Early Prediction of Delayed Cerebral Ischemia After Subarachnoid Hemorrhage Development and Validation of a Practical Risk Chart

To develop and validate a risk chart for prediction of delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage based on admission characteristics. For derivation of the risk chart, we studied data from 371 prospectively collected consecutive subarachnoid hemorrhage patients with a confirm...

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Veröffentlicht in:Stroke (1970) 2013-05, Vol.44 (5), p.1288-1294
Hauptverfasser: DE ROOIJ, Nicolien K, GREVING, Jacoba P, RINKEL, Gabriel J. E, FRIJNS, Catharina J. M
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container_end_page 1294
container_issue 5
container_start_page 1288
container_title Stroke (1970)
container_volume 44
creator DE ROOIJ, Nicolien K
GREVING, Jacoba P
RINKEL, Gabriel J. E
FRIJNS, Catharina J. M
description To develop and validate a risk chart for prediction of delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage based on admission characteristics. For derivation of the risk chart, we studied data from 371 prospectively collected consecutive subarachnoid hemorrhage patients with a confirmed aneurysm admitted between 1999 and 2007. For its validation we similarly studied 255 patients admitted between 2007 and 2009. The predictive value of admission characteristics was tested in logistic regression models with delayed cerebral ischemia-related infarction as primary outcome. Procedure-related infarctions were not included. Performance of the models was tested by discrimination and calibration. On the basis of these models, a risk chart was developed for application in clinical practice. The strongest predictors were clinical condition on admission, amount of blood on computed tomography (both cisternal and intraventricular) and age. A model that combined these 4 predictors had an area under the receiver operating characteristic curve of 0.63 (95% confidence interval, 0.57-0.69). This model improved little by including current smoking and hyperglycemia on admission (area under the receiver operating characteristic curve, 0.65; 95% confidence interval, 0.59-0.71). The risk chart predicted risks of delayed cerebral ischemia-related infarction varying from 12% to 61%. Both low risk (40% risk) were predicted in ≈20% of the patients. Validation confirmed that the discriminative ability was adequate (area under the receiver operating characteristic curve, 0.69; 95% confidence interval, 0.61-0.77). Absolute risks of delayed cerebral ischemia-related infarction can be reliably estimated by a simple risk chart that includes clinical condition on admission, amount of blood on computed tomography (both cisternal and intraventricular), and age.
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The strongest predictors were clinical condition on admission, amount of blood on computed tomography (both cisternal and intraventricular) and age. A model that combined these 4 predictors had an area under the receiver operating characteristic curve of 0.63 (95% confidence interval, 0.57-0.69). This model improved little by including current smoking and hyperglycemia on admission (area under the receiver operating characteristic curve, 0.65; 95% confidence interval, 0.59-0.71). The risk chart predicted risks of delayed cerebral ischemia-related infarction varying from 12% to 61%. Both low risk (&lt;20% risk) and high risk (&gt;40% risk) were predicted in ≈20% of the patients. Validation confirmed that the discriminative ability was adequate (area under the receiver operating characteristic curve, 0.69; 95% confidence interval, 0.61-0.77). 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E</creatorcontrib><creatorcontrib>FRIJNS, Catharina J. M</creatorcontrib><title>Early Prediction of Delayed Cerebral Ischemia After Subarachnoid Hemorrhage Development and Validation of a Practical Risk Chart</title><title>Stroke (1970)</title><addtitle>Stroke</addtitle><description>To develop and validate a risk chart for prediction of delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage based on admission characteristics. For derivation of the risk chart, we studied data from 371 prospectively collected consecutive subarachnoid hemorrhage patients with a confirmed aneurysm admitted between 1999 and 2007. For its validation we similarly studied 255 patients admitted between 2007 and 2009. The predictive value of admission characteristics was tested in logistic regression models with delayed cerebral ischemia-related infarction as primary outcome. Procedure-related infarctions were not included. Performance of the models was tested by discrimination and calibration. On the basis of these models, a risk chart was developed for application in clinical practice. The strongest predictors were clinical condition on admission, amount of blood on computed tomography (both cisternal and intraventricular) and age. A model that combined these 4 predictors had an area under the receiver operating characteristic curve of 0.63 (95% confidence interval, 0.57-0.69). This model improved little by including current smoking and hyperglycemia on admission (area under the receiver operating characteristic curve, 0.65; 95% confidence interval, 0.59-0.71). The risk chart predicted risks of delayed cerebral ischemia-related infarction varying from 12% to 61%. Both low risk (&lt;20% risk) and high risk (&gt;40% risk) were predicted in ≈20% of the patients. Validation confirmed that the discriminative ability was adequate (area under the receiver operating characteristic curve, 0.69; 95% confidence interval, 0.61-0.77). Absolute risks of delayed cerebral ischemia-related infarction can be reliably estimated by a simple risk chart that includes clinical condition on admission, amount of blood on computed tomography (both cisternal and intraventricular), and age.