title: Predicting treatment costs after acute ischemic stroke on the basis of patient characteristics at presentation and early dysfunction.
abstract: BACKGROUND
  Given the pressure on healthcare budgets, assessing the cost of managing a disease has become a major research focus; yet collection of these data are labor intensive and difficult. Understanding the predictors of cost provides an efficient means of incorporating such information in decision-making concerning new therapies.
  
  
  METHODS
  Data from two 12-week multinational trials that collected information on a variety of neurological, functional, and cost parameters for 1341 ischemic stroke patients were examined by means of multiple linear regression. Because the intent is for the model to be predictive, only patient characteristics that can be known at the time of patient presentation or shortly thereafter were evaluated for inclusion in the model.
  
  
  RESULTS
  The Barthel Index was the strongest predictor of cost in all models evaluated. Other major predictors, either directly or through their impact on survival, were stroke subtype, neurological impairment, congestive heart failure, and country. A good model fit was obtained, judging by the model statistics (model F:=84, 3 df, P:<0.0001) and the accuracy of the predictions (<3% difference between mean actual and predicted cost).
  
  
  CONCLUSIONS
  Through the use of key patient characteristics, this regression model allows for prediction of the cost of stroke care, which may be helpful in the context of therapeutic decisions and budgetary planning purposes. It also provides insight into how specific treatments, through their impact on clinical characteristics, can modify the cost of stroke treatment.
authors.affiliation: Caro Research, Concord, MA 01742, USA. jcaro@caroresearch.com
authors.forename: J J
authors.initials: JJ
authors.lastname: Caro
authors.forename: K F
authors.initials: KF
authors.lastname: Huybrechts
authors.forename: H E
authors.initials: HE
authors.lastname: Kelley
mesh_terms: D000367:Age Factors; D000368:Aged; D001315:Australia; D002170:Canada; D003710:Demography; D019468:Disease Management; D005060:Europe; D005260:Female; D017048:Health Care Costs / Q000706:statistics & numerical data*; D006304:Health Status; D006801:Humans; D016014:Linear Models; D008297:Male; D015233:Models, Statistical; D015999:Multivariate Analysis; D009313:National Health Programs / Q000191:economics; D017063:Outcome Assessment, Health Care / Q000191:economics* / Q000379:methods*; D011237:Predictive Value of Tests; D011379:Prognosis; D012307:Risk Factors; D012720:Severity of Illness Index; D012737:Sex Factors; D020521:Stroke / Q000145:classification / Q000175:diagnosis / Q000191:economics* / Q000401:mortality; D015996:Survival Rate; D014481:United States