Prediction of early recurrence of atrial fibrillation after external cardioversion by means of P wave signal-averaged electrocardiogram

Body surface mapping of P-waves in sinus rhythm to predict recurrence following cardioversion for atrial fibrillation

Background

Direct current cardioversion (DCCV) is used as elective and emergency rhythm control for atrial fibrillation (AF). We aimed to explore the role of P-wave parameters measured during sinus rhythm using body surface mapping (BSM) in predicting successful DCCV for persistent atrial fibrillation (persAF) at 12 months.

Methods

This case-control study included 56 males >18 years old who underwent DCCV for persAF. P-wave parameter collection after DCCV for AF was done using 128 unipolar leads. A band-pass filter of 1-50 Hz was utilised. Corrected P-wave duration (PWDc), P-wave amplitude, and P-wave dispersion were measured to predict 12-month outcomes and time of recurrence.

Results

The mean age was 64 ± 4 years, and 23 patients (44%) were on amiodarone. The 12-month success rate was 44% (n = 23), while the rest reverted to AF after 2.6 ± 0.4 months. The parameters were comparable between successful and failed DCCV in the entire cohort and patients not on amiodarone. In patients on amiodarone, patients with failed arms had higher PWDc than those with successful arms (188 vs. 150 ms, P = 0.04). Receiver operator characteristic curve analysis for PWDc in the amiodarone cohort showed an area under the curve (AUC) of 0.75 and P = 0.049. A recurrence cut-off >161 ms had a sensitivity of 69% and a specificity of 100%, with a hazard ratio of 10.7, P = 0.004. The parameters were not predictive of the time of recurrence.

Conclusion

In patients on amiodarone, increased PWDc measured using BSM was associated with higher AF recurrence at 12 months following DCCV for persAF.

Machine Learning Prediction for the Recurrence After Electrical Cardioversion of Patients With Persistent Atrial Fibrillation

BACKGROUND AND OBJECTIVES

There is limited evidence regarding machine-learning prediction for the recurrence of atrial fibrillation (AF) after electrical cardioversion (ECV). This study aimed to predict the recurrence of AF after ECV using machine learning of clinical features and electrocardiograms (ECGs) in persistent AF patients.

METHODS

We analyzed patients who underwent successful ECV for persistent AF. Machine learning was designed to predict patients with 1-month recurrence. Individual 12-lead ECGs were collected before and after ECV. Various clinical features were collected and trained the extreme gradient boost (XGBoost)-based model. Ten-fold cross-validation was used to evaluate the performance of the model. The performance was compared to the C-statistics of the selected clinical features.

RESULTS

Among 718 patients (mean age 63.5±9.3 years, men 78.8%), AF recurred in 435 (60.6%) patients after 1 month. With the XGBoost-based model, the areas under the receiver operating characteristic curves (AUROCs) were 0.57, 0.60, and 0.63 if the model was trained by clinical features, ECGs, and both (the final model), respectively. For the final model, the sensitivity, specificity, and F1-score were 84.7%, 28.2%, and 0.73, respectively. Although the AF duration showed the best predictive performance (AUROC, 0.58) among the clinical features, it was significantly lower than that of the final machine-learning model (p<0.001). Additional training of extended monitoring data of 15-minute single-lead ECG and photoplethysmography in available patients (n=261) did not significantly improve the model's performance.

CONCLUSIONS

Machine learning showed modest performance in predicting AF recurrence after ECV in persistent AF patients, warranting further validation studies.

Effect of renal sympathetic denervation on the progression of paroxysmal atrial fibrillation in canines with long-term intermittent atrial pacing

AIMS

The aim of the present study was to explore the effect of renal sympathetic denervation (RSD) on the progression of paroxysmal atrial fibrillation (AF) in canines with long-term intermittent atrial pacing.

