ReviewClinical cardiology2019
Clinical scores used for the prediction of negative events in patients undergoing catheter ablation for atrial fibrillation.
Review in Clinical cardiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it, 50 citations in OpenAlex.
- Predicting recurrent atrial fibrillation after catheter ablation: a systematic review of prognostic models.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2020Pooled it
- Catheter Ablation vs Drug Therapy in Patients With Atrial Fibrillation and Nonmodifiable Recurrence Risk Factors: A Secondary Analysis of the CABANA Randomized Clinical Trial.JAMA network open · 2025Trial
- NT-proBNP as a Predictor of Atrial Fibrillation Recurrence After Cryoballoon, Radiofrequency and Pulsed-Field Ablation.Pacing and clinical electrophysiology : PACE · 2026Article
- Epicardial adipose tissue radiomic features from pre-procedural CT to predict atrial fibrillation recurrence after catheter ablation for pulmonary vein isolation.Frontiers in cardiovascular medicine · 2026Article
- Predicting Atrial Fibrillation Ablation Outcomes: Machine Learning Model Development and Validation Using a Large Administrative Claims Database.JMIR cardio · 2025Article
- Combining clinical and left atrial electromechanical remodelling data: potential to improve atrial fibrillation ablation outcome prediction.BMC medical informatics and decision making · 2025Article
- Clinical, Electrocardiographic and Echocardiographic Predictors of Atrial Fibrillation Recurrence After Pulmonary Vein Isolation.Journal of clinical medicine · 2025Review
- The relationship between the 3D electroanatomical mapping parameters of the left atrial posterior wall and the recurrence of paroxysmal atrial fibrillation.Frontiers in cardiovascular medicine · 2025Article
- Beyond Clinical Factors: Harnessing Artificial Intelligence and Multimodal Cardiac Imaging to Predict Atrial Fibrillation Recurrence Post-Catheter Ablation.Journal of cardiovascular development and disease · 2024Review
- Article
- Deep learning-based multimodal fusion of the surface ECG and clinical features in prediction of atrial fibrillation recurrence following catheter ablation.BMC medical informatics and decision making · 2024Article
- Potential Role of Left Atrial Strain to Predict Atrial Fibrillation Recurrence after Catheter Ablation Therapy: A Clinical and Systematic Review.Journal of cardiovascular development and disease · 2024Review
- Chinese Guidelines for the Diagnosis and Management of Atrial Fibrillation.Journal of geriatric cardiology : JGC · 2024Article
- Machine learning-based risk models for procedural complications of radiofrequency ablation for atrial fibrillation.BMC medical informatics and decision making · 2023Article
- Atrial fibrillation ablation outcome prediction with a machine learning fusion framework incorporating cardiac computed tomography.Journal of cardiovascular electrophysiology · 2023Article
- An Artificial Intelligence-Enabled ECG Algorithm for Predicting the Risk of Recurrence in Patients with Paroxysmal Atrial Fibrillation after Catheter Ablation.Journal of clinical medicine · 2023Article
- Machine Learning-Enabled Multimodal Fusion of Intra-Atrial and Body Surface Signals in Prediction of Atrial Fibrillation Ablation Outcomes.Circulation. Arrhythmia and electrophysiology · 2022Article
- The predictive value of galectin-3 levels on left atrial low voltage areas assessed by high-density mapping in patients with paroxysmal atrial fibrillation.Journal of arrhythmia · 2022Article
- P-Wave Beat-to-Beat Analysis to Predict Atrial Fibrillation Recurrence after Catheter Ablation.Diagnostics (Basel, Switzerland) · 2022Article
- Treatment success and its predictors as well as the complications of catheter ablation for atrial fibrillation in a high-volume centre.Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Atrial fibrillation (AF) is the most prevalent sustained cardiac arrhythmia in adults. Catheter ablation (CA) is one of the most important management strategies to reduce AF burden and AF-associated complications. In order to stratify the risk of adverse events and to predict treatment success in AF patients undergoing CA, several risk stratification scores had been developed during the last decade. The aim of this review is to provide an overview of the most important clinical risk scores predicting rhythm outcomes, electro-anatomical substrate and mortality in AF.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.