Recent versions of evidence-based guidelines on the management of atrial fibrillation (AF) have been published by the European Society of Cardiology (ESC) in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS), the American College of Cardiology, American Heart Association, and the Heart Rhythm Society (AHA/ACC/HRS), and the Canadian Cardiovascular Society/Canadian Heart Rhythm Society (CCS). As all societies refer to the same multicentric and usually multinational studies, the similarities undoubtedly outweigh the differences. Nonetheless, interesting differences can often be found in details, which are usually based on a different assessment of the same study, the availability of data in relation to the publication date and local preferences and availabilities of certain cardiovascular drugs. The following article aims at lining out these similarities and differences.

Plenty of non-cardiovascular drugs alter cardiac electrophysiology and may ultimately lead to life-threatening arrhythmias. In clinical practice, measuring the QT interval as a marker for the repolarization period is the most common tool to assess the electrophysiologic safety of drugs. However, the sole measurement of the QT interval may be insufficient to determine the proarrhythmic risk of non-cardiovascular agents. Several other markers are considered in pre-clinical safety testing to determine potential harm on cardiac electrophysiology. Besides measuring typical electrophysiologic parameters such as repolarization duration, whole-heart models allow the determination of potential predictors for proarrhythmia. Spatial and temporal heterogeneity as well as changes of shape of the action potential can be easily assessed. In addition, provocation manoeuvers (either by electrolyte imbalances or programmed pacing protocols) may induce sustained arrhythmias and thereby determine ventricular vulnerability to arrhythmias. Compared with the human heart, the rabbit heart possesses a similar distribution of ion currents that govern cardiac repolarization, resulting in a rectangular action potential configuration in both species. In addition, similar biophysical properties of rabbit and human cardiac ion channels lead to a comparable pharmacologic response in human and rabbit hearts. Of note, arrhythmia patterns resemble in both species due to the similar effective size of human and rabbit hearts. Thus, the rabbit heart is particularly suitable for testing the electrophysiologic safety of drugs. Several experimental setups have been developed for studying cardiac electrophysiology in rabbits, ranging from single cell to tissue preparations, whole-heart setups, and in vivo models.

Recent experimental studies suggested direct effects of the anti-influenza drug oseltamivir on cardiac electrophysiology. We therefore aimed at analyzing potential antiarrhythmic effects of oseltamivir on atrial fibrillation (AF) in an experimental whole-heart model. Twelve rabbit hearts were isolated and Langendorff perfused. Thereafter, hearts were paced at cycle lengths of 350, 250, and 200 ms in the atrium. A standardized protocol employing atrial burst pacing induced AF in 4 of 12 hearts under baseline conditions (33%, 11 episodes). Subsequently, a combination of acetylcholine (1 μM) and isoproterenol (1 μM) was administered to increase AF occurrence. Two monophasic action potential recordings on the left and two on the right atrial epicardium displayed a decrease of atrial action potential duration (aAPD, -38 ms, p < 0.01) and atrial effective refractory period (aERP; -20 ms, p < 0.05). Under the influence of acetylcholine/isoproterenol AF was inducible in 8 of 12 hearts (66%; 69 episodes). Additional infusion of oseltamivir (100 μM) resulted in a significant increase of both aAPD (+ 29 ms, p < 0.05) and aERP (+ 40 ms, p < 0.01) leading to an increase of atrial post-repolarization refractoriness (aPRR). Under the influence of oseltamivir only 3 of 12 hearts (25%, 8 episodes) remained inducible. In six additional hearts oseltamivir (50 μM and 100 μM) did not significantly alter ventricular APD, QRS duration and QT interval but induced a significant increase of ventricular ERP. In the present experimental study, acute infusion of the anti-influenza drug oseltamivir reduced atrial fibrillation. The antiarrhythmic effect can be explained by a significant increase in aERP and aPRR. These results suggest an antiarrhythmic potential of oseltamivir in atrial arrhythmias.

Machine learning has immense novel but also disruptive potential for medicine. Numerous applications have already been suggested and evaluated concerning cardiovascular diseases. One important aspect is the detection and management of potentially thrombogenic arrhythmias such as atrial fibrillation. While atrial fibrillation is the most common arrhythmia with a lifetime risk of one in three persons and an increased risk of thromboembolic complications such as stroke, many atrial fibrillation episodes are asymptomatic and a first diagnosis is oftentimes only reached after an embolic event. Therefore, screening for atrial fibrillation represents an important part of clinical practice. Novel technologies such as machine learning have the potential to substantially improve patient care and clinical outcomes. Additionally, machine learning applications may aid cardiologists in the management of patients with already diagnosed atrial fibrillation, for example, by identifying patients at a high risk of recurrence after catheter ablation. We summarize the current state of evidence concerning machine learning and, in particular, artificial neural networks in the detection and management of atrial fibrillation and describe possible future areas of development as well as pitfalls. Graphical abstract Typical data flow in machine learning applications for atrial fibrillation detection.

1. Introduction: Pulmonary vein isolation (PVI) is an established procedure used to achieve rhythm control in atrial fibrillation (AF). In obese patients (pts), in whom AF occurs more frequently, a reduced effectiveness of PVI has been observed. Therefore, this study’s aim was to compare the long-term efficacy of PVI between obese and non-obese patients. 2. Methods: We enrolled 111 consecutive pts with a body mass index (BMI) of >30 kg/m2 undergoing PVI from our large registry. Procedural data and outcomes were compared with a matched group of 115 non-obese PVI pts and the long-term outcomes were analyzed. 3. Results: Overall follow-up duration was 314 patient-years in the obese and 378 patient-years in the non-obese group. The follow-up rate was 71% in the obese and 76% in the non-obese group. In both groups, their AF-characteristics did not differ significantly, while known risk factors were significantly more prevalent in the obese group. Procedural characteristics were similar in both groups. During follow-up, the obese pts demonstrated significant weight loss compared to the non-obese pts, while at the same time, the overall recurrence rate during follow-up did not differ significantly between both groups (obese: 39.2% and non-obese: 43.7%). PVI related and long-term complications were comparable between both groups. In the univariate analysis, obesity was not found to be associated with an increased AF recurrence risk. 4. Conclusion: These real-life data demonstrate that obese pts may not show higher AF recurrence rates after PVI compared to pts with normal body weight. Furthermore, PVI was found to be safe and effective in obese patients; thus, a BMI alone may not be a criterion for refusal of PVI.