title: Clinical prediction of functional outcome after ischemic stroke: the surprising importance of periventricular white matter disease and race.
abstract: OBJECTIVE
  We sought to build models that address questions of interest to patients and families by predicting short- and long-term mortality and functional outcome after ischemic stroke, while allowing for risk restratification as comorbid events accumulate.
  
  
  METHODS
  A cohort of 451 ischemic stroke subjects in 1999 were interviewed during hospitalization, at 3 months, and at approximately 4 years. Medical records from the acute hospitalization were abstracted. All hospitalizations for 3 months poststroke were reviewed to ascertain medical and psychiatric comorbidities, which were categorized for analysis. Multivariable models were derived to predict mortality and functional outcome (modified Rankin Scale) at 3 months and 4 years. Comorbidities were included as modifiers of the 3-month models, and included in 4-year predictions.
  
  
  RESULTS
  Poststroke medical and psychiatric comorbidities significantly increased short-term poststroke mortality and morbidity. Severe periventricular white matter disease (PVWMD) was significantly associated with poor functional outcome at 3 months, independent of other factors, such as diabetes and age; inclusion of this imaging variable eliminated other traditional risk factors often found in stroke outcomes models. Outcome at 3 months was a significant predictor of long-term mortality and functional outcome. Black race was a predictor of 4-year mortality.
  
  
  CONCLUSIONS
  We propose that predictive models for stroke outcome, as well as analysis of clinical trials, should include adjustment for comorbid conditions. The effects of PVWMD on short-term functional outcomes and black race on long-term mortality are findings that require confirmation.
authors.affiliation: Department of Neurology, University of Cincinnati, Ohio, USA. Brett.kissela@uc.edu
authors.forename: Brett
authors.initials: B
authors.lastname: Kissela
authors.forename: Christopher J
authors.initials: CJ
authors.lastname: Lindsell
authors.forename: Dawn
authors.initials: D
authors.lastname: Kleindorfer
authors.forename: Kathleen
authors.initials: K
authors.lastname: Alwell
authors.forename: Charles J
authors.initials: CJ
authors.lastname: Moomaw
authors.forename: Daniel
authors.initials: D
authors.lastname: Woo
authors.forename: Matthew L
authors.initials: ML
authors.lastname: Flaherty
authors.forename: Ellen
authors.initials: E
authors.lastname: Air
authors.forename: Joseph
authors.initials: J
authors.lastname: Broderick
authors.forename: Joel
authors.initials: J
authors.lastname: Tsevat
mesh_terms: D000368:Aged; D000465:Algorithms; D044383:Black People; D001921:Brain / Q000473:pathology*; D001927:Brain Diseases / Q000473:pathology*; D015331:Cohort Studies; D015897:Comorbidity; D005006:Ethnicity; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D011446:Prospective Studies; D012959:Socioeconomic Factors; D020521:Stroke / Q000401:mortality / Q000473:pathology*; D000071939:Stroke Rehabilitation; D016896:Treatment Outcome; D044465:White People