• Title/Summary/Keyword: Electrical cardioversion

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The Impact of Right Atrial Size to Predict Success of Direct Current Cardioversion in Patients With Persistent Atrial Fibrillation

  • Christoph Doring;Utz Richter;Stefan Ulbrich;Carsten Wunderlich;Micaela Ebert;Sergio Richter;Axel Linke;Krunoslav Michael Sveric
    • Korean Circulation Journal
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    • v.53 no.5
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    • pp.331-343
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    • 2023
  • Background and Objectives: The prognostic implication of right atrial (RA) and left atrial (LA) size for an immediate success of direct current cardioversion (DCCV) in atrial fibrillation (AF) remains unclear. This study aimed to compare RA and LA size for the prediction of DCCV success. Methods: Between 2012 and 2018, 734 consecutive outpatients were screened for our prospective registry. Each eligible patient received a medical history, blood analysis, and transthoracic echocardiography with a focus on indexed RA (iRA) area and LA volume (iLAV) prior to DCCV with up to three biphasic shocks (200-300-360 J) or additional administration of amiodarone or flecainide to restore sinus rhythm. Results: We enrolled 589 patients, and DCCV was in 89% (n=523) successful. Mean age was 68 ± 10 years, and 40% (n=234) had New York heart association class >II. A prevalence of the male sex (64%, n=376) and of persistent AF (86%, n=505) was observed. Although DCCV success was associated with female sex (odds ratio [OR], 1.88; 95% confidence interval [CI], 1.06-3.65), with absence of coronary heart disease and normal left ventricular function (OR, 2.24; 95% CI, 1.26-4.25), with short AF duration (OR, 1.93; 95% CI, 1.05-4.04) in univariable regression, only iRA area remained a stable and independent predictor of DCCV success (OR, 0.27; 95% CI, 0.12-0.69; area under the curve 0.71), but not iLAV size (OR, 1.16; 95% CI, 1.05-1.56) in multivariable analysis. Conclusions: iRA area is superior to iLAV for the prediction of immediate DCCV success in AF.

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

  • Soonil Kwon;Eunjung Lee;Hojin Ju;Hyo-Jeong Ahn;So-Ryoung Lee;Eue-Keun Choi;Jangwon Suh;Seil Oh;Wonjong Rhee
    • Korean Circulation Journal
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    • v.53 no.10
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    • pp.677-689
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    • 2023
  • 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.

Postoperative Automatic Junctional Tachycardia treated with Amiodarone (Amiodarone으로 치료한 postoperative automatic junctional tachycardia)

  • 이택연
    • Journal of Chest Surgery
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    • v.25 no.9
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    • pp.905-911
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    • 1992
  • Automatic junctional tachycardia is one of common atrial arrhythmia after open heart surgery which is often refractory to antiarrhythmic agents. We have experienced refractory automatic junctional tachycardia in two patients. In the first, it occured after cryosurgery for AV nodal reentry tachycardia and simultaneous dissection of a posterior septal bypass tract. In the second, it complicated the postoperative course of a patient who received intracardiac repair for double outlet right ventricle, ventricular septal defect, and pulmonary stenosis. Conventional therapy with atrial pacing, verapamil, digoxin, and electrical cardioversion were ineffective. Therefore, amiodarone was administered intravenously and it controlled automatic junctional tachycardia. The need for accurate and rapid diagnosis of this condition along with results of treatment are discussed.

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