Efficacy and safety of catheter ablation for atrial fibrillation in congenital heart disease – A systematic review and meta-analysis

Pranata, Raymond; Tondas, Alexander Edo; Yonas, Emir; Chintya, Veresa; Yamin, Muhammad

doi:10.1016/j.ipej.2019.09.005

Cited by 7 publications

(7 citation statements)

References 24 publications

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“…Clinical data support that transseptal access through the device is safe and efficacious, 54 , 60 , 61 , 62 , 63 , 64 with success rates comparable with those described in patients in whom access was gained through the native septum. 54 , 59 , 65 However, to avoid potential septal spit or device dislodgement in the process of penetrating the device, 63 an upsized dilator may be used to dilate the puncture site and enable the sheath to go through the device.…”

Section: Achd Forms Treated With Af Ablationmentioning

confidence: 57%

“… 38 , 40 , 42 , 43 , 44 , 51 , 52 , 54 , 59 , 63 , 92 , 93 , 94 , 95 , 96 , 97 Notably, the procedure seems to be more effective in patients with ASD. 64 However, the AF recurrence rate is high, reaching up to 56.1% in previous studies ( Table ); therefore, repeated ablation attempts may be needed to manage AF. Anatomic complexity, persistent AF pattern, and left atrial size have been proposed as independent predictors of arrhythmia recurrence after AF catheter ablation.…”

Section: Achd Forms Treated With Af Ablationmentioning

confidence: 96%

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Atrial Fibrillation Ablation in Congenital Heart Disease: Therapeutic Challenges and Future Perspectives

Baroutidou,

Otountzidis,

Papazoglou

et al. 2024

JAHA

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The increasing prevalence of atrial fibrillation (AF) in adults with congenital heart disease raises significant questions regarding its management. The unique underlying anatomic and physiological background further adds to the difficulty in eliminating the AF burden in these patients. Herein, we provide an overview of the current knowledge on the pathophysiology and risk factors for AF in adult congenital heart disease, with a special focus on the existing challenges in AF ablation. Emerging imaging modalities and ablation techniques might have a role to play. Evidence regarding the safety and efficacy of AF ablation in adult congenital heart disease is summarized, especially for patients with an atrial septal defect, Ebstein anomaly of the tricuspid valve, tetralogy of Fallot, and Fontan circulation. Finally, any remaining gaps in knowledge and potential areas of future research are highlighted.

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Section: Achd Forms Treated With Af Ablationmentioning

confidence: 57%

Section: Achd Forms Treated With Af Ablationmentioning

confidence: 96%

Atrial Fibrillation Ablation in Congenital Heart Disease: Therapeutic Challenges and Future Perspectives

Baroutidou,

Otountzidis,

Papazoglou

et al. 2024

JAHA

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“…In the current era, the correlation between the success rates of AF ablation and the complexity of CHD remains a controversial issue. The ablation of atrial septal defects seems to have the highest success rates [25]. A meta-analysis published at 2019 by Pranata R. et al showed no great differences between simple-moderate and complex CHD, with the latter needing more advanced equipment and being performed by experts in tertiary centers [25].…”

Section: Atrial Arrhythmiasmentioning

confidence: 99%

Arrhythmias in Patients with Congenital Heart Disease: An Ongoing Morbidity

Ntiloudi,

Rammos,

Karakosta

et al. 2023

JCM

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With the aging of congenital heart disease (CHD) patients, the burden of arrhythmias is expanding. Atrial arrhythmias, especially intra-atrial reentrant tachycardia and atrial fibrillation, are the most prevalent forms of arrhythmia. Managing comorbidities, such as obesity, using pharmacotherapy, including antiarrhythmics and anticoagulants, and ablation therapy has become the cornerstone of arrhythmia management. Ventricular tachycardias are also not rare; however, except for tetralogy of Fallot patients, recommendations for the use of implantable cardioverter defibrillators for primary prevention in other CHD patients are still not well established. Patients with CHD might also present with atrioventricular blockages because of their anatomy or following a surgical procedure. The scope of this article is to review the current knowledge and discuss the future directions regarding arrhythmia management in CHD patients.

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“…AF is the most prevalent severe abnormal heart rhythm associated with a fast heart rate. It refers to an abnormal, rapid, and non-synchronized muscle fiber contraction with complex patophysiology [4][5][6][7], and is recognized as an independent risk factor for stroke, with important clinical and economic consequences. Diagnosing the symptoms is important before treatment of this severe disease; however, existing commercial ECG devices for AF detection methods still show actual misdiagnosis rates.…”

Section: Introductionmentioning

confidence: 99%

AFibNet: an implementation of atrial fibrillation detection with convolutional neural network

Tutuko

Nurmaini

Tondas³

et al. 2021

BMC Med Inform Decis Mak

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Background Generalization model capacity of deep learning (DL) approach for atrial fibrillation (AF) detection remains lacking. It can be seen from previous researches, the DL model formation used only a single frequency sampling of the specific device. Besides, each electrocardiogram (ECG) acquisition dataset produces a different length and sampling frequency to ensure sufficient precision of the R–R intervals to determine the heart rate variability (HRV). An accurate HRV is the gold standard for predicting the AF condition; therefore, a current challenge is to determine whether a DL approach can be used to analyze raw ECG data in a broad range of devices. This paper demonstrates powerful results for end-to-end implementation of AF detection based on a convolutional neural network (AFibNet). The method used a single learning system without considering the variety of signal lengths and frequency samplings. For implementation, the AFibNet is processed with a computational cloud-based DL approach. This study utilized a one-dimension convolutional neural networks (1D-CNNs) model for 11,842 subjects. It was trained and validated with 8232 records based on three datasets and tested with 3610 records based on eight datasets. The predicted results, when compared with the diagnosis results indicated by human practitioners, showed a 99.80% accuracy, sensitivity, and specificity. Result Meanwhile, when tested using unseen data, the AF detection reaches 98.94% accuracy, 98.97% sensitivity, and 98.97% specificity at a sample period of 0.02 seconds using the DL Cloud System. To improve the confidence of the AFibNet model, it also validated with 18 arrhythmias condition defined as Non-AF-class. Thus, the data is increased from 11,842 to 26,349 instances for three-class, i.e., Normal sinus (N), AF and Non-AF. The result found 96.36% accuracy, 93.65% sensitivity, and 96.92% specificity. Conclusion These findings demonstrate that the proposed approach can use unknown data to derive feature maps and reliably detect the AF periods. We have found that our cloud-DL system is suitable for practical deployment

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Efficacy and safety of catheter ablation for atrial fibrillation in congenital heart disease – A systematic review and meta-analysis

Cited by 7 publications

References 24 publications

Atrial Fibrillation Ablation in Congenital Heart Disease: Therapeutic Challenges and Future Perspectives

Atrial Fibrillation Ablation in Congenital Heart Disease: Therapeutic Challenges and Future Perspectives

Arrhythmias in Patients with Congenital Heart Disease: An Ongoing Morbidity

AFibNet: an implementation of atrial fibrillation detection with convolutional neural network

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