Background - Non-pulmonary vein (NPV) trigger has been reported as an important predictor of recurrence post-atrial fibrillation (AF) ablation. Elimination of NPV triggers can reduce the recurrence of post-ablation AF. Deep learning was applied to pre-ablation pulmonary vein computed tomography (PVCT) geometric slices to create a prediction model for NPV triggers in patients with paroxysmal atrial fibrillation (PAF). Methods - We retrospectively analyzed 521 PAF patients who underwent catheter ablation of PAF. Among them, PVCT geometric slices from 358 non-recurrent AF patients (1-3 mm interspace per slice, 20-200 slices for each patient, ranging from the upper border of the left atrium to the bottom of the heart, for a total of 23683 images of slices) were used in the deep learning process, the ResNet34 of the neural network, to create the prediction model of the NPV trigger. There were 298 (83.2%) patients with only pulmonary vein (PV) triggers and 60 (16.8%) patients with NPV triggers +/- PV triggers. The patients were randomly assigned to either training, validation or test groups and their data was allocated according to those sets. The image datasets were split into training (n=17340), validation (n=3491), and testing (n=2852) groups, which had completely independent sets of patients. Results - The accuracy of prediction in each PVCT image for NPV trigger was up to 82.4±2.0%. The sensitivity and specificity were 64.3±5.4% and 88.4±1.9%, respectively. For each patient, the accuracy of prediction for a NPV trigger was 88.6±2.3%. The sensitivity and specificity were 75.0±5.8% and 95.7±1.8%, respectively. The area under the curve (AUC) for each image and patient were 0.82±0.01 and 0.88±0.07, respectively. Conclusions - The deep learning model using pre-ablation PVCT can be applied to predict the trigger origins in PAF patients receiving catheter ablation. The application of this model may identify patients with a high risk of NPV trigger before ablation.

Background: Although several risk schemes have been proposed to predict new‐onset atrial fibrillation (AF), clinical prediction models specific for Asian patients were limited. In the present study, we aimed to develop a clinical risk score (Taiwan AF score) for AF prediction using the whole Taiwan population database with a long‐term follow‐up. Methods and Results: Among 7 220 654 individuals aged ≥40 years without a past history of cardiac arrhythmia identified from the Taiwan Health Insurance Research Database, 438 930 incident AFs occurred after a 16‐year follow‐up. Clinical risk factors of AF were identified using Cox regression analysis and then combined into a clinical risk score (Taiwan AF score). The Taiwan AF score included age, male sex, and important comorbidities (hypertension, heart failure, coronary artery disease, end‐stage renal disease, and alcoholism) and ranged from −2 to 15. The area under the receiver operating characteristic curve of the Taiwan AF scores in the predictions of AF are 0.857 for the 1‐year follow‐up, 0.825 for the 5‐year follow‐up, 0.797 for the 10‐year follow‐up, and 0.756 for the 16‐year follow‐up. The annual risks of incident AF were 0.21%/year, 1.31%/year, and 3.37%/year for the low‐risk (score −2 to 3), intermediate‐risk (score 4 to 9), and high‐risk (score ≥10) groups, respectively. Compared with low‐risk patients, the hazard ratios of incident AF were 5.78 (95% CI, 3.76–7.75) for the intermediate‐risk group and 8.94 (95% CI, 6.47–10.80) for the high‐risk group. Conclusions: We developed a clinical AF prediction model, the Taiwan AF score, among a large‐scale Asian cohort. The new score could help physicians to identify Asian patients at high risk of AF in whom more aggressive and frequent detections and screenings may be considered.

The health crisis due to coronavirus disease 2019 (COVID-19) has shocked the world, with more than 1 million infections and casualties. COVID-19 can present from mild illness to multi-organ involvement, but especially acute respiratory distress syndrome. Cardiac injury and arrhythmias, including atrial fibrillation (AF), are not uncommon in COVID-19. COVID-19 is highly contagious, and therapy against the virus remains premature and largely unknown, which makes the management of AF patients during the pandemic particularly challenging. We describe a possible pathophysiological link between COVID-19 and AF, and therapeutic considerations for AF patients during this pandemic.