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Optimizing Emergency Stroke Transport Strategies Using Physiological Models
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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Lippincott Williams & Wilkins
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8607917/ https://www.ncbi.nlm.nih.gov/pubmed/34407639 http://dx.doi.org/10.1161/STROKEAHA.120.031633 |
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author | Paydarfar, Daniel A. Paydarfar, David Mucha, Peter J. Chang, Joshua |
author_facet | Paydarfar, Daniel A. Paydarfar, David Mucha, Peter J. Chang, Joshua |
author_sort | Paydarfar, Daniel A. |
collection | PubMed |
description | 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 t test determine which transport strategy provides a significantly better probability of a good outcome for a given pickup location in each respective county (P<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. |
format | Online Article Text |
id | pubmed-8607917 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Lippincott Williams & Wilkins |
record_format | MEDLINE/PubMed |
spelling | pubmed-86079172021-11-22 Optimizing Emergency Stroke Transport Strategies Using Physiological Models Paydarfar, Daniel A. Paydarfar, David Mucha, Peter J. Chang, Joshua Stroke Original Contributions 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 t test determine which transport strategy provides a significantly better probability of a good outcome for a given pickup location in each respective county (P<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. Lippincott Williams & Wilkins 2021-08-19 2021-12 /pmc/articles/PMC8607917/ /pubmed/34407639 http://dx.doi.org/10.1161/STROKEAHA.120.031633 Text en © 2021 The Authors. https://creativecommons.org/licenses/by-nc-nd/4.0/Stroke is published on behalf of the American Heart Association, Inc., by Wolters Kluwer Health, Inc. This is an open access article under the terms of the Creative Commons Attribution Non-Commercial-NoDerivs (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use, distribution, and reproduction in any medium, provided that the original work is properly cited, the use is noncommercial, and no modifications or adaptations are made. This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections. |
spellingShingle | Original Contributions Paydarfar, Daniel A. Paydarfar, David Mucha, Peter J. Chang, Joshua Optimizing Emergency Stroke Transport Strategies Using Physiological Models |
title | Optimizing Emergency Stroke Transport Strategies Using Physiological Models |
title_full | Optimizing Emergency Stroke Transport Strategies Using Physiological Models |
title_fullStr | Optimizing Emergency Stroke Transport Strategies Using Physiological Models |
title_full_unstemmed | Optimizing Emergency Stroke Transport Strategies Using Physiological Models |
title_short | Optimizing Emergency Stroke Transport Strategies Using Physiological Models |
title_sort | optimizing emergency stroke transport strategies using physiological models |
topic | Original Contributions |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8607917/ https://www.ncbi.nlm.nih.gov/pubmed/34407639 http://dx.doi.org/10.1161/STROKEAHA.120.031633 |
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