Pharmacological Cardioversion after Pre-Treatment with Antiarrythmic Drugs Prior to Electrical Cardioversion in Persistent Atrial Fibrillation: Impact on Maintenance of Sinus Rhythm

Drug management of atrial fibrillation in light of guidelines and current evidence: an Italian Survey on behalf of Italian Association of Arrhythmology and Cardiac Pacing

AIM

Atrial fibrillation is a multifaceted disease requiring personalized treatment, in accordance with current ESC guidelines. Despite a wide range of literature, we still have various aspects dividing the opinion of the experts in rate control, rhythm control and thromboembolic prophylaxis. The aim of this survey was to provide a country-wide picture of current practice regarding atrial fibrillation pharmacological management according to a patient's characteristics.

METHODS

Data were collected using an in-person survey that was administered to members of the Italian Association of Arrhythmology and Cardiac Pacing.

RESULTS

We collected data from 106 physicians, working in 72 Italian hospitals from 15 of 21 regions. Our work evidenced a high inhomogeneity in atrial fibrillation management regarding rhythm control, rate control and thromboembolic prophylaxis in both acute and chronic patients. This element was more pronounced in settings in which literature shows a lack of evidence and, consequently, the indications provided by the guidelines are weak or absent.

CONCLUSION

This National survey evidenced a high inhomogeneity in current approaches adopted for atrial fibrillation management by a sample of Italian cardiologist experts in arrhythmia management. Further studies are needed to explore if these divergences are associated with different long-term outcomes.

Outcome Analysis in Elective Electrical Cardioversion of Atrial Fibrillation Patients: Development and Validation of a Machine Learning Prognostic Model

Background: The integrated approach to electrical cardioversion (EC) in atrial fibrillation (AF) is complex; candidates can resolve spontaneously while waiting for EC, and post-cardioversion recurrence is high. Thus, it is especially interesting to avoid the programming of EC in patients who would restore sinus rhythm (SR) spontaneously or present early recurrence. We have analyzed the whole elective EC of the AF process using machine-learning (ML) in order to enable a more realistic and detailed simulation of the patient flow for decision making purposes. Methods: The dataset consisted of electronic health records (EHRs) from 429 consecutive AF patients referred for EC. For analysis of the patient outcome, we considered five pathways according to restoring and maintaining SR: (i) spontaneous SR restoration, (ii) pharmacologic-cardioversion, (iii) direct-current cardioversion, (iv) 6-month AF recurrence, and (v) 6-month rhythm control. We applied ML classifiers for predicting outcomes at each pathway and compared them with the CHA2DS2-VASc and HATCH scores. Results: With the exception of pathway (iii), all ML models achieved improvements in comparison with CHA2DS2-VASc or HATCH scores (p < 0.01). Compared to the most competitive score, the area under the ROC curve (AUC-ROC) was: 0.80 vs. 0.66 for predicting (i); 0.71 vs. 0.55 for (ii); 0.64 vs. 0.52 for (iv); and 0.66 vs. 0.51 for (v). For a threshold considered optimal, the empirical net reclassification index was: +7.8%, +47.2%, +28.2%, and +34.3% in favor of our ML models for predicting outcomes for pathways (i), (ii), (iv), and (v), respectively. As an example tool of generalizability of ML models, we deployed our algorithms in an open-source calculator, where the model would personalize predictions. Conclusions: An ML model improves the accuracy of restoring and maintaining SR predictions over current discriminators. The proposed approach enables a detailed simulation of the patient flow through personalized predictions.