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Optimizing Emergency Stroke Transport Strategies Using Physiological Models.
Paydarfar, Daniel A; Paydarfar, David; Mucha, Peter J; Chang, Joshua.
Afiliação
  • Paydarfar DA; Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics (D.A.P., P.J.M.), University of North Carolina, Chapel Hill.
  • Paydarfar D; 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.
  • Mucha PJ; Carolina Center for Interdisciplinary Applied Mathematics, Department of Mathematics (D.A.P., P.J.M.), University of North Carolina, Chapel Hill.
  • Chang J; 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.
Stroke ; 52(12): 4010-4020, 2021 12.
Article em En | MEDLINE | ID: mdl-34407639
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 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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Transporte de Pacientes / Acidente Vascular Cerebral / Modelos Neurológicos / Modelos Teóricos Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: Stroke Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Transporte de Pacientes / Acidente Vascular Cerebral / Modelos Neurológicos / Modelos Teóricos Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: Stroke Ano de publicação: 2021 Tipo de documento: Article