ArticleCureus2023
Creation and Validation of an Algorithm for Predicting the Recurrence of Atrial Fibrillation Following Pulmonary Vein Isolation by Utilizing Real-World Data and Ensemble Modeling Techniques.
Article in Cureus, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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
1 citing paper in PubMed.
- 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
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction Catheter ablation (CA) of atrial fibrillation (AF) represents a mainstay in the treatment of this increasingly prevalent arrhythmia. Prospective clinical trials investigating the efficacy of CA may poorly represent real-world patient populations. However, many real-world clinical datasets possess missing data, which may impede their applicability in research. Thus, we sought to use ensemble modeling to address missing data and develop a model to estimate the probability of AF recurrence following CA. Methods We retrospectively analyzed clinical variables in 476 patients who underwent an initial CA of AF. Univariate and multivariate logistic regression was performed to determine those variables predictive of AF recurrence. A multivariate logistic model was created to estimate the probability of AF recurrence after CA. Missing data were addressed using ensemble modeling, and variable selection was performed using the aggregate of multiple models. Results After analysis, six variables remained in the model: AF during the post-procedural blanking period, coexistence of atrial flutter, end-stage renal disease, reduced left ventricular ejection fraction, prior failure of anti-arrhythmic drugs, and valvular heart disease. Predictive modeling was performed using these variables for 1000 randomly partitioned datasets (80% training, 20% testing) and 1000 random imputations for each partitioned dataset. The model predicted AF recurrence with an accuracy of 74.34 ± 3.99% (recall: 54.03 ± 8.15%; precision: 89.30 ± 4.21%; F1 score: 81.08 ± 3.65%). Conclusion We successfully identified six clinical variables that, when modeled, predicted AF recurrence following CA with a high degree of classification accuracy. Application of this model to patients undergoing CA of AF may help identify those at risk of post-procedural AF recurrence.
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