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. 2011 Dec;34(12):1665-70.
doi: 10.1111/j.1540-8159.2011.03194.x. Epub 2011 Sep 13.

Incidental extracardiac findings in cardiac computed tomography performed before radiofrequency ablation of atrial fibrillation

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Incidental extracardiac findings in cardiac computed tomography performed before radiofrequency ablation of atrial fibrillation

Raphaël Pedro Martins et al. Pacing Clin Electrophysiol. 2011 Dec.

Abstract

Background: Radiofrequency ablation has became a validated therapeutic technique for symptomatic drug refractory atrial fibrillation (AF). Cardiac computed tomography (CT) is used to evaluate left atrial (LA) anatomy in order to improve AF ablation. The analysis of noncardiac structures during cardiac CT may identify clinically significant incidental findings (IFs). The objective of this study was to determine the prevalence of IF in patients undergoing AF catheter ablation.

Methods: Between February 2008 and March 2010, all patients planned for a first procedure of AF or LA tachycardia (LAT) ablation underwent a cardiac CT scan and were retrospectively included in this study. Extracardiac IFs were considered to be present if an abnormality was identified without previous clinical suspicion or known disease.

Results: Two hundred and fifty patients (55.2 ± 9.6 years of age, 82.4% men) were enrolled (133 paroxysmal, 43 persistent, 58 permanent AF, and 16 LAT). Fifty-eight patients (23.2%) had a total of 76 IFs. Patients with IF were significantly older (59.5 ± 8.2 vs 53.8 ± 9.7 years old, P < 0.001). No relationship existed between the type of arrhythmia and IF existence. The majority of IFs were pulmonary (50%), with 15.8% of pulmonary emphysema. Two cases of lung cancer and of pulmonary fibrosis, 15 mediastinal adenopathies, and three congenital coronary arteries anomalies were found.

Conclusions: Cardiac CT scan is a useful tool to evaluate LA morphology before AF ablation. However, as a considerable prevalence of IF was found in our study, extracardiac structures should be routinely analyzed to detect unknown conditions, which could require specific management.

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