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Observational Study
. 2020 Jun 5;20(1):270.
doi: 10.1186/s12872-020-01544-8.

BMI differences among in-hospital management and outcomes in patients with atrial fibrillation: findings from the Care for Cardiovascular Disease project in China

Affiliations
Observational Study

BMI differences among in-hospital management and outcomes in patients with atrial fibrillation: findings from the Care for Cardiovascular Disease project in China

Fuxue Deng et al. BMC Cardiovasc Disord. .

Abstract

Background: Underweight or obese status influences the prognosis of atrial fibrillation (AF). However, the association between stratification of body mass index (BMI) and in-hospital outcomes in patients with AF, remains lacking in China.

Methods: Using data from the Improving Care for Cardiovascular Disease in China-AF project, which was launched in February 2015 and recruited 150 hospitals in China, we compared characteristics, in-hospital treatments and clinical outcomes among the stratifications of BMI for Asians.

Results: A total of 15,867 AF patients with AF were enrolled, including 830 (5.23%) underweight, 4965 (31.29%) with normal weight, 3716 (23.42%) overweight, 5263 (33.17%) obese class I and 1093 (6.89%) obese class II participants. Compared with normal weight patients, underweight, overweight, and obese patients showed increased percentages of CHADS2 scores (3-6) and CHA2DS2-VASc scores (5-9). During hospitalization, overweight or obese patients showed greater use of rhythm control medications, anticoagulant drugs, and intervention therapies than underweight-normal weight patients. In adjusted logistic models, BMI was a strong predictor of in-hospital mortality. Especially, underweight BMI was associated with higher incidence of in-hospital mortality, with an adjusted odds ratio of 2.08 (95% confidence interval, 1.56-4.46; p = 0.04) than overweight and obese BMI.

Conclusions: Asian patients with AF and high BMI received more medical treatments and presented less adverse in-hospital outcomes compared with those with underweight-normal weight. Although low BMI may be associated with other comorbidities and advanced age, underweight BMI retained a negative correlation with all-cause mortality in the patients with AF during hospitalization.

Keywords: Atrial fibrillation; Body mass index; Clinical outcomes; Medical care.

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Conflict of interest statement

As AHA sponsor to this multi-center project, Pfizer bears no responsibility on the goals, execution, nor publication of the project. The authors are solely responsible for the design and conduct of this content, all study analyses, the manuscript performance, and its final contents. The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Medication and intervention therapies in hospitalization (a) and discharge (b) respectively according to BMI stratification. Ab indicates ablation; Pi, Pacemaker implantation; Cv, Cardioversion; β, beta-blocker; CCB, Calcium channel blocker; Aa, Antiarrhythmic agents; Ap, Antiplatelet agents; Wa, Warfarin; As, Aspirin. ** p < 0.01 among groups. † Treatment of calcium channel blocker included Dihydroarsenidine and non-Dihydroarsenidine. †† Treatment of cardioversion included drug and electrical cardioversion
Fig. 2
Fig. 2
In-hospital outcomes among hospitalized patients with AF according to BMI stratification. † In-hospital outcomes in patients with normal weight were considered as references (the value was presented as 1 on Y-axis.). †† The orange curves above the columns presented the corresponding in-hospital outcomes as columns, and they shared the same data and statistical analysis, providing intuitive ratio change among groups. AF indicates atrial fibrillation. ** p < 0.01 among groups
Fig. 3
Fig. 3
Regression analyses of association between BMI and all-cause mortality. With adjustment of age and sex only (a), and adjustment of sex, age, history of hypertension, diabetes mellitus, stroke, HF, renal failure, RHD, COPD, and cancer (b) in which the covariates were not included when lacking of statistical significance. BMI indicates body mass index; COPD, chronic obstructive pulmonary disease; HF, heart failure; OR, odds ratios; RHD, rheumatic heart disease

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References

    1. Kalra S, Kishor K, Batra A, Aggarwal S. Atrial fibillation in diabetes: need for cardiovigilance. J Pak Med Assoc. 2019;69:437–439. - PubMed
    1. Li Y, et al. Risk factors for new-onset atrial fibrillation: a focus on Asian populations. Int J Cardiol. 2018;261:92–98. doi: 10.1016/j.ijcard.2018.02.051. - DOI - PubMed
    1. Chiang CE, et al. 2017 consensus of the Asia Pacific Heart Rhythm Society on stroke prevention in atrial fibrillation. J Arrhythm. 2017;33:345–367. doi: 10.1016/j.joa.2017.05.004. - DOI - PMC - PubMed
    1. Baek YS, et al. Associations of abdominal obesity and new-onset atrial fibrillation in the general population. J Am Heart Assoc. 2017;6:6. doi: 10.1161/JAHA.116.004705. - DOI - PMC - PubMed
    1. Karasoy D, et al. Obesity is a risk factor for atrial fibrillation among fertile young women: a nationwide cohort study. Europace. 2013;15:781–786. doi: 10.1093/europace/eus422. - DOI - PubMed

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