Abstract 4370598: Machine Learning Models to Predict High Atrial Fibrillation Burden Post-Catheter Ablation in Patients with Persistent AF: Insights from the DECAAF II Trial
Bibliographic record
Abstract
Background: High atrial fibrillation (AF) burden is associated with increased risk of stroke and heart failure. While catheter ablation reduces AF burden in most patients, a minority remain at risk for high AF burden after the procedure. Objective: In this study, we aimed to utilize machine learning to predict high AF burden post-ablation in patients with persistent AF. Methods: This study analyzed six hundred and eighty-five with persistent AF (mean age: 62.0 ± 9.1; women: 20%) who underwent catheter ablation in the DECAAF II trial and were followed for a total of 540 days. Four machine-learning models—Elastic Net, Decision Tree, Random Forest, and XGBoost—were developed to predict each of AF recurrence and AF burden ≥10% using 200 pre-ablation variables, including clinical, MRI, and laboratory data. The models were trained and validated using stratified 5-fold cross-validation. SHapley Additive exPlanations (SHAP) were derived to explain the most impactful features collected from each patient. Results: The XGBoost models outperformed all other models in predicting AF recurrence (30 variables; cross-validated AUC = 0.64 ± 0.04) and AF burden ≥ 10% (27 variables; cross-validated AUC of 0.66 ± 0.03) (Figure 1A). SHAP analysis revealed the top predictors of high AF burden on a patient-specific level, including left atrial volume index (importance: 0.1), age (0.02), left atrial appendage enhancement (0.01), Utah stage <3 (0.01), and left pulmonary vein enhancement percentage (0.01). Fatigue at rest (0.01) and frequency of AF episodes (0.001) based on patient-filled questionnaires (University of Toronto Atrial Fibrillation Severity Scale- AFSS) did contribute to the prediction (Figure 1B). Conclusion: XGBoost models, augmented by SHAP explainability, were the most reliable and explainable models for predicting both recurrence and high post-ablation AF burden (≥10%). These models can lead to a more granular risk stratification and facilitate future patient-specific management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".