• The Korean Journal of Emergency Medical Services
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• v.16 no.3
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• pp.9-18
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• 2012

Purpose : The purpose of this study was to determine the changes that occur due to the real-time monitoring of paddle pressures which has an important influence on the defibrillation success rate in defibrillation treatment known as the only treatment for cardiac arrest patients with VF. Methods : 40 people participated in the cardiac arrest simulation training and played the role of the defibrillation operator. Investigators measured the pressure of paddle while defibrillating by using instrument which was developed by the investigator. Results : Through real-time monitoring of the paddle pressures of defibrillator by indicator, the front sternum paddle showed a 77.5% success rate and the apex paddle showed a 40% success rate. While the values without monitoring the paddle pressures, the front sternum paddle showed a 51% success rate and the apex paddle showed a 20% success rate. These experiment revealed statistically significant(p <.001) low success rate. Conclusion : The method of monitoring the paddle pressures during defibrillation showed that the paddle can be precisely gripped. The success rate of paddle pressures is significantly correlated with height, weight and grip strength.

• 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.