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Faculty of Medical Sciences

Incidence and predictors of new-onset atrial fibrillation after ST-segment elevation myocardial infarction

Velt, M.J.H. (2021) Incidence and predictors of new-onset atrial fibrillation after ST-segment elevation myocardial infarction. thesis, Medicine.

Full text available on request.

Abstract

Background: New-onset atrial fibrillation (NOAF) is a common complication following STsegment elevation myocardial infarction (STEMI) and is associated with worse short- and longterm prognosis. Identifying patients at high risk of developing NOAF is essential in order to perform regularly screening and initiate early intervention. In this study we aimed to investigate the incidence and predictors of NOAF after a first STEMI. Method: A total of 225 STEMI patients treated with percutaneous coronary intervention between July 2015 and January 2017 at the University Medical Center Groningen were retrospectively enrolled. Univariate and multivariate logistic regression analysis were performed to identify predictors of NOAF. Results: Out of the 225 STEMI patients (mean age 62 ± 11 years, 75% men), 24 (10.7%) developed NOAF within 6 months after STEMI onset. Patients with NOAF were older (67 ± 10 vs. 62 ± 12 years, p = 0.002), more likely to have hypertension (65.2% vs. 39.9%, p = 0.026), suffered more from inferior myocardial infarction (94.7% vs. 66.7%, p = 0.014) and less often had the left anterior descending (LAD) artery as culprit vessel (16.7% vs. 41.0%, p = 0.025). Laboratory comparisons showed that glucose level, peak creatine kinase and peak NT-proBNP were higher in the NOAF group (all p < 0.05). In multivariate regression analysis, male gender, hypertension, peak NT-proBNP, hemoglobin and LAD as culprit vessel remained significant predictors of NOAF. Conclusion: NOAF is a frequent complication of STEMI, affecting more than 1 out of 10 patients. Male gender, hypertension, peak NT-proBNP, hemoglobin and LAD as culprit vessel predict NOAF after a first STEMI and may add valuable information to detect high-risk patients.

Item Type: Thesis (UNSPECIFIED)
Supervisor name: Voors, Prof. dr. A.A.
Faculty: Medical Sciences
Date Deposited: 22 Dec 2021 09:25
Last Modified: 22 Dec 2021 09:25
URI: https://umcg.studenttheses.ub.rug.nl/id/eprint/2890

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