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Background: Accurate detection of atrial fibrillation (AF) recurrence after catheter ablation is crucial. In this study, we aimed to conduct a systematic review of machine-learning-based recurrence detection in the relevant literature.
Methods: We conducted a comprehensive search of PubMed, Embase, Cochrane, and Web of Science databases from 1980 to December 31, 2022 to identify studies on prediction models for AF recurrence risk after catheter ablation. We used the prediction model risk of bias assessment tool (PROBAST) to assess the risk of bias, and R4.2.0 for meta-analysis, with subgroup analysis based on model type.
Results: After screening, 40 papers were eligible for synthesis. The pooled concordance index (C-index) in the training set was 0.760 (95% confidence interval [CI] 0.739 to 0.781), the sensitivity was 0.74 (95% CI 0.69 to 0.77), and the specificity was 0.76 (95% CI 0.72 to 0.80). The combined C-index in the validation set was 0.787 (95% CI 0.752 to 0.821), the sensitivity was 0.78 (95% CI 0.73 to 0.83), and the specificity was 0.75 (95% CI 0.65 to 0.82). The subgroup analysis revealed no significant difference in the pooled C-index between models constructed based on radiomics features and those based on clinical characteristics. However, radiomics based showed a slightly higher sensitivity (training set: 0.82 0.71, validation set: 0.83 0.73). Logistic regression, one of the most common machine learning (ML) methods, exhibited an overall pooled C-index of 0.785 and 0.804 in the training and validation sets, respectively. The Convolutional Neural Networks (CNN) models outperformed these results with an overall pooled C-index of 0.862 and 0.861. Age, radiomics features, left atrial diameter, AF type, and AF duration were identified as the key modeling variables.
Conclusions: ML has demonstrated excellent performance in predicting AF recurrence after catheter ablation. Logistic regression (LR) being the most widely used ML algorithm for predicting AF recurrence, also showed high accuracy. The development of risk prediction nomograms for wide application is warranted.
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http://dx.doi.org/10.31083/j.rcm2411315 | DOI Listing |
Background: Catheter ablation is the primary treatment option for idiopathic ventricular tachycardia (VT). It plays a key role in acute therapy of electrical storm, treatment of VTs in patients with structural heart disease (SHD), and can reduce VT burden. Here we report on 10-year clinical outcomes following VT ablation from patients enrolled in the prospective German Ablation Registry.
View Article and Find Full Text PDFEur Heart J
December 2024
Department of Cardiology, West China Hospital, Sichuan University, 37 Guoxue Road, Chengdu 610041, China.
Pacing Clin Electrophysiol
December 2024
Department of Cardiology, University Hospital of Patras, Rion-Patras, Greece.
Esophageal injury is a serious complication following atrial fibrillation catheter ablation procedures. It may manifest as atrio-esophageal fistula, pericardio-esophageal fistula (PEF), or restricted perforation, with high mortality rate if left unoperated. Chest computed tomography with intravenous contrast is the mainstay of diagnosis; however, a definite imaging diagnosis is often delayed and may worsen patient outcomes.
View Article and Find Full Text PDFPacing Clin Electrophysiol
December 2024
Department of Cardiology, University Hospitals of Leicester NHS Trust, Glenfield Hospital, Leicester, UK.
Background: Pulmonary vein isolation (PVI) has been established as an effective management option for symptomatic paroxysmal atrial fibrillation (PAF). We aimed to explore the role of P-wave parameters in a 12-lead electrocardiogram (ECG) in predicting the success of repeat PAF ablation.
Methods: We enrolled consecutive patients who underwent a second AF ablation procedure for PAF in a UK tertiary center after an index ablation conducted between 2018 and 2019 and a repeat ablation up to 2021.
Pacing Clin Electrophysiol
December 2024
Department of Cardiology, Dr. Siyami Ersek Thoracic and Cardiovascular Surgery Training and Research Hospital, Istanbul, Turkey.
This case report presents the management of tachycardiomyopathy (TCM) in a patient with Friedreich ataxia, a hereditary disorder characterized by progressive neurodegeneration and associated cardiac complications. The patient exhibited severe tachycardia-induced cardiac dysfunction, complicating the clinical picture due to the overlapping neurological symptoms of Friedreich ataxia. Utilizing a 3D mapping system, catheter ablation was performed to accurately identify and target the arrhythmogenic foci contributing to the patient's TCM.
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