Literature DB >> 33079437

Atrial fibrillation ablation success defined by duration of recurrence on cardiac implantable electronic devices.

Graham Lohrmann1, Rachel Kaplan1, Paul D Ziegler2, João Monteiro2, Rod Passman1,3,4.   

Abstract

INTRODUCTION: Ablation for atrial fibrillation (AF) has emerged as an effective method of rhythm control. This exploratory analysis aimed to determine how various measures of recurrence would influence the definition of treatment success.
METHODS: Using an electronic health record data set from January 2007 to June 2019 linked with Medtronic cardiac implantable electronic device (CIED) data, patients who underwent a first AF ablation procedure following CIED implantation were identified. Data were analyzed for recurrence of AF stratified by varying definitions of successful ablation. The performance of various simulated external AF monitoring strategies was assessed.
RESULTS: A total of 665 patients were analyzed including 248 with paroxysmal AF (mean age: 66.2 ± 9.3 years, 73.0% male) and 417 patients with persistent AF (mean age: 67.3 ± 9.0 years, 73.6% male). Among patients with paroxysmal AF, survival free from recurrence at 1 year ranged from 28.2% to 72.1% (>6 min and >23 h thresholds, respectively) with an overall median percentage of time in AF reduction of 99.6%. Among patients with persistent AF, survival free from recurrence at 1 year ranged from 24.9% to 60.0% (>6 min and 7 consecutive days > 23 h thresholds, respectively) with an overall median percentage of time in AF reduction of 99.3%. A single 7-day monitoring strategy had a sensitivity of less than 50% for detecting AF greater than 6 min in patients with paroxysmal and persistent AF.
CONCLUSION: In this real-world data set of AF patients with CIEDs undergoing catheter ablation, treatment success varied substantially with different definitions of minimally required AF duration and is significantly impacted by the method of recurrence detection.
© 2020 Wiley Periodicals LLC.

Entities:  

Keywords:  ablation; atrial fibrillation; burden; monitoring; recurrence

Mesh:

Year:  2020        PMID: 33079437     DOI: 10.1111/jce.14781

Source DB:  PubMed          Journal:  J Cardiovasc Electrophysiol        ISSN: 1045-3873


  6 in total

1.  CHA2DS2VASc score as a predictor of ablation success defined by continuous long-term monitoring.

Authors:  Graham Lohrmann; Albert Liu; Paul Ziegler; João Monteiro; Nathan Varberg; Rod Passman
Journal:  J Interv Card Electrophysiol       Date:  2022-08-02       Impact factor: 1.759

2.  Influencing Factors of Recurrence of Nonvalvular Atrial Fibrillation after Radiofrequency Catheter Ablation and Construction of Clinical Nomogram Prediction Model.

Authors:  Zhong-Bao Ruan; Hong-Xia Liang; Fei Wang; Ge-Cai Chen; Jun-Guo Zhu; Yin Ren; Li Zhu
Journal:  Int J Clin Pract       Date:  2022-03-15       Impact factor: 3.149

3.  HRS White Paper on Clinical Utilization of Digital Health Technology.

Authors:  Elaine Y Wan; Hamid Ghanbari; Nazem Akoum; Zachi Itzhak Attia; Samuel J Asirvatham; Eugene H Chung; Lilas Dagher; Sana M Al-Khatib; G Stuart Mendenhall; David D McManus; Rajeev K Pathak; Rod S Passman; Nicholas S Peters; David S Schwartzman; Emma Svennberg; Khaldoun G Tarakji; Mintu P Turakhia; Anthony Trela; Hirad Yarmohammadi; Nassir F Marrouche
Journal:  Cardiovasc Digit Health J       Date:  2021-07-10

4.  Healthcare utilization and clinical outcomes after ablation of atrial fibrillation in patients with and without insertable cardiac monitoring.

Authors:  Moussa C Mansour; Emily M Gillen; Audrey Garman; Sarah C Rosemas; Noreli Franco; Paul D Ziegler; Jesse M Pines
Journal:  Heart Rhythm O2       Date:  2022-01-07

5.  Impact of pre-ablation weight loss on the success of catheter ablation for atrial fibrillation.

Authors:  Graham Peigh; Jeremiah Wasserlauf; Kelly Vogel; Rachel M Kaplan; Anna Pfenniger; Daniel Marks; Arjun Mehta; Alexandru B Chicos; Rishi Arora; Susan Kim; Albert Lin; Nishant Verma; Kaustubha D Patil; Bradley P Knight; Rod S Passman
Journal:  J Cardiovasc Electrophysiol       Date:  2021-07-05       Impact factor: 2.942

Review 6.  Mobile Health for Arrhythmia Diagnosis and Management.

Authors:  Jayson R Baman; Daniel T Mathew; Michael Jiang; Rod S Passman
Journal:  J Gen Intern Med       Date:  2021-07-19       Impact factor: 5.128

  6 in total

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