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Observational Study
. 2019 Dec 23;14(12):e0226730.
doi: 10.1371/journal.pone.0226730. eCollection 2019.

Health-related quality of life in patients with atrial fibrillation: The role of symptoms, comorbidities, and the type of atrial fibrillation

Affiliations
Observational Study

Health-related quality of life in patients with atrial fibrillation: The role of symptoms, comorbidities, and the type of atrial fibrillation

Fabienne Witassek et al. PLoS One. .

Abstract

Aims: This study aimed to analyse health related quality of life (HRQoL) for patients with different atrial fibrillation (AF) types and to identify patient characteristics, symptoms and comorbidities that influence HRQoL.

Methods: We used baseline data from the Swiss Atrial Fibrillation (Swiss-AF) study, a prospective multicentre observational cohort study conducted in 13 clinical centres in Switzerland. Between April 2014 and August 2017, 2415 AF patients were recruited. Patients were included in this analysis if they had baseline HRQoL data as assessed with EQ-5D-based utilities and visual analogue scale (VAS) scores. Patient characteristics and HRQoL were described stratified by AF type. The impact of symptoms, comorbidities and socio-economic factors on HRQoL was analysed using multivariable regression analysis.

Results: Based on 2412 patients with available baseline HRQoL data, the lowest unadjusted mean HRQoL was found in patients with permanent AF regardless of whether measured with utilities (paroxysmal: 0.83, persistent: 0.84, permanent: 0.80, p<0.001) or VAS score (paroxysmal: 73.6, persistent: 72.8, permanent: 69.2, p<0.001). In multivariable analysis of utilities and VAS scores, higher European Heart Rhythm Association (EHRA) score, recurrent falls and several comorbidities showed a strong negative impact on HRQoL while AF type was no longer associated with HRQoL.

Conclusions: Multiple factors turned out to influence HRQoL in AF patients. After controlling for several comorbidities, the EHRA score was one of the strongest predictors independent of AF type. The results may be valuable for better patient assessment and provide a reference point for further QoL and health economic analyses in AF populations.

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Conflict of interest statement

JHB has received research grant support from the Swiss National Science Foundation and from the Swiss Heart Foundation, grant support, lecture and conference fees from Böhringer, Pfizer, Bayer and Daiichi Sankyo. LHB has received grants from the Swiss National Science Foundation, the University of Basel, and the Swiss Heart Foundation, Switzerland; a grant from The Stroke Association, UK; an unrestricted research grant from AstraZeneca; and consultancy and advisory board fees from Amgen, Bayer, Bristol-Myers Squibb, and Claret Medical. The Department of Radiology, University Hospital Basel, holds a general research agreement with Siemens and receives support from Guerbet, Bracco and Bayer, all unrelated to this work. DC has received consulting fees from Servier, Canada. RK holds grants from Biotronik, Biosense Webster, Boston, Medtronic, Abbott. MK has received consultant fees from Bayer, Boehringer-Ingelheim, Pfizer-BMS, Daiichi-Sankyo, Medtronic, Biotronik, Boston Scientific, Biosense Webster, Astra Zeneca, Novartis. NR has received a grant from the Swiss Heart Foundation. CS has received speaker honoraria from Biosense Webster and Medtronic and research grants from Biosense Webster, Daiichi-Sankyo, and Medtronic. MS has received grants unrelated to the submitted work from Amgen, MSD, Novartis, Pfizer, The Medicines Company; received fees unrelated to the submitted work, from Amgen; and received a grant from the Swiss National Science Foundation. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Figures

Fig 1
Fig 1. Frequencies of comorbidities according to AF type.
TIA, transient ischemic attack; DVT, deep vein thrombosis; OSAS, obstructive sleep apnoea syndrome; PAD, peripheral artery disease.
Fig 2
Fig 2. Boxplots of utility and VAS score according to the EHRA class.
Fig 3
Fig 3. Distribution of utility and VAS score in the study population.

References

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