Leary Marion, McGovern Shaun K, Balian Steve, Abella Benjamin S, Blewer Audrey L
Center for Resuscitation Science and Department of Emergency Medicine, University of Pennsylvania, Philadelphia, PA, United States.
School of Nursing, University of Pennsylvania, Philadelphia, PA, United States.
Front Digit Health. 2020 Feb 28;2:1. doi: 10.3389/fdgth.2020.00001. eCollection 2020.
Guidelines-based cardiopulmonary resuscitation (CPR) during in-hospital cardiac arrest is a significant predictor of survival, yet the quality of healthcare provider (HCP) CPR (e.g., nurses, physicians etc.) has been shown to be poor. Studies have found that providing HCPs with simulated CPR refresher trainings can improve their CPR quality, however, no studies have compared the use of an augmented reality (AR) CPR refresher training with a standard audio-visual (AV) feedback manikin to improve HCP training. In our pilot study, HCPs were randomized to a refresher CPR simulation training with either our AR CPR training application (CPReality) or a standard AV feedback manikin. All subjects completed 2 min of CPR on their respective CPR training modalities, followed by an additional 2 min post-simulation CPR evaluation with no feedback. We hypothesized that the AR CPR training application would confer improved CPR quality defined as chest compression rate and depth compared with the standard AV feedback training. Between January 2019 and May 2019, 100 HCPs were enrolled (50 in the CPReality cohort and 50 in the standard AV manikin cohort). The mean chest compression (CC) rate for all subjects during the intervention was 118 ± 15 cpm, and CC depth was 50 ± 8; post-intervention the CC rate was 120 ± 13 and CC depth was 51 ± 8. The mean CC rate for those trained with CPReality was 121 ± 3 compared with the standard CPR manikin training which was 114 ± 1 cpm ( < 0.006); CC depth was 48 ± 1 mm vs. 52 ± 1 ( = 0.007), respectively. Post-simulation CPR quality with no feedback showed a mean CC rate for the CPReality application at 122 ± 15 cpm compared with the standard CPR manikin at 117 ± 11 cpm ( = 0.09); depth was 49 ± 8 mm vs. 52 ± 8 ( = 0.095), respectively. In the post-survey, 79% of CPReality subjects agreed that the AR application provided a realistic patient presence compared with 59% ( = 0.07) of subjects in the standard CPR manikin cohort. In a randomized trial of an AR CPR training application compared with a standard CPR manikin training, the AR CPR application did not improve the quality of CPR performed during a CPR refresher training compared with the standard training in HCPs. Future studies should investigate the use of this and other digital technologies for CPR training and education.
基于指南的院内心脏骤停期间的心肺复苏（CPR）是生存的重要预测指标，但医疗保健提供者（HCP，如护士、医生等）实施的心肺复苏质量一直被证明较差。研究发现，为HCP提供模拟心肺复苏复习培训可以提高他们的心肺复苏质量，然而，尚无研究比较增强现实（AR）心肺复苏复习培训与标准视听（AV）反馈人体模型在改善HCP培训方面的效果。在我们的初步研究中，HCP被随机分配到使用我们的AR心肺复苏培训应用程序（CPReality）或标准AV反馈人体模型进行心肺复苏复习模拟培训。所有受试者在各自的心肺复苏培训模式下完成2分钟的心肺复苏，随后在无反馈的情况下进行额外2分钟的模拟后心肺复苏评估。我们假设，与标准AV反馈培训相比，AR心肺复苏培训应用程序将带来更高的心肺复苏质量，定义为胸外按压速率和深度。在2019年1月至2019年5月期间，招募了100名HCP（CPReality队列50名，标准AV人体模型队列50名）。干预期间所有受试者的平均胸外按压（CC）速率为118±15次/分钟，CC深度为50±8毫米；干预后CC速率为120±13次/分钟，CC深度为51±8毫米。接受CPReality培训的人员的平均CC速率为121±3次/分钟，而标准心肺复苏人体模型培训的速率为114±1次/分钟（P<0.006）；CC深度分别为48±1毫米和52±1毫米（P = 0.007）。无反馈的模拟后心肺复苏质量显示，CPReality应用程序的平均CC速率为122±15次/分钟，而标准心肺复苏人体模型为117±11次/分钟（P = 0.09）；深度分别为49±8毫米和52±8毫米（P = 0.095）。在调查中，79%的CPReality受试者同意AR应用程序提供了逼真的患者临场感，而标准心肺复苏人体模型队列中的这一比例为59%（P = 0.07）。在一项将AR心肺复苏培训应用程序与标准心肺复苏人体模型培训进行比较的随机试验中，与HCP的标准培训相比，AR心肺复苏应用程序在心肺复苏复习培训期间并未提高所实施的心肺复苏质量。未来的研究应调查这种及其他数字技术在心肺复苏培训和教育中的应用。
