The association between asthma and atrial fibrillation: systematic review and meta-analysis

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作者
Beatriz Nogueira-Garcia
Mariana Alves
Fausto J. Pinto
Daniel Caldeira
机构
[1] Hospital Universitário de Santa Maria – CHULN,Serviço de Cardiologia
[2] Hospital Pulido Valente,Serviço de Medicina III
[3] CHLN,Laboratório de Farmacologia Clínica e Terapêutica, Faculdade de Medicina
[4] Universidade de Lisboa,Instituto de Medicina Molecular, Faculdade de Medicina
[5] Universidade de Lisboa,Centro Cardiovascular da Universidade de Lisboa – CCUL (CCUL@RISE), CAML, Faculdade de Medicina
[6] Universidade de Lisboa,Centro de Estudos de Medicina Baseada na Evidência (CEMBE), Faculdade de Medicina
[7] Universidade de Lisboa,undefined
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Respiratory disease and atrial fibrillation (AF) frequent coexist, but the risk of AF among asthma patients is less characterized. Growing evidence suggest that AF shares with asthma a systemic inflammation background and asthma treatments, such as beta agonists, have been associated with increased risk of cardiac arrhythmias. The aim of this systematic review was to assess the risk of AF in patients with asthma in observational studies. We search for longitudinal studies reporting AF outcome in asthma and control patients through MEDLINE, Cochrane Central Register of Controlled Trials and EMBASE. Pooled estimates of odds ratios (ORs) and 95% confidence intervals (CIs) were derived by random effects meta-analysis. Heterogeneity was assessed using the I2 test. The risk of bias of individual studies was evaluated using the ROBINS-E tool. The study protocol was registered at PROSPERO: CRD42020215707. Seven cohort/nested case–control studies with 1 405 508 individuals were included. The mean follow-up time was 9 years, ranging from 1 to 15 years. Asthma was associated with a higher risk of AF (OR 1.15. 95% CI 1.01–1.29). High heterogeneity (I2 = 81%) and overall “serious” risk of bias, lead to a very low confidence in in this result. Asthma was associated with an increased risk of AF. However, the high risk of bias and high heterogeneity reduces the robustness of these results, calling for further high-quality data.
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