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在线网络研讨会培训以分析复杂的心房颤动图谱:一项随机试验。

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

机构信息

Department of Cardiology, Centro Hospitalar de Vila Nova de Gaia/Espinho, Gaia, Portugal.

Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, California, United States of America.

出版信息

PLoS One. 2019 Jul 3;14(7):e0217988. doi: 10.1371/journal.pone.0217988. eCollection 2019.

Abstract

BACKGROUND

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.

OBJECTIVES

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.

METHODS

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).

RESULTS

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.

CONCLUSION

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.

摘要

背景

最近开发了一些特定的工具来绘制心房颤动(AF)图谱并帮助指导消融。然而,当这些工具在临床实践中使用时,从多极心内电图生成的全景 AF 图谱在不同中心之间产生了相互矛盾的结果,这可能是由于其复杂性和陡峭的学习曲线,从而限制了对其临床影响的正确评估。

目的

本试验的主要目的是通过标准化的培训计划,评估在线培训对识别消融终止持续性 AF 的 AF 驱动部位的影响。将这一概念扩展到移动健康被定义为次要目标。

方法

使用一种免费的方法(Kuklik 等人的方法 A)和一种商业方法(RhythmView 方法 B),从一个多中心患者登记处生成了一个全景 AF 电影的在线数据库,该登记处中靶向消融终止了非阵发性 AF。招募了对 AF 图谱一无所知的心脏病学研究员,并将其随机分为培训组和对照组(无培训)。所有参与者都评估了一组初始电影来识别 AF 终止部位。随机分配到培训组的参与者评估了第二组电影,其中他们收到了对其答案的反馈。两组都重新评估了初始组,以评估培训的影响。然后,将这一概念迁移到智能手机应用程序(App)。

结果

12 名个体(中位数年龄为 30 岁(IQR 28-32),6 名女性)阅读了 480 个 AF 图谱。消融终止 AF 部位的基线识别率较差(40%±12%与 42%±11%,P=0.78),但两种图谱方法的识别率相似(P=0.68)。培训提高了两种方法(A 和 B)的准确性(P=0.001 和 P=0.012);而对照组的准确性没有变化(P=NS)。智能手机 App 通过云访问来自多个系统的 AF 地图,以重新创建这种培训环境。

结论

数字在线培训提高了以前没有经验的临床医生对全景 AF 图谱的解读能力。结合在线临床数据、智能手机应用程序和其他数字资源,为电生理新技术的培训提供了一种强大、可扩展的方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4703/6609132/7a15b3153428/pone.0217988.g001.jpg

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