The DOI matches its Crossref record; 333 of the 333 references it cites were found in a registry or an index.
Atrial fibrillation
Open accessAbstract
Atrial fibrillation (AF) is the most common cardiac arrhythmia despite substantial efforts to understand the pathophysiology of the condition and develop improved treatments. Identifying the underlying causative mechanisms of AF in individual patients is difficult and the efficacy of current therapies is suboptimal. Consequently, the incidence of AF is steadily rising and there is a pressing need for novel therapies. Research has revealed that defects in specific molecular pathways underlie AF pathogenesis, resulting in electrical conduction disorders that drive AF. The severity of this so-called electropathology correlates with the stage of AF disease progression and determines the response to AF treatment. Therefore, unravelling the molecular mechanisms underlying electropathology is expected to fuel the development of innovative personalized diagnostic tools and mechanism-based therapies. Moreover, the co-creation of AF studies with patients to implement novel diagnostic tools and therapies is a prerequisite for successful personalized AF management. Currently, various treatment modalities targeting AF-related electropathology, including lifestyle changes, pharmaceutical and nutraceutical therapy, substrate-based ablative therapy, and neuromodulation, are available to maintain sinus rhythm and might offer a novel holistic strategy to treat AF. Atrial fibrillation (AF) is the most common cardiac arrhythmia and is increasing in prevalence as populations age. This Primer provides an overview of AF epidemiology, the mechanisms underlying electropathology in AF, and the diagnostic approaches and treatments, and highlights important directions to improve understanding and management of AF in co-creation with patients.
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Metadata
- Authors
- Brundel, B. J. J. M., Ai, X., Hills, M. T., Kuipers, M. F., Lip, G. Y. H., & de Groot, N. M. S.
- Venue
- Nature Reviews Disease Primers
- Published
- 2022-04-07
- Volume
- 8
- Issue
- 1
- Pages
- 21-21
- ISSN
- 2056-676X (ISSN-L)
- OpenAlex
- W4223534056
- Cited by
- 499
- References
- 333
- FWCI
- 30.55
- Type
- journal article
- Language
- en
- Open access
- hybrid
- Licence
- www.springernature.com/gp/researchers/text-and-data-mining (tdm)
- www.springernature.com/gp/researchers/text-and-data-mining (vor)
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