Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada.
Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, ON, Canada.
Eur J Trauma Emerg Surg. 2024 Apr;50(2):497-504. doi: 10.1007/s00068-023-02381-y. Epub 2023 Nov 18.
The STAT (Safety Threats and Adverse Events in Trauma) taxonomy was developed through expert consensus, and groups 65 identified trauma resuscitation adverse events (AEs) into nine distinct categories. It provides a framework for standardized analysis of trauma resuscitations and creates a foundation for targeted quality improvement and patient safety initiatives. This study aims to evaluate the reliability of the STAT taxonomy in identifying AEs during video-recorded trauma resuscitations.
High-definition audiovisual data from 30 trauma resuscitations were reviewed. Videos were assessed and scored by four independent reviewers (two trainees and two staff). The STAT taxonomy was used to identify AEs based on binary responses: yes and no. Inter-rater reliability was calculated using Gwet's AC1. The frequencies of AEs were tallied and reported as counts and percentages.
The most common AEs identified in the videos were failure to measure temperature (86.7%) and inadequate personal protective equipment (86.7%), followed by inability to use closed-loop communication (76.7%). The agreement on all AEs between reviewers was 0.94 (95% CI: 0.93-0.95). The Gwet's AC1 agreement across the 9 AE categories was paramedic handover (0.82), airway and breathing (0.99), circulation (0.95), assessment of injuries (0.91), management of injuries (0.96), procedure-related (0.97), patient monitoring and IV access (0.99), disposition (0.98), team communication and dynamics (0.87).
The STAT taxonomy demonstrated excellent inter-rater reliability between reviewers and can be used to identify AEs in video-recorded trauma resuscitations. These results provide a foundation for adapting video review to objectively quantify and assess AEs in the trauma bay.
STAT（创伤中的安全威胁和不良事件）分类法是通过专家共识制定的，将 65 种确定的创伤复苏不良事件（AE）分为九个不同类别。它为标准化分析创伤复苏提供了框架，并为有针对性的质量改进和患者安全计划奠定了基础。本研究旨在评估 STAT 分类法在识别视频记录的创伤复苏期间不良事件的可靠性。
对 30 例创伤复苏的高清视听数据进行了回顾。视频由四位独立评审员（两名受训者和两名工作人员）进行评估和评分。STAT 分类法用于根据二进制反应（是和否）识别 AE。使用 Gwet 的 AC1 计算组内可靠性。不良事件的频率以计数和百分比报告。
视频中最常见的 AE 是未能测量体温（86.7%）和个人防护设备不足（86.7%），其次是无法使用闭环沟通（76.7%）。评审员对所有 AE 的一致性为 0.94（95%CI：0.93-0.95）。9 种 AE 类别中 Gwet 的 AC1 一致性为护理人员交接（0.82）、气道和呼吸（0.99）、循环（0.95）、损伤评估（0.91）、损伤管理（0.96）、程序相关（0.97）、患者监测和 IV 通道（0.99）、处置（0.98）、团队沟通和动态（0.87）。
STAT 分类法在评审员之间具有出色的组内可靠性，可用于识别视频记录的创伤复苏中的 AE。这些结果为适应视频审查提供了基础，以便在创伤室中客观地量化和评估 AE。
