Data of atrial arrhythmias in hospitalized COVID-19 and influenza patients

• Published in: Data in brief Vol. 42; p. 108177
• Main Authors: Jehangir, Qasim, Lee, Yi, Latack, Katie, Poisson, Laila, Wang, Dee Dee, Song, Shiyi, Apala, Dinesh R., Patel, Kiritkumar, Halabi, Abdul R., Krishnamoorthy, Geetha, Sule, Anupam A.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Inc 01.06.2022; Elsevier
• Subjects: Atrial arrhythmias; chest; Chest computerized tomography; coronary artery disease; COVID-19; COVID-19 infection; Data; data collection; demographic statistics; Echocardiography; Heart failure; hypomagnesemia; hypoxia; Influenza; kidney diseases; males; mortality; Poisson distribution; polymerase chain reaction; regression analysis; relative risk; Severe acute respiratory syndrome coronavirus 2; Heart failure; COVID-19; Atrial arrhythmias; Echocardiography; Chest computerized tomography; Influenza
• ISSN: 2352-3409; 2352-3409
• DOI: 10.1016/j.dib.2022.108177

Atrial arrhythmias (AA) are common in hospitalized COVID-19 patients with limited data on their association with COVID-19 infection, clinical and imaging outcomes. In the related research article using retrospective research data from one quaternary care and five community hospitals, patients aged 18 years and above with positive SARS-CoV-2 polymerase chain reaction test were included. 6927 patients met the inclusion criteria. The data in this article provides demographics, home medications, in-hospital events and COVID-19 treatments, multivariable generalized linear regression regression models using a log link with a Poisson distribution (multi-parameter regression [MPR]) to determine predictors of new-onset AA and mortality in COVID-19 patients, computerized tomography chest scan findings, echocardiographic findings, and International Classification of Diseases–Tenth Revision codes. The clinical outcomes were compared to a propensity-matched cohort of influenza patients. For influenza, data is reported on baseline demographics, comorbid conditions, and in-hospital events. Generalized linear regression models were built for COVID-19 patients using demographic characteristics, comorbid conditions, and presenting labs which were significantly different between the groups, and hypoxia in the emergency room. Statistical analysis was performed using R programming language (version 4, ggplot2 package). Multivariable generalized linear regression model showed that, relative to normal sinus rhythm, history of AA (adjusted relative risk [RR]: 1.38; 95% CI: 1.11–1.71; p = 0.003) and newly-detected AA (adjusted RR: 2.02 95% CI: 1.68–2.43; p < 0.001) were independently associated with higher in-hospital mortality. Age in increments of 10 years, male sex, White race, prior history of coronary artery disease, congestive heart failure, end-stage renal disease, presenting leukocytosis, hypermagnesemia, and hypomagnesemia were found to be independent predictors of new-onset AA in the MPR model. The dataset reported is related to the research article entitled “Incidence, Mortality, and Imaging Outcomes of Atrial Arrhythmias in COVID-19” [Jehangir et al. Incidence, Mortality, and Imaging Outcomes of Atrial Arrhythmias in COVID-19, American Journal of Cardiology] [1].