1.Department of Surgery, Brigham and Women's Hospital,Boston, Massachusetts,USA.
3.Department of Surgery, Division of Acute Care Surgery, University of Michigan,Ann Arbor, Michigan,USA.
Prehosp Disaster Med. 2019 Aug;34(4):356-362. doi: 10.1017/S1049023X19004515. Epub 2019 Jul 19.
With the increasing availability of vehicle telemetry technology, there is great potential for Advanced Automatic Collision Notification (AACN) systems to improve trauma outcomes by detecting patients at-risk for severe injury and facilitating early transport to trauma centers.
National Automotive Sampling System Crashworthiness Data System (NASS-CDS) data from 1999-2013 were used to construct a logistic regression model (injury severity prediction [ISP] model) predicting the probability that one or more occupants in planar, non-rollover motor vehicle collisions (MVCs) would have Injury Severity Score (ISS) 15+ injuries. Variables included principal direction of force (PDOF), change in velocity (Delta-V), multiple impacts, presence of any older occupant (≥55 years old), presence of any female occupant, presence of right-sided passenger, belt use, and vehicle type. The model was validated using medical records and 2008-2011 crash data from AACN-enabled Michigan (USA) vehicles identified from OnStar (OnStar Corporation; General Motors; Detroit, Michigan USA) records. To compare the ISP to previously established protocols, a literature search was performed to determine the sensitivity and specificity of first responder identification of ISS 15+ for MVC occupants.
The study population included 924 occupants in 836 crash events. The ISP model had a sensitivity of 72.7% (95% Confidence Interval [CI] 41%-91%) and specificity of 93% (95% CI 92%-95%) for identifying ISS 15+ occupants injured in planar MVCs. The current standard 2006 Field Triage Decision Scheme (FTDS) was 56%-66% sensitive and 75%-88% specific in identifying ISS 15+ patients.
The ISP algorithm comparably is more sensitive and more specific than current field triage in identifying MVC patients at-risk for ISS 15+ injuries. This real-world field study shows telemetry data transmitted before dispatch of emergency medical systems can be helpful to quickly identify patients who require urgent transfer to trauma centers.
随着车辆遥测技术的日益普及，高级自动碰撞通知（AACN）系统通过检测有严重受伤风险的患者并促进其尽早送往创伤中心，有可能改善创伤结局。
使用 1999 年至 2013 年国家汽车抽样系统碰撞数据系统（NASS-CDS）的数据构建了一个逻辑回归模型（损伤严重程度预测[ISP]模型），预测平面、非翻滚机动车碰撞（MVC）中一个或多个乘员发生损伤严重程度评分（ISS）≥15 损伤的概率。变量包括主要力方向（PDOF）、速度变化（Delta-V）、多次撞击、是否有任何老年乘员（≥55 岁）、是否有任何女性乘员、是否有右侧乘客、安全带使用情况和车辆类型。该模型使用医疗记录和来自 OnStar（OnStar 公司；通用汽车公司；底特律，密歇根州美国）记录的 2008 年至 2011 年 AACN 启用密歇根州（美国）车辆的碰撞数据进行了验证。为了将 ISP 与先前建立的协议进行比较，进行了文献检索，以确定急救人员识别 MVC 乘员 ISS≥15 的灵敏度和特异性。
研究人群包括 836 起碰撞事件中的 924 名乘员。ISP 模型对平面 MVC 中受伤的 ISS≥15 乘员的识别灵敏度为 72.7%（95%置信区间[CI]41%-91%），特异性为 93%（95%CI92%-95%）。目前的标准 2006 年现场分诊决策方案（FTDS）在识别 ISS≥15 患者方面的灵敏度为 56%-66%，特异性为 75%-88%。
与当前的现场分诊相比，ISP 算法在识别 MVC 患者中对 ISS≥15 损伤风险的患者更敏感和更特异。这项真实世界的现场研究表明，在调度紧急医疗系统之前传输遥测数据有助于快速识别需要紧急转送至创伤中心的患者。
