An Effective Novel Index for Predicting the Recurrence of Atrial Fibrillation Ablation: P Wave Duration-to-Amplitude Ratio

Doğduş, Mustafa; Turan, Oğuzhan Ekrem; Başkurt, Ahmet Anıl; Yılancıoğlu, Reşit Yiğit; Özgül, Ufuk; İnevi, Umut Dursun; Özcan, Emin Evren

doi:10.5543/tkda.2022.22416

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Consequently, identifying patient groups at a higher risk of recurrent AF can help ensure a higher success rate after CA and avoid unnecessary risks associated with invasive procedures [ 2 , 7 ]. This also helps clinicians develop appropriate treatment and prevention strategies for patients with AF and adjust the rhythm control approach after CA promptly [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Value of Imaging in the Non-Invasive Prediction of Recurrence after Catheter Ablation in Patients with Atrial Fibrillation: An Up-to-Date Review

Jing,

Li,

et al. 2023

Rev. Cardiovasc. Med.

View full text Add to dashboard Cite

Catheter ablation (CA) is the first-line treatment for atrial fibrillation (AF) patients. However, the risk of recurrence associated with CA treatment should not be ignored. Therefore, the preoperative identification of patients at risk of recurrence is essential for identifying patients who will benefit from non-invasive surgery. Echocardiography, computed tomography (CT), and magnetic resonance imaging (MRI) are essential for the preoperative non-invasive prediction of AF recurrence after CA. Compared to laboratory examinations and other examination methods, these modalities can identify structural changes in the heart and assess functional variations. Accordingly, in past studies, morphological features, quantitative parameters, and imaging information of the heart, as assessed by echocardiography, CT, and MRI, have been used to predict AF recurrence after CA noninvasively. This review summarizes and discusses the current research on echocardiography, CT, MRI, and machine learning for predicting AF recurrence following CA. Recommendations for future research are also presented.

show abstract

Section: Introductionmentioning

confidence: 99%