Pierce Ayal Z, Couperus Cody, Parker Jordan, LaRocco Allison, Mazzeffi Michael, Morris Nicholas A
Division of Pulmonary, Critical Care, & Sleep Medicine, University of Maryland, Baltimore, MD, USA.
Department of Anesthesiology, University of Virginia, Charlottesville, VA, USA.
Resusc Plus. 2025 May 22;24:100989. doi: 10.1016/j.resplu.2025.100989. eCollection 2025 Jul.
Accurate measurement of CPR quality metrics is critical for improving cardiac arrest outcomes. Impedance based automated devices have demonstrated limitations. Zoll RescueNet CaseReview, rather, uses accelerometry to analyze chest compressions and automatically provides code feedback, including CPR pause number, length, and chest compression fraction. However, the reliability of these automated measurements compared to manual physician review remains uncertain.
We conducted a retrospective observational cohort study at a tertiary academic medical center, analyzing 212 in-hospital cardiac arrest cases recorded between July 1, 2023, and July 1, 2024. The study compared CPR metrics generated by the Zoll RescueNet CaseReview algorithm to manual physician review of raw defibrillator data, focusing on pause durations and chest compression fraction (CCF) using Bland-Altman plots.
Bland-Altman plots indicated overestimation of individual pause times (mean difference 4.00 s), max pause time per arrest (mean difference 24.57 s) total pause time per arrest (mean difference 0.73 min), and average number of pauses per arrest, with corresponding underestimation of CCF (mean difference 8.33%). Substantial variability was present for all variables with increased disagreement for longer pause times.
The Zoll RescueNet CaseReview algorithm estimates longer CPR pause durations than manual physician review, thereby lowering the chest compression fraction estimate. These findings support manual review of raw data and improved algorithmic detection of compressions to ensure feedback to resuscitation teams is reliable.
准确测量心肺复苏质量指标对于改善心脏骤停结局至关重要。基于阻抗的自动化设备已显示出局限性。相反，卓尔救援网络病例回顾（Zoll RescueNet CaseReview）使用加速度计分析胸外按压并自动提供代码反馈，包括心肺复苏暂停次数、时长和胸外按压分数。然而，与医生手动审查相比，这些自动测量的可靠性仍不确定。
我们在一家三级学术医疗中心进行了一项回顾性观察队列研究，分析了2023年7月1日至2024年7月1日期间记录的212例院内心脏骤停病例。该研究将卓尔救援网络病例回顾算法生成的心肺复苏指标与医生对原始除颤器数据的手动审查进行了比较，使用布兰德-奥特曼图重点关注暂停持续时间和胸外按压分数（CCF）。
布兰德-奥特曼图显示，个体暂停时间（平均差异4.00秒）、每次心脏骤停的最大暂停时间（平均差异24.57秒）、每次心脏骤停的总暂停时间（平均差异0.73分钟）和每次心脏骤停的平均暂停次数被高估，而CCF相应被低估（平均差异8.33%）。所有变量均存在较大变异性，且暂停时间越长，分歧越大。
卓尔救援网络病例回顾算法估计的心肺复苏暂停持续时间比医生手动审查的更长，从而降低了胸外按压分数估计值。这些发现支持对原始数据进行手动审查，并改进算法对按压的检测，以确保复苏团队获得的反馈可靠。
