Emre Oto1, Sercan Okutucu2, Deniz Katircioglu-Öztürk1, Halil Altay Güvenir3, Ergun Karaagaoglu4, Martin Borggrefe5, Günter Breithardt6,7, Andreas Goette6,8, Ursula Ravens9, Gerhard Steinbeck10, Karl Wegscheider11, Ali Oto2, Paulus Kirchhof6,12,13. 1. Medical Information Technology Solutions(MITS), Bilkent University Cyberpark, Ankara, Turkey. 2. Department of Cardiology, Memorial Ankara Hospital, Memorial Healthcare Group, Ankara, Turkey. 3. Department of Computer Engineering, Faculty of Engineering, Bilkent University, Ankara, Turkey. 4. Department of Biostatistics, Faculty of Medicine, Hacettepe University, Ankara, Turkey. 5. Department of Cardiology, University of Mannheim, Mannheim, Germany. 6. Atrial Fibrillation Network Association, Münster, Germany. 7. Department of Cardiovascular Medicine, Division of Rhythmology, University Hospital Münster, Münster, Germany. 8. Department of Cardiology, Vincenz-Krankenhaus, Paderborn, Germany. 9. Department of Pharmacology, Technical University, Dresden, Germany. 10. Department of Cardiology, Ludwig-Maximilians-University of Munich, Germany. 11. Department of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany. 12. Institute of Cardiovascular Sciences, University of Birmingham and SWBH and UHB NHS Trusts, Birmingham, UK. 13. Department of Cardiovascular Medicine, University Hospital Münster, Münster, Germany.
Abstract
AIMS: Data mining is the computational process to obtain information from a data set and transform it for further use. Herein, through data mining with supportive statistical analyses, we identified and consolidated variables of the Flecainide Short-Long (Flec-SL-AFNET 3) trial dataset that are associated with the primary outcome of the trial, recurrence of persistent atrial fibrillation (AF) or death. METHODS AND RESULTS: The 'Ranking Instances by Maximizing the Area under the ROC Curve' (RIMARC) algorithm was applied to build a classifier that can predict the primary outcome by using variables in the Flec-SL dataset. The primary outcome was time to persistent AF or death. The RIMARC algorithm calculated the predictive weights of each variable in the Flec-SL dataset for the primary outcome. Among the initial 21 parameters, 6 variables were identified by the RIMARC algorithm. In univariate Cox regression analysis of these variables, increased heart rate during AF and successful pharmacological conversion (PC) to sinus rhythm (SR) were found to be significant predictors. Multivariate Cox regression analysis revealed successful PC as the single relevant predictor of SR maintenance. The primary outcome risk was 3.14 times (95% CI:1.7-5.81) lower in those who had successful PC to SR than those who needed electrical cardioversion. CONCLUSIONS: Pharmacological conversion of persistent AF with flecainide without the need for electrical cardioversion is a powerful and independent predictor of maintenance of SR. A strategy of flecainide pretreatment for 48 h prior to planned electrical cardioversion may be a useful planning of a strategy of long-term rhythm control. Published on behalf of the European Society of Cardiology. All rights reserved.
AIMS: Data mining is the computational process to obtain information from a data set and transform it for further use. Herein, through data mining with supportive statistical analyses, we identified and consolidated variables of the Flecainide Short-Long (Flec-SL-AFNET 3) trial dataset that are associated with the primary outcome of the trial, recurrence of persistent atrial fibrillation (AF) or death. METHODS AND RESULTS: The 'Ranking Instances by Maximizing the Area under the ROC Curve' (RIMARC) algorithm was applied to build a classifier that can predict the primary outcome by using variables in the Flec-SL dataset. The primary outcome was time to persistent AF or death. The RIMARC algorithm calculated the predictive weights of each variable in the Flec-SL dataset for the primary outcome. Among the initial 21 parameters, 6 variables were identified by the RIMARC algorithm. In univariate Cox regression analysis of these variables, increased heart rate during AF and successful pharmacological conversion (PC) to sinus rhythm (SR) were found to be significant predictors. Multivariate Cox regression analysis revealed successful PC as the single relevant predictor of SR maintenance. The primary outcome risk was 3.14 times (95% CI:1.7-5.81) lower in those who had successful PC to SR than those who needed electrical cardioversion. CONCLUSIONS: Pharmacological conversion of persistent AF with flecainide without the need for electrical cardioversion is a powerful and independent predictor of maintenance of SR. A strategy of flecainide pretreatment for 48 h prior to planned electrical cardioversion may be a useful planning of a strategy of long-term rhythm control. Published on behalf of the European Society of Cardiology. All rights reserved.
Authors: Jean C Nuñez-Garcia; Antonio Sánchez-Puente; Jesús Sampedro-Gómez; Victor Vicente-Palacios; Manuel Jiménez-Navarro; Armando Oterino-Manzanas; Javier Jiménez-Candil; P Ignacio Dorado-Diaz; Pedro L Sánchez Journal: J Clin Med Date: 2022-05-07 Impact factor: 4.964
Authors: Nicklas Vinter; Anne Sofie Frederiksen; Andi Eie Albertsen; Gregory Y H Lip; Morten Fenger-Grøn; Ludovic Trinquart; Lars Frost; Dorthe Svenstrup Møller Journal: Open Heart Date: 2020-06