</description><subject>Adolescent</subject><subject>Adult</subject><subject>Age Factors</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Biological and medical sciences</subject><subject>Brain - diagnostic imaging</subject><subject>Brain Ischemia - diagnostic imaging</subject><subject>Brain Ischemia - etiology</subject><subject>Female</subject><subject>Humans</subject><subject>Logistic Models</subject><subject>Male</subject><subject>Medical sciences</subject><subject>Middle Aged</subject><subject>Models, Theoretical</subject><subject>Neurology</subject><subject>Neuropharmacology</subject><subject>Neuroprotective agent</subject><subject>Pharmacology. Drug treatments</subject><subject>Prognosis</subject><subject>Prospective Studies</subject><subject>Radiography</subject><subject>Risk Assessment</subject><subject>Risk Factors</subject><subject>Subarachnoid Hemorrhage - complications</subject><subject>Subarachnoid Hemorrhage - diagnostic imaging</subject><subject>Time Factors</subject><subject>Vascular diseases and vascular malformations of the nervous system</subject><issn>0039-2499</issn><issn>1524-4628</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpFkEtvEzEUhS0EomnhHyDkDRKbKX7bs4xCaCoqFbWF7eiO55oY5hHsCVJ2_HSMkhZvrCt95xzpI-QNZ5ecG_7h_uHu9vN6uVmWU14yxrnQz8iCa6EqZYR7ThaMyboSqq7PyHnOPxhjQjr9kpwJqbmorV6QP2tI_YF-SdhFP8dppFOgH7GHA3Z0hQnbBD29zn6LQwS6DDMmer9vIYHfjlPs6AaHKaUtfMeS-439tBtwnCmMHf0GfezgsRXKCpQNXwrvYv5JV1tI8yvyIkCf8fXpvyBfP60fVpvq5vbqerW8qbyUdq64dd4KQFd3AYUACAGs0cq1ynupAgRXXpCmdnVQmvPWeWMlutY4WyB5Qd4fe3dp-rXHPDdDzB77Hkac9rnhUmnNrTWmoOqI-jTlnDA0uxQHSIeGs-af--bJfTllc3RfYm9PC_t2wO4p9Ci7AO9OAOQiISQYfcz_OSsFN8zJv0UvjjQ</recordid><startdate>20130501</startdate><enddate>20130501</enddate><creator>DE ROOIJ, Nicolien K</creator><creator>GREVING, Jacoba P</creator><creator>RINKEL, Gabriel J. 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Drug treatments</topic><topic>Prognosis</topic><topic>Prospective Studies</topic><topic>Radiography</topic><topic>Risk Assessment</topic><topic>Risk Factors</topic><topic>Subarachnoid Hemorrhage - complications</topic><topic>Subarachnoid Hemorrhage - diagnostic imaging</topic><topic>Time Factors</topic><topic>Vascular diseases and vascular malformations of the nervous system</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>DE ROOIJ, Nicolien K</creatorcontrib><creatorcontrib>GREVING, Jacoba P</creatorcontrib><creatorcontrib>RINKEL, Gabriel J. E</creatorcontrib><creatorcontrib>FRIJNS, Catharina J. 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M</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Early Prediction of Delayed Cerebral Ischemia After Subarachnoid Hemorrhage Development and Validation of a Practical Risk Chart</atitle><jtitle>Stroke (1970)</jtitle><addtitle>Stroke</addtitle><date>2013-05-01</date><risdate>2013</risdate><volume>44</volume><issue>5</issue><spage>1288</spage><epage>1294</epage><pages>1288-1294</pages><issn>0039-2499</issn><eissn>1524-4628</eissn><coden>SJCCA7</coden><abstract>To develop and validate a risk chart for prediction of delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage based on admission characteristics. For derivation of the risk chart, we studied data from 371 prospectively collected consecutive subarachnoid hemorrhage patients with a confirmed aneurysm admitted between 1999 and 2007. For its validation we similarly studied 255 patients admitted between 2007 and 2009. The predictive value of admission characteristics was tested in logistic regression models with delayed cerebral ischemia-related infarction as primary outcome. Procedure-related infarctions were not included. Performance of the models was tested by discrimination and calibration. On the basis of these models, a risk chart was developed for application in clinical practice. The strongest predictors were clinical condition on admission, amount of blood on computed tomography (both cisternal and intraventricular) and age. A model that combined these 4 predictors had an area under the receiver operating characteristic curve of 0.63 (95% confidence interval, 0.57-0.69). This model improved little by including current smoking and hyperglycemia on admission (area under the receiver operating characteristic curve, 0.65; 95% confidence interval, 0.59-0.71). The risk chart predicted risks of delayed cerebral ischemia-related infarction varying from 12% to 61%. Both low risk (&lt;20% risk) and high risk (&gt;40% risk) were predicted in ≈20% of the patients. Validation confirmed that the discriminative ability was adequate (area under the receiver operating characteristic curve, 0.69; 95% confidence interval, 0.61-0.77). Absolute risks of delayed cerebral ischemia-related infarction can be reliably estimated by a simple risk chart that includes clinical condition on admission, amount of blood on computed tomography (both cisternal and intraventricular), and age.</abstract><cop>Hagerstown, MD</cop><pub>Lippincott Williams &amp; Wilkins</pub><pmid>23512975</pmid><doi>10.1161/STROKEAHA.113.001125</doi><tpages>7</tpages></addata></record>
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source MEDLINE; American Heart Association Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Alma/SFX Local Collection; Journals@Ovid Complete
subjects Adolescent
Adult
Age Factors
Aged
Aged, 80 and over
Biological and medical sciences
Brain - diagnostic imaging
Brain Ischemia - diagnostic imaging
Brain Ischemia - etiology
Female
Humans
Logistic Models
Male
Medical sciences
Middle Aged
Models, Theoretical
Neurology
Neuropharmacology
Neuroprotective agent
Pharmacology. Drug treatments
Prognosis
Prospective Studies
Radiography
Risk Assessment
Risk Factors
Subarachnoid Hemorrhage - complications
Subarachnoid Hemorrhage - diagnostic imaging
Time Factors
Vascular diseases and vascular malformations of the nervous system
title Early Prediction of Delayed Cerebral Ischemia After Subarachnoid Hemorrhage Development and Validation of a Practical Risk Chart
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