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Neurology. 2010 Jan 12;74(2):128-35. doi: 10.1212/WNL.0b013e3181ca9cff. Epub 2009 Dec 16.

A score to predict early risk of recurrence after ischemic stroke.

Author information

  • 1Stroke Service, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. hay@partners.org

Abstract

BACKGROUND:

There is currently no instrument to stratify patients presenting with ischemic stroke according to early risk of recurrent stroke. We sought to develop a comprehensive prognostic score to predict 90-day risk of recurrent stroke.

METHODS:

We analyzed data on 1,458 consecutive ischemic stroke patients using a Cox regression model with time to recurrent stroke as the response and clinical and imaging features typically available to physician at admission as covariates. The 90-day risk of recurrent stroke was calculated by summing up the number of independent predictors weighted by their corresponding beta-coefficients. The resultant score was called recurrence risk estimator at 90 days or RRE-90 score (available at: http://www.nmr.mgh.harvard.edu/RRE-90/).

RESULTS:

Sixty recurrent strokes (54 had baseline imaging) occurred during the follow-up period. The risk adjusted for time to follow-up was 6.0%. Predictors of recurrence included admission etiologic stroke subtype, prior history of TIA/stroke, and topography, age, and distribution of brain infarcts. The RRE-90 score demonstrated adequate calibration and good discrimination (area under the ROC curve [AUC] = 0.70-0.80), which was maintained when applied to a separate cohort of 433 patients (AUC = 0.70-0.76). The model's performance was also maintained for predicting early (14-day) risk of recurrence (AUC = 0.80).

CONCLUSIONS:

The RRE-90 is a Web-based, easy-to-use prognostic score that integrates clinical and imaging information available in the acute setting to quantify early risk of recurrent stroke. The RRE-90 demonstrates good predictive performance, suggesting that, if validated externally, it has promise for use in creating individualized patient management algorithms and improving clinical practice in acute stroke care.

Comment in

PMID:
20018608
[PubMed - indexed for MEDLINE]
PMCID:
PMC2809031
Free PMC Article
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