An artificial intelligence-based model for prediction of atrial fibrillation from single-lead sinus rhythm electrocardiograms facilitating screening.

AIMS: Screening for atrial fibrillation (AF) is recommended in the European Society of Cardiology guidelines. Yields of detection can be low due to the paroxysmal nature of the disease. Prolonged heart rhythm monitoring might be needed to increase yield but can be cumbersome and expensive. The aim of this study was to observe the accuracy of an artificial intelligence (AI)-based network to predict paroxysmal AF from a normal sinus rhythm single-lead ECG. METHODS AND RESULTS: A convolutional neural network model was trained and evaluated using data from three AF screening studies. A total of 478 963 single-lead ECGs from 14 831 patients aged ≥65 years were included in the analysis. The training set included ECGs from 80% of participants in SAFER and STROKESTOP II. The remaining ECGs from 20% of participants in SAFER and STROKESTOP II together with all participants in STROKESTOP I were included in the test set. The accuracy was estimated using the area under the receiver operating characteristic curve (AUC). From a single timepoint ECG, the artificial intelligence-based algorithm predicted paroxysmal AF in the SAFER study with an AUC of 0.80 [confidence interval (CI) 0.78-0.83], which had a wide age range of 65-90+ years. Performance was lower in the age-homogenous groups in STROKESTOP I and STROKESTOP II (age range: 75-76 years), with AUCs of 0.62 (CI 0.61-0.64) and 0.62 (CI 0.58-0.65), respectively. CONCLUSION: An artificial intelligence-enabled network has the ability to predict AF from a sinus rhythm single-lead ECG. Performance improves with a wider age distribution.

Systolic blood pressure increases in patients with atrial fibrillation regaining sinus rhythm after electrical cardioversion.

Direct current (DC) cardioversion is used to convert persistent atrial fibrillation (AF) to sinus rhythm (SR), but there is limited knowledge about how blood pressure (BP) is affected by conversion to SR. We sought to evaluate how BP changed in AF patients who converted to SR, compared to patients still in AF. In this retrospective registry analysis, we included a total of 487 patients, treated with DC cardioversion for persistent AF. We obtained data regarding medical history, medication, BP, and electrocardiogram the day before and 7 days after cardioversion. Systolic BP increased by 9 (±16) mm Hg (P < 0.01) and diastolic BP decreased by 3 (±9) mm Hg (P < 0.01) after conversion to SR. In the group of patients with restored SR, there was a 40% increase in the proportion of patients with a hypertensive BP level (≥140/90 mm Hg) after DC cardioversion compared to before. Patients still in AF had no significant change in BP. Systolic BP increases and diastolic BP slightly decreases when persistent AF is converted to SR. The underlying mechanisms explaining these findings are not known, but may involve either hemodynamic changes that occur when SR is restored, an underestimation of systolic BP in AF, or a combination of both. Our findings suggest that an increased attention to BP levels after a successful cardioversion is warranted.