Background

Out-of-hospital cardiac arrest (OHCA) data debriefing and clinical research often require the retrospective analysis of large datasets containing defibrillator files from different vendors and clinical annotations by the emergency medical services.

Aim

To introduce and evaluate a methodology to automatically extract cardiopulmonary resuscitation (CPR) quality data in a uniform and systematic way from OHCA datasets from multiple heterogeneous sources.

Methods

A dataset of 2236 OHCA cases from multiple defibrillator models and manufacturers was analyzed. Chest compressions were automatically identified using the thoracic impedance and compression depth signals. Device event time-stamps and clinical annotations were used to set the start and end of the analysis interval, and to identify periods with spontaneous circulation. A manual audit of the automatic annotations was conducted and used as gold standard. Chest compression fraction (CCF), rate (CCR) and interruption ratio were computed as CPR quality variables. The unsigned error between the automated procedure and the gold standard was calculated.

Results

Full-episode median errors below 2% in CCF, 1 min⁻¹ in CCR, and 1.5% in interruption ratio, were measured for all signals and devices. The proportion of cases with large errors ($>$10% in CCF and interruption ratio, and $>$10 min⁻¹ in CCR) was below 10%. Errors were lower for shorter sub-intervals of interest, like the airway insertion interval.

Conclusions

An automated methodology was validated to accurately compute CPR metrics in large and heterogeneous OHCA datasets. Automated processing of defibrillator files and the associated clinical annotations enables the aggregation and analysis of CPR data from multiple sources.

中文翻译：

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在大型和异质心脏骤停数据集中分析心肺复苏质量的方法和框架

背景

院外心脏骤停 (OHCA) 数据汇报和临床研究通常需要对包含来自不同供应商的除颤器文件和紧急医疗服务的临床注释的大型数据集进行回顾性分析。

目的

介绍和评估一种方法，以统一和系统的方式从来自多个异构来源的 OHCA 数据集中自动提取心肺复苏 (CPR) 质量数据。

方法

分析了来自多个除颤器型号和制造商的 2236 个 OHCA 病例的数据集。使用胸部阻抗和按压深度信号自动识别胸部按压。设备事件时间戳和临床注释用于设置分析间隔的开始和结束，并识别具有自主循环的时期。对自动注释进行了手动审核，并将其用作黄金标准。胸外按压分数 (CCF)、速率 (CCR) 和中断率被计算为 CPR 质量变量。计算了自动化程序和黄金标准之间的无符号误差。

结果

对于所有信号和设备，CCF 中低于 2%、CCR中低于 1 min ^-1和中断率低于1.5% 的完整剧集中值误差均被测量。大错误案例的比例（$>$10% 的 CCF 和中断率，以及 $>$10 min ^-1 (CCR) 低于 10%。对于较短的感兴趣的子间隔，如气道插入间隔，误差较低。

结论

验证了一种自动化方法，可以在大型异构 OHCA 数据集中准确计算 CPR 指标。除颤器文件和相关临床注释的自动处理能够聚合和分析来自多个来源的 CPR 数据。