Research Article: Online webinar training to analyse complex atrial fibrillation maps: A randomized trial

Date Published: July 3, 2019

Publisher: Public Library of Science

Author(s): João Mesquita, Natasha Maniar, Tina Baykaner, Albert J. Rogers, Mark Swerdlow, Mahmood I. Alhusseini, Fatemah Shenasa, Catarina Brizido, Daniel Matos, Pedro Freitas, Ana Rita Santos, Gustavo Rodrigues, Claudia Silva, Miguel Rodrigo, Yan Dong, Paul Clopton, António M. Ferreira, Sanjiv M. Narayan, Jichao Zhao.


Specific tools have been recently developed to map atrial fibrillation (AF) and help guide ablation. However, when used in clinical practice, panoramic AF maps generated from multipolar intracardiac electrograms have yielded conflicting results between centers, likely due to their complexity and steep learning curve, thus limiting the proper assessment of its clinical impact.

The main purpose of this trial was to assess the impact of online training on the identification of AF driver sites where ablation terminated persistent AF, through a standardized training program. Extending this concept to mobile health was defined as a secondary objective.

An online database of panoramic AF movies was generated from a multicenter registry of patients in whom targeted ablation terminated non-paroxysmal AF, using a freely available method (Kuklik et al–method A) and a commercial one (RhythmView–method B). Cardiology Fellows naive to AF mapping were enrolled and randomized to training vs no training (control). All participants evaluated an initial set of movies to identify sites of AF termination. Participants randomized to training evaluated a second set of movies in which they received feedback on their answers. Both groups re-evaluated the initial set to assess the impact of training. This concept was then migrated to a smartphone application (App).

12 individuals (median age of 30 years (IQR 28–32), 6 females) read 480 AF maps. Baseline identification of AF termination sites by ablation was poor (40%±12% vs 42%±11%, P = 0.78), but similar for both mapping methods (P = 0.68). Training improved accuracy for both methods A (P = 0.001) and B (p = 0.012); whereas controls showed no change in accuracy (P = NS). The Smartphone App accessed AF maps from multiple systems on the cloud to recreate this training environment.

Digital online training improved interpretation of panoramic AF maps in previously inexperienced clinicians. Combining online clinical data, smartphone apps and other digital resources provides a powerful, scalable approach for training in novel techniques in electrophysiology.

Partial Text

Portable diagnostic devices and mobile health (mHealth) are powerful technologies in healthcare[1,2]. Together, they have altered the paradigm in which students and healthcare providers train and acquire new competencies[1,3–5],[6], with a generally positive impact in education and practice[6,7], and are likely to become a dominant mode of clinical training. Nevertheless, few mHealth apps or e-learning platforms have been applied to clinical electrophysiology (EP), with existing tools mostly relating to heart monitoring systems[8], ECGs and medical calculators[9,10].

The main findings in our study were that: (1) graded systematic exposure to an online automated webinar improves identification of AF sources on panoramic AF maps. Nevertheless, performance on this complex task remained suboptimal, suggesting that a longer training curve is needed for optimal clinical performance; (2) Videogame exposure, but no other characteristic, was associated with higher map reading accuracy; (3) Migration of this training paradigm to a Smartphone App is feasible. Our project thus demonstrates the feasibility of several novel digital strategies to improve complex but clinically relevant visual tasks in electrophysiology. This includes the application of downloadable mapping software to compute complex AF movies from de-identified clinical electrograms, to visualize these maps to automate training, and to migrate this digital strategy to a smartphone for mobile health applications. With further development, these and similar strategies could become part of standardized training platforms in EP.

In this proof-of-concept randomized trial, enrollment was limited to a small number of participants. This was mitigated, in part, by analyzing many maps, and the fact that are our results are intuitively correct. However, these results require validation in a larger trial, with different datasets and potentially more diverse mapping systems.

In this randomized-controlled trial we showed that automated structured training via an online webinar improved diagnostic skills on a challenging visual task, when compared to no training–interpreting complex panoramic AF maps to identify potential drivers or sources where ablation had been shown to terminate AF. Training improved accuracy for two AF mapping modalities (both a freely available and a commercial one).




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