Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada.
Peel Regional Paramedic Services, Brampton, ON, Canada.
Resuscitation. 2021 May;162:120-127. doi: 10.1016/j.resuscitation.2021.02.028. Epub 2021 Feb 22.
Drone-delivered defibrillators have the potential to significantly reduce response time for out-of-hospital cardiac arrest (OHCA). However, optimal policies for the dispatch of such drones are not yet known. We sought to develop dispatch rules for a network of defibrillator-carrying drones.
We identified all suspected OHCAs in Peel Region, Ontario, Canada from Jan. 2015 to Dec. 2019. We developed drone dispatch rules based on the difference between a predicted ambulance response time to a calculated drone response time for each OHCA. Ambulance response times were predicted using linear regression and neural network models, while drone response times were calculated using drone specifications from recent pilot studies and the literature. We evaluated the dispatch rules based on response time performance and dispatch decisions, comparing them to two baseline policies of never dispatching and always dispatching drones.
A total of 3573 suspected OHCAs were included in the study with median and mean historical ambulance response times of 5.8 and 6.2 min. All machine learning-based dispatch rules significantly reduced the median response time to 3.9 min and mean response time to 4.1-4.2 min (all P < 0.001) and were non-inferior to universally dispatching drones (all P < 0.001) while reducing the number of drone flights by up to 30%. Dispatch rules with more drone flights achieved higher sensitivity but lower specificity and accuracy.
Machine learning-based dispatch rules for drone-delivered defibrillators can achieve similar response time reductions as universal drone dispatch while substantially reducing the number of trips.
无人机配送除颤器有可能显著缩短院外心脏骤停（OHCA）的反应时间。然而，对于此类无人机的最佳调度策略尚不清楚。我们试图为携带除颤器的无人机网络制定调度规则。
我们从 2015 年 1 月至 2019 年 12 月确定了安大略省皮尔地区所有疑似 OHCA。我们根据每个 OHCA 的预测救护车反应时间与计算的无人机反应时间之间的差异制定了无人机调度规则。使用线性回归和神经网络模型预测救护车反应时间，使用最近的试点研究和文献中的无人机规格计算无人机反应时间。我们根据反应时间性能和调度决策评估调度规则，将其与从不调度和始终调度无人机的两个基线策略进行比较。
共纳入 3573 例疑似 OHCA，中位数和平均历史救护车反应时间分别为 5.8 分钟和 6.2 分钟。所有基于机器学习的调度规则均显著将中位数反应时间缩短至 3.9 分钟，平均反应时间缩短至 4.1-4.2 分钟（均 P < 0.001），且与普遍调度无人机相当（均 P < 0.001），同时减少了多达 30%的无人机飞行次数。具有更多无人机飞行次数的调度规则具有更高的敏感性，但特异性和准确性较低。
基于机器学习的无人机配送除颤器调度规则可以实现与普遍无人机调度相似的反应时间减少，同时大大减少飞行次数。
