J Electrocardiol. 2021 Jan 21;65:76-81. doi: 10.1016/j.jelectrocard.2020.12.005. Online ahead of print.
ABSTRACT
BACKGROUND: There is limited data concerning the prevalence of arrhythmias, particularly atrial fibrillation (AF), which may develop as a consequence of direct myocardial injury and the inflammatory state existing in COVID-19.
METHODS: This single-center study included data concerning 658 COVID-19 patients, who were hospitalized in our institute, between April 20th, 2020 and July 30th, 2020. Demographic data, findings of the imaging studies, and laboratory test results were retrieved from the institutional digital database.
RESULTS: New onset AF (NOAF) was identified in 33 patients (5%). Patients who developed AF were older (72.42 ± 6.10 vs 53.78 ± 13.80, p < 0.001) and had higher frequencies of hypertension and heart failure compared to patients without NOAF (p < 0.001, for both). The CHA2DS2-VASc score was higher in patients, who developed NOAF, compared to those who did not during hospitalization for COVID-19 (p < 0.001). Subjects, who developed NOAF during hospitalization, had a higher leukocyte count, neutrophil / lymphocyte ratio (NLR), C-reactive protein, erythrocyte sedimentation rate, and procalcitonin levels compared to those without NOAF (p < 0.001 for all comparisons). Diffuse lung infiltration was also more frequent in COVID-19 patients, who developed NOAF, during hospitalization (p = 0.015). Multivariate logistic regression analysis demonstrated that age, CHA2DS2-VASc score, CRP, erythrocyte sedimentation rate, and presence of diffuse lung infiltration on thorax CT were predictive for NOAF.
CONCLUSION: The prevalence of NOAF in hospitalized COVID-19 patients is higher than the general population. Age, CHA2DS2-VASc score, C-reactive protein, erythrocyte sedimentation rate, and presence of diffuse lung infiltration on thorax CT may be used to identify patients at high risk for development of NOAF. Especially among these parameters, the presence of diffuse lung infiltration on thorax CT it was the most powerful independent predictor of NOAF development.
PMID:33556739 | DOI:10.1016/j.jelectrocard.2020.12.005