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Consistency of ablations with trainee and increasing independence during fellowship training-Analysis of ablation data by CARTONET.

John WhitakerTina D HunterJane CarseyWilliam H ThatcherDon YungherStanislav GoldbergChristina KanekoMati AmitOmar KreidiehClinton ThurberNathaniel SteigerDavid ChangUyanga BatnyamEsseim SharmaSeth McClennenSunil KapurThomas TadrosWilliam H SauerBruce A KoplanUsha TedrowPaul C Zei
Published in: Journal of cardiovascular electrophysiology (2024)
Objective ablation data from Cartonet showed that the progression of trainees through CCEP training does not impact lesion-level measures of treatment efficacy (i.e., catheter stability, impedance drop). Data demonstrates increasing independence over a training fellowship. Analyses like these could be useful to inform individualized training programs and to track trainee's progress. It may also be a useful quality assurance tool for ensuring ongoing consistency of treatment delivered within training institutions.
Keyphrases
  • virtual reality
  • electronic health record
  • big data
  • machine learning
  • computed tomography
  • data analysis
  • atrial fibrillation
  • artificial intelligence
  • ultrasound guided
  • catheter ablation