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.

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.

A 70-years-old male with a history of hypertension and drug resistant paroxysmal atrial fibrillation (AF) presented to our hospital for catheter ablation to his symptomatic AF. He had no prior surgical or percutaneous procedure to close or exclude the left atrial appendage (LAA). A transesophageal echocardiography (TEE) was performed to rule out intra-cardiac thrombus prior to the ablation procedure. Although the TEE imaging at multiple acquisition angles was obtained, the LAA could not be visualized and an absence of the LAA was suspected. An absence of the LAA was confirmed using cardiac computed tomography (CT), which included 3D reconstruction. Additionally, the LAA was not visualized with left atrium (LA) angiography. During the ablation procedure, 3D voltage mapping in LA was created and no low voltage area or abnormal potential was recorded around the usual root location of the LAA. Successful electrical pulmonary vein isolation was achieved with no major complications. After six months of follow-up, the patient remained in sinus rhythm without any antiarrhythmic drugs and showed no related clinical symptoms. He stopped his anticoagulation therapy due to lack of evidence of AF recurrence and an absence of LAA. Multimodality imaging allowed us to identify the congenital absence of LAA.