title: A score to predict early risk of recurrence after ischemic stroke.
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.
authors.affiliation: Stroke Service, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. hay@partners.org
authors.forename: H
authors.initials: H
authors.lastname: Ay
authors.forename: L
authors.initials: L
authors.lastname: Gungor
authors.forename: E M
authors.initials: EM
authors.lastname: Arsava
authors.forename: J
authors.initials: J
authors.lastname: Rosand
authors.forename: M
authors.initials: M
authors.lastname: Vangel
authors.forename: T
authors.initials: T
authors.lastname: Benner
authors.forename: L H
authors.initials: LH
authors.lastname: Schwamm
authors.forename: K L
authors.initials: KL
authors.lastname: Furie
authors.forename: W J
authors.initials: WJ
authors.lastname: Koroshetz
authors.forename: A G
authors.initials: AG
authors.lastname: Sorensen
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D002545:Brain Ischemia / Q000175:diagnosis* / Q000453:epidemiology*; D015331:Cohort Studies; D005260:Female; D006801:Humans; D020407:Internet / Q000639:trends; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D011379:Prognosis; D016016:Proportional Hazards Models; D012008:Recurrence; D012044:Regression Analysis; D015203:Reproducibility of Results; D012189:Retrospective Studies; D018570:Risk Assessment / Q000379:methods; D012984:Software; D020521:Stroke / Q000175:diagnosis* / Q000453:epidemiology*