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1000
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Abstract/Summary
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Background and aim!#!The extent of penumbra tissue and outcome in stroke patients depend on the collateral cranial vasculature. To provide optimal individualized care for stroke patients in the emergency room setting we investigated the predictive capability of a stringent evaluation of the collateral vessels in ischemic stroke on clinical outcome and infarct size.!##!Methods!#!We retrospectively studied uniform clinical and radiological data of 686 consecutive patients admitted to the emergency department with suspected acute ischemic stroke. Cranial collateral vasculature status was graded using the initial CT-angiography. Outcome was measured by mRS, NIHSS and final infarct size at hospital discharge. All data were used to build a linear regression model to predict the patients´ outcome.!##!Results!#!Univariate and multivariate analyses showed significant effects of the whole brain collateral vessel score on all outcome variables. Atherosclerosis and piale collateral status were associated with the final infarct volume (FIV). Atherosclerosis and age were associated with the NIHSS at discharge. The presence of atherosclerosis, glucose level on admission and age were associated with the mRS at discharge. The multivariate models were able to predict 29% of the variance of the mRS at discharge, 24% of the variance in FIV and 17% of the variance of the NIHSS at discharge. The whole brain collateral status and the presence of atherosclerosis were the most relevant predictors for the clinical and radiological outcome.!##!Conclusion!#!The whole brain collateral vasculature status is clearly associated with clinical and radiological outcome but in a multivariate model seems not sufficiently predictive for FIV, mRS and NIHSS outcome at discharge in non-preselected patients admitted to the emergency department with ischemic stroke.
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1000
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Hinweis
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DeepGreen-ID: 1b83170fd7fd4ca594ade2649e619104 ; metadata provieded by: DeepGreen (https://www.oa-deepgreen.de/api/v1/), LIVIVO search scope life sciences (http://z3950.zbmed.de:6210/livivo), Crossref Unified Resource API (https://api.crossref.org/swagger-ui/index.html), to.science.api (https://frl.publisso.de/), ZDB JSON-API (beta) (https://zeitschriftendatenbank.de/api/), lobid - Dateninfrastruktur für Bibliotheken (https://lobid.org/resources/search)
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