Background: Radiofrequency catheter ablation (RFCA) is an effective therapy for atrial fibrillation (AF). However, it the problem of AF recurrence remains. This study investigates whether a deep convolutional neural network (CNN) can accurately predict AF recurrence in patients with AF who underwent RFCA, and compares CNN with conventional statistical analysis.Methods and Results: Three-hundred and ten patients with AF after RFCA treatment, including 94 patients with AF recurrence, were enrolled. Nine variables are identified as candidate predictors by univariate Cox proportional hazards regression (CPH). A CNNSurv model for AF recurrence prediction was proposed. The model's discrimination ability is validated by a 10-fold cross validation method and measured by C-index. After back elimination, 4 predictors are used for model development, they are N-terminal pro-BNP (NT-proBNP), paroxysmal AF (PAF), left atrial appendage volume (LAAV) and left atrial volume (LAV). The average testing C-index is 0.76 (0.72-0.79). The corresponding calibration plot appears to fit well to a diagonal, and the P value of the Hosmer-Lemeshow test also indicates the proposed model has good calibration ability. The proposed model has superior performance compared with the DeepSurv and multivariate CPH. The result of risk stratification indicates that patients with non-PAF, higher NT-proBNP, larger LAAV and LAV would have higher risks of AF recurrence. Conclusions:The proposed CNNSurv model has better performance than conventional statistical analysis, which may provide valuable guidance for clinical practice.

Arterial stiffness results in elevated left ventricular filling pressure and can promote atrial remodeling due to chronic pressure overload. However, the impact of arterial stiffness on the process of atrial remodeling in association with atrial fibrillation (AF) has not been fully evaluated. Methods:We enrolled 237 consecutive patients diagnosed with AF who had undergone ablation; data from 213 patients were analyzed. Cardio-ankle vascular index (CAVI) was used as a marker of arterial stiffness. The left atrial (LA) and right atrial (RA) volumes were determined by computed tomography imaging; atrial conduction and voltage amplitude were evaluated using a three-dimensional electromapping system used to guide the ablation procedure.

Identifying patient prognostic phenotypes facilitates precision medicine. This study aimed to explore phenotypes of patients with heart failure (HF) corresponding to prognostic condition (risk of mortality) and identify the phenotype of new patients by machine learning (ML). A unsupervised ML was applied to explore phenotypes of patients in a derivation dataset (n = 562) based on their medical records. Thereafter, supervised ML models were trained on the derivation dataset to classify these identified phenotypes. Then, the trained classifiers were further validated on an independent validation dataset (n = 168). Finally, Shapley additive explanations were used to interpret decision making of phenotype classification. Three patient phenotypes corresponding to stratified mortality risk (high, low, and intermediate) were identified. Kaplan–Meier survival curves among the three phenotypes had significant difference (pairwise comparison p < 0.05). Hazard ratio of all-cause mortality between patients in phenotype 1 (n = 91; high risk) and phenotype 3 (n = 329; intermediate risk) was 2.08 (95%CI 1.29–3.37, p = 0.003), and 0.26 (95%CI 0.11–0.61, p = 0.002) between phenotype 2 (n = 142; low risk) and phenotype 3. For phenotypes classification by random forest, AUCs of phenotypes 1, 2, and 3 were 0.736 ± 0.038, 0.815 ± 0.035, and 0.721 ± 0.03, respectively, slightly better than the decision tree. Then, the classifier effectively identified the phenotypes for new patients in the validation dataset with significant difference on survival curves and hazard ratios. Finally, age and creatinine clearance rate were identified as the top two most important predictors. ML could effectively identify patient prognostic phenotypes, facilitating reasonable management and treatment considering prognostic condition.

Background Recurrence of atrial fibrillation (AF) after pulmonary vein isolation (PVI) is associated with left atrial (LA) remodeling; however, its association with right atrial (RA) remodeling remains unclear. Objective This study aimed to identify whether RA structural remodeling could predict recurrence of AF after PVI. Methods This study prospectively analyzed 245 patients with AF who had undergone PVI. RA and LA volumes were determined by contrast‐enhanced computed tomography. Atrial structural remodeling was defined as an atrial volume of ≥110 mL according to previous reports and receiver operating characteristic curve analysis. Results After excluding 32 patients, 213 patients were analyzed. During a follow‐up period of 12 months, 41 patients (19%) demonstrated atrial arrhythmia recurrence after PVI. With the Cox proportional‐hazards model, RA structural remodeling was the only predictor of arrhythmia recurrence (hazard ratio, 1.012; 95% confidence interval 1.003‐1.021; P = .009). Kaplan–Meier analysis showed that arrhythmia recurrence was more frequent in the RA structural remodeling group compared with the group without RA remodeling (log‐rank, P < .001), and the arrhythmia‐free survival rates in these groups at 12 months were 68.0% and 91.4%, respectively. Additionally, there was a significant difference in recurrence‐free survival after RA structural remodeling in each type of AF (log‐rank, P < .001). Conclusions RA structural remodeling is a useful predictor of clinical outcome after PVI regardless of the type of AF. Our results suggest that patients without RA structural remodeling may be good candidates for successful ablation with PVI.

No abstract