ArticleCirculation. Arrhythmia and electrophysiology2022
Predicting Atrial Fibrillation Recurrence by Combining Population Data and Virtual Cohorts of Patient-Specific Left Atrial Models.
Article in Circulation. Arrhythmia and electrophysiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 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
63 citing papers in PubMed, 1 synthesis or guideline pooled it, 86 citations in OpenAlex.
- Predictive Value of Machine Learning for Recurrence of Atrial Fibrillation after Catheter Ablation: A Systematic Review and Meta-Analysis.Reviews in cardiovascular medicine · 2023Pooled it
- Explainable machine learning model reveals its decision-making process in identifying patients with paroxysmal atrial fibrillation at high risk for recurrence after catheter ablation.BMC cardiovascular disorders · 2023Trial
- Multimodal Characterization of Atrial Fibrillation: From Patient-Specific Anatomy and Electrophysiology to Standardized Atrial Mapping.Tomography (Ann Arbor, Mich.) · 2026Review
- AI-Driven Atrial Fibrillation Management: From Signal to Strategy.Balkan medical journal · 2026Review
- Transmural heterogeneity in 3D fibrosis distribution is associated with endo-epicardial electrical dyssynchrony in persistent atrial fibrillation.The Journal of physiology · 2026Article
- Three-Dimensional Left Atrial Geometry in Atrial Fibrillation: Imaging Biomarkers, Substrate Phenotyping, and Ablation Outcome Prediction.Diagnostics (Basel, Switzerland) · 2026Review
- Atrial fibrosis in atrial fibrillation: Mechanisms, mapping techniques and clinical applications.The Journal of physiology · 2026Review
- Assessing the importance of sex and disease-specific anatomy in electrophysiology and mechanical simulations with a newly developed public virtual cohort of four-chamber heart models.PLoS computational biology · 2026Article
- Models of the human heart for biomedical research: Opportunities and challenges.Physiological reports · 2026Review
- Rapid calibration of atrial electrophysiology models using Gaussian process emulators in the ensemble Kalman filter.Scientific reports · 2026Article
- Automated generation of ablation lesion masks: a unison of electro and optic flow mapping for persistent AF virtual cohorts.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 · 2026Article
- Artificial Intelligence-driven Detection, Mapping, and Personalized Therapy for Atrial Fibrillation.The Journal of innovations in cardiac rhythm management · 2026Review
- Full-field surrogate modeling of cardiac electrophysiology encoding geometric variability.Computer methods in applied mechanics and engineering · 2026Article
- The effect of clinically relevant changes in extracellular electrolyte concentrations on human atrial arrhythmias.Communications medicine · 2025Article
- A Left Atrial Positioning System to Enable Follow-up and Cohort Studies.IEEE transactions on bio-medical engineering · 2025Article
- Development and external validation of a nomogram to predict the need for electrical cardioversion during radiofrequency ablation in patients with atrial fibrillation.International journal of cardiology. Heart & vasculature · 2025Article
- Predicting arrhythmia recurrence post-ablation in atrial fibrillation using explainable machine learning.Communications medicine · 2025Article
- Applications of digital twins in medicine.Nature biotechnology · 2025Article
- Digital Twin Models in Atrial Fibrillation: Charting the Future of Precision Therapy?Journal of personalized medicine · 2025Review
- Design and Validation of a High-Fidelity Left Atrial Cardiac Simulator for the Study and Advancement of Left Atrial Appendage Occlusion.Cardiovascular engineering and technology · 2025Article
3 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors at 5 institutions in 4 countries.
Funding
Abstract
backgroundCurrent ablation therapy for atrial fibrillation is suboptimal, and long-term response is challenging to predict. Clinical trials identify bedside properties that provide only modest prediction of long-term response in populations, while patient-specific models in small cohorts primarily explain acute response to ablation. We aimed to predict long-term atrial fibrillation recurrence after ablation in large cohorts, by using machine learning to complement biophysical simulations by encoding more interindividual variability.
methodsPatient-specific models were constructed for 100 atrial fibrillation patients (43 paroxysmal, 41 persistent, and 16 long-standing persistent), undergoing first ablation. Patients were followed for 1 year using ambulatory ECG monitoring. Each patient-specific biophysical model combined differing fibrosis patterns, fiber orientation maps, electrical properties, and ablation patterns to capture uncertainty in atrial properties and to test the ability of the tissue to sustain fibrillation. These simulation stress tests of different model variants were postprocessed to calculate atrial fibrillation simulation metrics. Machine learning classifiers were trained to predict atrial fibrillation recurrence using features from the patient history, imaging, and atrial fibrillation simulation metrics.
resultsWe performed 1100 atrial fibrillation ablation simulations across 100 patient-specific models. Models based on simulation stress tests alone showed a maximum accuracy of 0.63 for predicting long-term fibrillation recurrence. Classifiers trained to history, imaging, and simulation stress tests (average 10-fold cross-validation area under the curve, 0.85±0.09; recall, 0.80±0.13; precision, 0.74±0.13) outperformed those trained to history and imaging (area under the curve, 0.66±0.17) or history alone (area under the curve, 0.61±0.14).
conclusionA novel computational pipeline accurately predicted long-term atrial fibrillation recurrence in individual patients by combining outcome data with patient-specific acute simulation response. This technique could help to personalize selection for atrial fibrillation ablation.
Indexed as
Identifiers
What OpenQuestion holds
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.