Literature DB >> 18264013

Prognostic modelling in ischaemic stroke study, additional value of genetic characteristics. Rationale and design.

S Achterberg1, L J Kappelle, A Algra.   

Abstract

BACKGROUND AND AIM: The prediction of prognosis after cerebral infarction might be improved by genetic information. The aim of the Prognostic Modelling in Ischaemic Stroke study is to develop 2 different prognostic models on the basis of traditional vascular risk factors and genetic information in patients who have suffered from cerebral ischaemia of arterial origin, 1 concerning new ischaemic and the other new haemorrhagic events.
METHODS: Polymorphisms and haplotypes describing the haemostatic system and those that influence antithrombotic drug activity will be identified in a cohort of 1,200 patients with cerebral ischaemia of arterial origin who will be followed up for a mean of 6.5 years. In total, 312 ischaemic and 78 haemorrhagic events are anticipated. With a prevalence of a genetic characteristic of 10% a relative risk of 1.4 (95% confidence interval = 1.1-1.8) for ischaemic events and of 1.8 (95% confidence interval = 1.0-3.2) for haemorrhagic events can be estimated with sufficient precision. To determine the additional prognostic value of genetic characteristics the area under the ROC curves of 2 separate models will be compared: 1 based on non-genetic risk factors only, the other also including genetic data. (c) 2008 S. Karger AG, Basel.

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Year:  2008        PMID: 18264013     DOI: 10.1159/000115638

Source DB:  PubMed          Journal:  Eur Neurol        ISSN: 0014-3022            Impact factor:   1.710


  2 in total

1.  No Additional Prognostic Value of Genetic Information in the Prediction of Vascular Events after Cerebral Ischemia of Arterial Origin: The PROMISe Study.

Authors:  Sefanja Achterberg; L Jaap Kappelle; Paul I W de Bakker; Matthew Traylor; Ale Algra
Journal:  PLoS One       Date:  2015-04-23       Impact factor: 3.240

2.  A novel MMP12 locus is associated with large artery atherosclerotic stroke using a genome-wide age-at-onset informed approach.

Authors:  Matthew Traylor; Kari-Matti Mäkelä; Laura L Kilarski; Elizabeth G Holliday; William J Devan; Mike A Nalls; Kerri L Wiggins; Wei Zhao; Yu-Ching Cheng; Sefanja Achterberg; Rainer Malik; Cathie Sudlow; Steve Bevan; Emma Raitoharju; Niku Oksala; Vincent Thijs; Robin Lemmens; Arne Lindgren; Agnieszka Slowik; Jane M Maguire; Matthew Walters; Ale Algra; Pankaj Sharma; John R Attia; Giorgio B Boncoraglio; Peter M Rothwell; Paul I W de Bakker; Joshua C Bis; Danish Saleheen; Steven J Kittner; Braxton D Mitchell; Jonathan Rosand; James F Meschia; Christopher Levi; Martin Dichgans; Terho Lehtimäki; Cathryn M Lewis; Hugh S Markus
Journal:  PLoS Genet       Date:  2014-07-31       Impact factor: 5.917

  2 in total

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