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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.

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