Atypical atrial flutter: review of mechanisms, advances in mapping and ablation outcomes

Ko, Nway L. Ko; Sriramoju, Anil; Khetarpal, Banveet Kaur; Srivathsan, Komandoor

doi:10.1097/hco.0000000000000924

Cited by 11 publications

(6 citation statements)

References 38 publications

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“…Atypical AFLs are the minor part and include those arising in the left atrium and are usually associated with cardiomyopathies, cardiac surgery, and incomplete AF ablation. CA of these types are longer and more complex, with a significantly higher rate of recurrence [82]. AFL ablation guided by EAM systems aims to identify the best line of ablation based on local electrograms and activation map of the re-entry circuit, then to assess the bidirectional block.…”

Section: Atrial Fibrillation and Atrial Fluttermentioning

confidence: 99%

The Fluoroless Future in Electrophysiology: A State-of-the-Art Review

Preda,

Bonvicini,

Coradello

et al. 2024

Diagnostics

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Fluoroscopy has always been the cornerstone imaging method of interventional cardiology procedures. However, radiation exposure is linked to an increased risk of malignancies and multiorgan diseases. The medical team is even more exposed to X-rays, and a higher incidence of malignancies was reported in this professional group. In the last years, X-ray exposure has increased rapidly, involving, above all, the medical team and young patients and forcing alternative fluoroless imaging methods. In cardiac electrophysiology (EP) and pacing, the advent of 3D electroanatomic mapping systems with dedicated catheters has allowed real-time, high-density reconstruction of both heart anatomy and electrical activity, significantly reducing the use of fluoroscopy. In addition, the diffusion of intracardiac echocardiography has provided high anatomical resolution of moving cardiac structures, providing intraprocedural guidance for more complex catheter ablation procedures. These methods have largely demonstrated safety and effectiveness, allowing for a dramatic reduction in X-ray delivery in most arrhythmias’ ablations. However, some technical concerns, as well as higher costs, currently do not allow their spread out in EP labs and limit their use to only procedures that are considered highly complex and time-consuming and in young patients. In this review, we aim to update the current employment of fluoroless imaging in different EP procedures, focusing on its strengths and weaknesses.

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Section: Atrial Fibrillation and Atrial Fluttermentioning

confidence: 99%

The Fluoroless Future in Electrophysiology: A State-of-the-Art Review

Preda,

Bonvicini,

Coradello

et al. 2024

Diagnostics

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“…Atrial Fibrillation (AFib) and Atrial Flutter (AFlut) are distinct irregular heart rhythms originating from abnormal activity in the heart's upper chambers, the Atria [1], [2]. These conditions pose significant risks, especially for the elderly [3].…”

Section: Introductionmentioning

confidence: 99%

“…Subtle or absent symptoms often accompany irregular heart rhythms, including chest pain, dizziness, shortness of breath, fainting, and palpitations [3], [5], associated with rapid ventricular rate and inadequate diastolic ventricular filling [2]. Automated detection systems can significantly aid in promptly and accurately identifying these conditions, improving healthcare efficiency and reducing patient wait times.…”

Section: Introductionmentioning

confidence: 99%

“…waves, irregular RR intervals, and fibrillatory waves, while AFlut typically displays sawtooth flutter waves [4]. However, despite these distinct patterns, AFlut is often misdiagnosed as AFib due to similar symptoms and AFib's higher prevalence [2], [3], [5]. Some studies suggest that AFlut may be misinterpreted as AFib, especially when ventricular activity is highly irregular, causing AFlut to mimic AFib on surface ECGs [6].…”

Section: Introductionmentioning

confidence: 99%

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Can Deep Learning Models Differentiate Atrial Fibrillation from Atrial Flutter?

Ribeiro

Soares

Dias

et al. 2023

Preprint

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This study investigates the use of standard 12-lead ECG records from six largest PhysioNet CinC Challenge 2021 databases and a private database to differentiate Atrial Fibrillation from Atrial Flutter. Image-based and one-dimensional-based Deep Learning models were considered to perform the classification using different 1D and 2D Convolutional Neural Network architectures. For 1D CNNs, LiteVGG-11 was the architecture that best performed, with Acc 77.91 +/- 1.73%, AUROC 87.17 +/- 1.29%, F1 76.59 +/- 1.90%, Spe 71.69 +/- 4.73% and Se 86.53 +/- 5.33%. For 2D CNNs, EfficientNet-B2 was the architecture that best performed, with Acc 75.20 +/- 3.38%, AUROC 85.50 +/- 1.14%, F1 71.59 +/- 3.66%, Spe 74.76 +/- 13.85% and Se 75.74 +/- 13.85%. We showed that discrimination of AFib and AFlut is not trivial, and that one-dimensional signals had better performance than two-dimensional ones. Moreover, we demonstrate that the performance of our models only on the PhysioNet CinC Challenge 2021 databases are considerably lower than only on our private dataset.

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“…Atrial Fibrillation (AFib) and Atrial Flutter (AFlut) are heart conditions characterized by irregular heart rhythms at the Atria [Cosío 2017, Ko Ko et al 2022, being particularly dangerous for the elderly population [Shah et al 2018]. Both conditions are associated with an increased risk of stroke and other heart-related complications [Brundel et al 2022].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Approach for Detection of Atrial Fibrillation and Atrial Flutter Based on ECG Images

Ribeiro,

Dias,

Soares

et al. 2023

Anais Do XXIII Simpósio Brasileiro De Computação Aplicada À Saúde (SBCAS 2023)

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This study explores the application of image-based deep learning techniques to distinguish between Atrial Fibrillation (AFib) and Atrial Flutter (AFlut) using images of standard 12-lead ECG exams from a private database. By implementing a MobileNet Convolutional Neural Network architecture, we achieve a high classification performance, with an accuracy of 95.6%, AUROC of 97.6%, F1-score of 83.2%, specificity of 99.6%, and sensitivity of 72.7%. We also applied explainable methods, such as Grad-CAM and LIME, to try to interpret the model’s decision-making process and identify significant regions within the ECG images that contribute to the classification. Our results demonstrate the potential of image-based deep learning approaches for accurate and reliable discrimination between AFib and AFlut, paving the way for enhanced diagnostic capabilities in clinical settings.

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Atypical atrial flutter: review of mechanisms, advances in mapping and ablation outcomes

Cited by 11 publications

References 38 publications

The Fluoroless Future in Electrophysiology: A State-of-the-Art Review

The Fluoroless Future in Electrophysiology: A State-of-the-Art Review

Can Deep Learning Models Differentiate Atrial Fibrillation from Atrial Flutter?

Deep Learning Approach for Detection of Atrial Fibrillation and Atrial Flutter Based on ECG Images

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