METHODS AND RESULTS

Nineteen beagles were randomly divided into sham-operated group (six dogs), control group (six dogs), and RSD group (seven dogs). Sham-operated group were implanted with pacemakers without pacing; control group were implanted with pacemakers with long-term intermittent atrial pacing; and RSD group underwent catheter-based RSD bilaterally and were simultaneously implanted with pacemakers. Atrial pacing was maintained for 8 h a day and a total of 12 weeks in the control group and RSD group. Echocardiography showed that the left atrial structure and function were significantly improved in the RSD group compared with the control group (P < 0.05). Compared with the control group, the RSD group had fewer incidences of AF and a shorter duration of AF (P < 0.05) after long-term intermittent atrial pacing. In addition to increased atrial effective refractory period (AERP) and AF cycle length, AERP dispersion and P-wave duration and dispersion were significantly decreased in the RSD group compared with the control group (P < 0.05). Atrial morphological evaluation suggested that fibrosis and ultrastructural changes induced by long-term intermittent atrial pacing were markedly suppressed in the RSD dogs compared with controls (P < 0.05). Immunohistochemistry results showed that connexin 43 distribution in RSD mid-myocardial was significantly fewer heterogeneous than that in control mid-myocardial (P < 0.05).

CONCLUSION

Renal denervation inhibits the progression of paroxysmal AF, which might be related to the suppression of atrial electrophysiology and structural heterogeneity.

Automatic real time detection of atrial fibrillation

Atrial fibrillation (AF) is the most common sustained arrhythmia and is associated with significant morbidity and mortality. Timely diagnosis of the arrhythmia, particularly transient episodes, can be difficult since patients may be asymptomatic. In this study, we describe a robust algorithm for automatic detection of AF based on the randomness, variability and complexity of the heart beat interval (RR) time series. Specifically, we employ a new statistic, the Turning Points Ratio, in combination with the Root Mean Square of Successive RR Differences and Shannon Entropy to characterize this arrhythmia. The detection algorithm was tested on two databases, namely the MIT-BIH Atrial Fibrillation Database and the MIT-BIH Arrhythmia Database. These databases contain several long RR interval series from a multitude of patients with and without AF and some of the data contain various forms of ectopic beats. Using thresholds and data segment lengths determined by Receiver Operating Characteristic (ROC) curves we achieved a high sensitivity and specificity (94.4% and 95.1%, respectively, for the MIT-BIH Atrial Fibrillation Database). The algorithm performed well even when tested against AF mixed with several other potentially confounding arrhythmias in the MIT-BIH Arrhythmia Database (Sensitivity = 90.2%, Specificity = 91.2%).

The effects of losartan on signal-averaged P wave in patients with atrial fibrillation

BACKGROUND

Losartan has recently been reported to suppress atrial structural remodeling. However, few reports exist on signal-averaged electrocardiography (ECG) for preventing atrial electrical remodeling. We examined the effect of losartan on atrial electricity by using signal-averaged ECG of P waves.

METHODS

The subjects comprised 40 patients with essential hypertension complicated with symptomatic paroxysmal atrial fibrillation. The patients received pilsicainide for the complication; they were defibrillated and divided into two subgroups for antihypertensive therapy: calcium antagonist-administrated (CB) and losartan-administrated (LOS) groups. We recorded the signal-averaged electrocardiography of P waves and calculated (1) filtered P wave duration (PD), (2) the voltage integral for the entire P wave (integral-p), and (3) the root mean square voltages of the terminal 40, 30, and 20 ms (RMS-40, RMS-30, and RMS-20). Procollagen C propeptide type I (PIP) and A- and B-type natriuretic peptide (ANP and BNP, respectively) levels in the groups were measured before and after antihypertensive agent administration.

RESULTS

RMS-20 increased significantly and PD decreased significantly in the LOS group 24 weeks after antihypertensive drug administration; however, they remained unchanged in the CB group. Integral-p decreased significantly in both groups, and the decrease rate was significantly higher in the LOS group. Serum BNP levels decreased significantly only in the LOS group.

CONCLUSIONS

Losartan inhibits atrial remodeling by inhibiting left atrial fibrosis as indicated by the procollagen C propeptide type I, ANP, and BNP levels. Signal-averaged ECG demonstrated that losartan suppresses atrial fibrillation recurrence by improving atrial conduction disturbance.