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Updated: Jan 29, 2026
Catheter Ablation in Combination With Left Atrial Appendage Closure for Atrial Fibrillation
Published on: February 26, 2013
Development and validation of a predictive model for left ventricular diastolic improvement following catheter
Ying Cao1, Zexi Li1, Aobo Gong1
1Department of Cardiology, West China Hospital, Sichuan University, Chengdu, China.
Insights
Catheter ablation can improve left ventricular diastolic dysfunction (LVDD) in atrial fibrillation (AF) patients. A new model using LVDD grade, LV mass, ejection fraction, and stroke history predicts LVDD improvement post-ablation.
Area of Science:
- Cardiology
- Medical Imaging
- Clinical Prediction Models
Background:
- Left ventricular diastolic dysfunction (LVDD) impact post-catheter ablation for atrial fibrillation (AF) is unclear.
- Lack of predictive tools for LVDD improvement after AF ablation.
Purpose of the Study:
- To develop and validate a predictive model for LVDD improvement after catheter ablation in AF patients.
- To identify key predictors of diastolic function recovery post-ablation.
Main Methods:
- Retrospective study of 141 AF patients with LVDD undergoing ablation.
- LVDD improvement defined as ≥1 grade reduction within 12 months.
- Logistic regression with 10-fold cross-validation; ROC, calibration, and DCA for evaluation.
Main Results:
- 49.6% of patients showed LVDD improvement.
- Predictors: higher baseline LVDD grade, lower LV mass index (LVMI), lower ejection fraction (LVEF), no stroke history.
- Model AUC: 0.786 (training), 0.756 (validation); comparable in sensitivity analysis.
Conclusions:
- A novel model integrating LVDDG, LVMI, LVEF, and stroke history predicts LVDD improvement post-ablation.
- Aids in selecting AF patients for ablation and guiding adjunctive therapies.
Background:
The impact of catheter ablation on left ventricular diastolic function in patients with atrial fibrillation (AF) and left ventricular diastolic dysfunction (LVDD) remains poorly characterised. There are no robust tools available to predict improvement in LVDD post-ablation.
Methods:
The retrospective study enrolled 141 patients diagnosed with AF and LVDD undergoing ablation. LVDD improvement was defined as a reduction of at least one grade in LVDD grade (LVDDG) within 12 ± 3 months after ablation. The predictive model was constructed using a logistic regression with 10-fold validation with stratification. The performance of the model was evaluated using ROC analysis (AUC), calibration plots, and decision curve analysis (DCA). An exploratory sensitivity analysis applying the updated 2025 ASE recommendations for LVDD assessment was additionally performed.
Results:
49.6% (70/141) of patients showed improvement in LVDD. The predictive model incorporated four independent predictors: higher baseline LVDDG, lower left ventricular mass index (LVMI), lower left ventricular ejection fraction (LVEF), and absence of a history of stroke. The AUC was 0.786 (95% CI 0.760-0.809) in the training cohort and 0.756 (95% CI: 0.677-0.835) in the validation cohort, with acceptable calibration and net clinical benefit across a restricted range of threshold probabilities. In the sensitivity analysis using the redefined LVDD endpoint, discrimination remained comparable (AUC 0.783, 95% CI 0.703-0.856).
Conclusions:
A novel model integrating LVDDG, LVMI, LVEF and stroke history effectively predicts post-ablation LVDD improvement in AF patients with LVDD, aiding patient selection for ablation and guiding adjunctive therapies.
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