Estimate of mortality reduction with implementation of advanced automatic collision notification

Objective: Advanced Automatic Collision Notification (AACN) is a system on a motor vehicle that notifies a public safety answering point (PSAP), either directly or through a third party, that the vehicle has had a crash. AACN systems enable earlier notification of a motor vehicle crash and provide an injury prediction that can help dispatchers and first responders make better decisions about how and where to transport the patient, thus getting the patient to definitive care sooner. The purposes of the current research are to identify the target population that could benefit from AACN, and to develop a reasonable estimate range of potential lives saved with implementation of AACN within the vehicle fleet.
Methods: Data from the Fatality Analysis Reporting System (FARS) years 2009–2015 and National Automotive Sampling System–Crashworthiness Data System (NASS-CDS) years 2000–2015 were obtained. FARS data were used to determine absolute estimates of the target population who may receive benefit from AACN. These estimates accounted for a number of factors, such as whether a fatal occupant had nearby access to a trauma center and also was correctly identified by the injury severity prediction algorithm as having a “high probability of severe injury.” NASS-CDS data were used to provide relative comparisons among subsets of the population. Specifically, relative survival rate ratios between occupants treated at trauma centers versus at non-trauma centers were determined using the nonparametric Kaplan–Meier estimator. Finally, the fatality reduction rate associated with trauma center care was combined with the previously published fatality reduction rate for faster notification time to develop a range for possible lives saved.
Results: Two relevant target populations were identified. A larger subset of 6893 fatalities can benefit only from earlier notification associated with AACN. A smaller subgroup of between 1495 and 2330 fatalities can benefit from both earlier notification and change in treatment destination (i.e., non-trauma center to trauma center). A Kaplan–Meier life curve and a multiple proportional hazard model were used to predict the benefits associated with transport to a trauma center. The resulting range for potential lives saved annually was 360 to 721.
Conclusions: This analysis provides the estimates of lives that could potentially be saved with full implementation of AACN and universal cell coverage availability. This represents a fatality reduction of approximately 1.6% to 3.3% per year, and more than double the lives saved by earlier notification alone. In conclusion, AACN is a postcrash technology with a promising potential for safety benefit. AACN is therefore a key component of integrated safety systems that aim to protect occupants across the entire crash spectrum.

研究目标：高级自动碰撞通知系统（Advanced Automatic Collision Notification, AACN）是一种车载系统，可直接或通过第三方向公共安全应答点（Public Safety Answering Point, PSAP）通报车辆发生碰撞的事件。AACN系统可实现机动车碰撞事件的更早通报，并提供损伤预测结果，辅助调度人员与急救人员优化患者转运方案与转运地点的选择，从而让患者更快获得确定性救治。本研究旨在识别可从AACN系统获益的目标人群，并估算在机动车全车队中部署AACN系统后，潜在可挽救生命的合理区间范围。
研究方法：本研究获取了2009-2015年的死亡分析报告系统（Fatality Analysis Reporting System, FARS）数据集，以及2000-2015年的全国汽车抽样系统-碰撞安全性数据系统（National Automotive Sampling System–Crashworthiness Data System, NASS-CDS）数据集。利用FARS数据集确定可从AACN系统获益的目标人群的绝对估算值，该估算纳入了多项影响因素，例如致命性乘员是否可就近抵达创伤中心（trauma center），以及其是否被损伤严重程度预测算法准确识别为“重度损伤高风险人群”。借助NASS-CDS数据集开展目标人群亚组间的相对比较分析：具体而言，采用非参数Kaplan-Meier估计法（Kaplan–Meier estimator）计算了在创伤中心与非创伤中心接受救治的乘员的相对生存率比值。最后，将创伤中心救治相关的死亡率降低率与已发表的“缩短通报时间可降低死亡率”的研究结果相结合，得到潜在可挽救生命的区间范围。
研究结果：本研究共识别出两类相关目标人群。规模较大的亚组包含6893例致命性碰撞事件，仅可从AACN系统带来的更早碰撞通报中获益；规模较小的亚组包含1495至2330例致命性碰撞事件，可同时从更早碰撞通报与救治地点调整（即从非创伤中心转运至创伤中心）中获益。本研究采用Kaplan-Meier生存曲线与多因素比例风险模型（proportional hazard model）预测了转运至创伤中心相关的获益。最终估算得到每年潜在可挽救的生命数区间为360至721例。
研究结论：本分析提供了“在全机动车车队部署AACN系统并实现全域移动通信覆盖”的场景下，潜在可挽救生命的估算值。该结果对应每年死亡率降低约1.6%至3.3%，且可挽救的生命数是仅依靠更早碰撞通报所能挽救生命数的两倍以上。综上，AACN是一项碰撞后安全技术，具备可观的安全获益潜力，因此可作为集成式安全系统的核心组成部分，旨在为全碰撞场景下的机动车乘员提供防护。