent comorbidities and death. 3 A 10-second, 12-lead electrocardiograph (ECG) in current clinical practice is unlikely to reveal possible atrial fibrillation if not present in this short monitoring time. Silent or undetected atrial fibrillation is common and the few screening methods available are demanding in terms of time and resources. Continuous monitoring by means of loop recorders is often indicated, particularly in case of embolic stroke of undetermined source (ESUS). 4 Novel and user-friendly wearables to identify arrhythmias have emerged with recent digital advances: wearable ECG technology using automated photoplethysmography algorithms have shown feasible and accurate cardiac rhythm detection and can aid in monitoring the dynamic burden of time spent in atrial fibrillation, 5 while mobile atrial fibrillation applications are available for patients and health-care professionals for education and guidance in management. 6
1.Chugh SS Havmoeller R Narayanan K et al.
Worldwide epidemiology of atrial fibrillation: a Global Burden of Disease 2010 Study.
Circulation. 2014; 129: 837-847
2.Gallagher C Hendriks JM Giles L et al.
Increasing trends in hospitalisations due to atrial fibrillation in Australia from 1993 to 2013.
Heart. 2019; (published online April 1.)
DOI:10.1136/heartjnl-2018-314471
3.Kotecha D Breithardt G Camm AJ et al.
Integrating new approaches to atrial fibrillation management: the 6th AFNET/EHRA Consensus Conference.
Europace. 2018; 20: 395-407
4.Sanna T Diener HC Passman RS et al.
Cryptogenic stroke and underlying atrial fibrillation.
N Engl J Med. 2014; 370: 2478-2486
5.Dorr M Nohturfft V Brasier N et al.
The WATCH AF trial: smartwatches for detection of atrial fibrillation.
JACC Clin Electrophysiol. 2019; 5: 199-208
6.Kotecha D Chua WWL Fabritz L et al.
European Society of Cardiology smartphone and tablet applications for patients with atrial fibrillation and their health care providers.
Europace. 2018; 20: 225-233
7.Attia ZI Noseworthy PA Lopez-Jimenez F et al.
An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction.
Lancet. 2019; (published online Aug 1.)
dx.doi.org/10.1016/S0140-67...
8.Kottkamp H
Human atrial fibrillation substrate: towards a specific fibrotic atrial cardiomyopathy.
Eur Heart J. 2013; 34: 2731-2738
9.Martinez-Selles M Masso-van Roessel A Alvarez-Garcia J et al.
Interatrial block and atrial arrhythmias in centenarians: prevalence, associations, and clinical implications.
Heart Rhythm. 2016; 13: 645-651
10.Chua W Purmah Y Cardoso VR et al.
Data-driven discovery and validation of circulating blood-based biomarkers associated with prevalent atrial fibrillation.
Eur Heart J. 2019; 40: 1268-1276
11.Roselli C Chaffin MD Weng LC et al.
Multi-ethnic genome-wide association study for atrial fibrillation.
Nat Genet. 2018; 50: 1225-1233
12.Hannun AY Rajpurkar P Haghpanahi M et al.
Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network.
Nat Med. 2019; 25: 65-69