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利用生理模型优化急诊脑卒中转运策略。

Optimizing Emergency Stroke Transport Strategies Using Physiological Models.

机构信息

Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics (D.A.P., P.J.M.), University of North Carolina, Chapel Hill.

Departments of Neurology (D.P., J.C.), Dell Medical School, Mulva Clinic for the Neurosciences and Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin.

出版信息

Stroke. 2021 Dec;52(12):4010-4020. doi: 10.1161/STROKEAHA.120.031633. Epub 2021 Aug 19.

Abstract

BACKGROUND AND PURPOSE

The criteria for choosing between drip and ship and mothership transport strategies in emergency stroke care is widely debated. Although existing data-driven probability models can inform transport decision-making at an epidemiological level, we propose a novel mathematical, physiologically derived framework that provides insight into how patient characteristics underlying infarct core growth influence these decisions.

METHODS

We represent the physiology of time-dependent infarct core growth within an ischemic penumbra as an exponential function with consideration to rate-determining collateral blood flow. Monte Carlo methods generate distributions of infarct core volumes, which are translated to distributions of 90-day modified Rankin Scale scores. We apply the model to a stroke network that serves rural Bastrop County and urban Travis County by simulating transport strategies from thousands of potential patient pickup locations. In every pickup location, the simulation yields a distribution of outcomes corresponding to each transport strategy. A 2-sample Kolmogorov-Smirnov test and Student test determine which transport strategy provides a significantly better probability of a good outcome for a given pickup location in each respective county (<0.01).

RESULTS

In Travis County, drip and ship provides significantly better probabilities of a good outcome in 24.0% of the pickup locations, while 59.8% favor mothership. In Bastrop County, 11.3% of the pickup locations favor drip and ship, while only 7.1% favor mothership. The remaining pickup locations in each county are not statistically significant in either direction. We also reveal how differing rates of infarct core growth, the application of bypass policies, and the use of large vessel occlusion field tests impact these results.

CONCLUSIONS

Modeling stroke physiology enables the use of clinically relevant metrics for determining comparative significance between drip and ship and mothership in a given geography. This formalism can help understand and inform emergency medical service transport decision-making, as well as regional bypass policies.

摘要

背景与目的

在急救卒中护理中,选择点滴输送和母舰运输策略的标准存在广泛争议。尽管现有的基于数据的概率模型可以在流行病学层面为转运决策提供信息,但我们提出了一种新颖的数学模型,该模型基于梗死核心增长的患者特征,深入了解这些决策。

方法

我们将缺血半影区时间依赖性梗死核心增长的生理学表示为一个考虑到决定速率的侧支血流的指数函数。蒙特卡罗方法生成梗死核心体积的分布,这些分布被转化为 90 天改良 Rankin 量表评分的分布。我们应用该模型模拟了服务于农村巴斯特罗普县和城市特拉维斯县的卒中网络,从数千个潜在的患者接送地点模拟转运策略。在每个接送地点,模拟都会产生与每种转运策略相对应的结果分布。双样本 Kolmogorov-Smirnov 检验和学生 t 检验确定了在每个县的每个接送地点,哪种转运策略提供了更好的良好结局的概率(<0.01)。

结果

在特拉维斯县,点滴输送和母舰在 24.0%的接送地点提供了更好的良好结局的概率,而 59.8%的地点倾向于母舰。在巴斯特罗普县,11.3%的接送地点倾向于点滴输送和母舰,而只有 7.1%的地点倾向于母舰。每个县的其余接送地点在这两个方向都没有统计学意义。我们还揭示了梗死核心增长速度、旁路策略的应用以及大血管闭塞现场测试的使用如何影响这些结果。

结论

模拟卒中生理学可以使用临床上相关的指标来确定在给定地理区域内点滴输送和母舰与母舰之间的比较意义。这种形式主义可以帮助理解和为急救医疗服务转运决策以及区域旁路政策提供信息。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9ef8/8607917/0002d223ac6e/str-52-4010-g001.jpg

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