Bogle Brittany M, Asimos Andrew W, Rosamond Wayne D
From the Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill (B.M.B., W.D.R.); and Department of Emergency Medicine, Carolinas Healthcare System, Charlotte, NC (A.W.A.).
Stroke. 2017 Oct;48(10):2827-2835. doi: 10.1161/STROKEAHA.117.017905. Epub 2017 Sep 15.
The Severity-Based Stroke Triage Algorithm for Emergency Medical Services endorses routing patients with suspected large vessel occlusion acute ischemic strokes directly to endovascular stroke centers (ESCs). We sought to evaluate different specifications of this algorithm within a region.
We developed a discrete event simulation environment to model patients with suspected stroke transported according to algorithm specifications, which varied by stroke severity screen and permissible additional transport time for routing patients to ESCs. We simulated King County, Washington, and Mecklenburg County, North Carolina, distributing patients geographically into census tracts. Transport time to the nearest hospital and ESC was estimated using traffic-based travel times. We assessed undertriage, overtriage, transport time, and the number-needed-to-route, defined as the number of patients enduring additional transport to route one large vessel occlusion patient to an ESC.
Undertriage was higher and overtriage was lower in King County compared with Mecklenburg County for each specification. Overtriage variation was primarily driven by screen (eg, 13%-55% in Mecklenburg County and 10%-40% in King County). Transportation time specifications beyond 20 minutes increased overtriage and decreased undertriage in King County but not Mecklenburg County. A low- versus high-specificity screen routed 3.7× more patients to ESCs. Emergency medical services spent nearly twice the time routing patients to ESCs in King County compared with Mecklenburg County.
Our results demonstrate how discrete event simulation can facilitate informed decision making to optimize emergency medical services stroke severity-based triage algorithms. This is the first step toward developing a mature simulation to predict patient outcomes.
紧急医疗服务的基于严重程度的卒中分诊算法支持将疑似大血管闭塞急性缺血性卒中患者直接送往血管内卒中中心(ESC)。我们试图评估该算法在一个地区内的不同规格。
我们开发了一个离散事件模拟环境,以根据算法规格对疑似卒中患者的转运进行建模,这些规格因卒中严重程度筛查和将患者送往ESC的允许额外转运时间而异。我们模拟了华盛顿州金县和北卡罗来纳州梅克伦堡县,将患者按地理区域分布到普查区。使用基于交通的出行时间估计到最近医院和ESC的转运时间。我们评估了分诊不足、分诊过度、转运时间以及为将一名大血管闭塞患者送往ESC而需要额外转运的患者数量(即“需转运人数”)。
对于每种规格,金县的分诊不足率高于梅克伦堡县,分诊过度率低于梅克伦堡县。分诊过度的差异主要由筛查驱动(例如,梅克伦堡县为13%-55%,金县为10%-40%)。金县超过20分钟的转运时间规格增加了分诊过度率并降低了分诊不足率,但梅克伦堡县并非如此。低特异性筛查与高特异性筛查相比,送往ESC的患者数量多出3.7倍。与梅克伦堡县相比,金县的紧急医疗服务机构将患者送往ESC的时间几乎多出一倍。
我们的结果表明离散事件模拟如何能够促进明智的决策制定,以优化基于卒中严重程度的紧急医疗服务分诊算法。这是朝着开发成熟模拟以预测患者结局迈出的第一步。