title: Clinical prediction models for aneurysmal subarachnoid hemorrhage: a systematic review.
abstract: BACKGROUND
  Clinical prediction models can enhance clinical decision-making and research. However, available prediction models in aneurysmal subarachnoid hemorrhage (aSAH) are rarely used. We evaluated the methodological validity of SAH prediction models and the relevance of the main predictors to identify potentially reliable models and to guide future attempts at model development.
  
  
  METHODS
  We searched the EMBASE, MEDLINE, and Web of Science databases from January 1995 to June 2012 to identify studies that reported clinical prediction models for mortality and functional outcome in aSAH. Validated methods were used to minimize bias.
  
  
  RESULTS
  Eleven studies were identified; 3 developed models from datasets of phase 3 clinical trials, the others from single hospital records. The median patient sample size was 340 (interquartile range 149-733). The main predictors used were age (n = 8), Fisher grade (n = 6), World Federation of Neurological Surgeons grade (n = 5), aneurysm size (n = 5), and Hunt and Hess grade (n = 3). Age was consistently dichotomized. Potential predictors were prescreened by univariate analysis in 36 % of studies. Only one study was penalized for model optimism. Details about model development were often insufficiently described and no published studies provided external validation.
  
  
  CONCLUSIONS
  While clinical prediction models for aSAH use a few simple predictors, there are substantial methodological problems with the models and none have had external validation. This precludes the use of existing models for clinical or research purposes. We recommend further studies to develop and validate reliable clinical prediction models for aSAH.
authors.affiliation: St. Michael's Hospital, University of Toronto, Toronto, ON, Canada.
authors.forename: Blessing N R
authors.initials: BN
authors.lastname: Jaja
authors.forename: Michael D
authors.initials: MD
authors.lastname: Cusimano
authors.forename: Nima
authors.initials: N
authors.lastname: Etminan
authors.forename: Daniel
authors.initials: D
authors.lastname: Hanggi
authors.forename: David
authors.initials: D
authors.lastname: Hasan
authors.forename: Don
authors.initials: D
authors.lastname: Ilodigwe
authors.forename: Hector
authors.initials: H
authors.lastname: Lantigua
authors.forename: Peter
authors.initials: P
authors.lastname: Le Roux
authors.forename: Benjamin
authors.initials: B
authors.lastname: Lo
authors.forename: Ada
authors.initials: A
authors.lastname: Louffat-Olivares
authors.forename: Stephan
authors.initials: S
authors.lastname: Mayer
authors.forename: Andrew
authors.initials: A
authors.lastname: Molyneux
authors.forename: Audrey
authors.initials: A
authors.lastname: Quinn
authors.forename: Tom A
authors.initials: TA
authors.lastname: Schweizer
authors.forename: Thomas
authors.initials: T
authors.lastname: Schenk
authors.forename: Julian
authors.initials: J
authors.lastname: Spears
authors.forename: Michael
authors.initials: M
authors.lastname: Todd
authors.forename: James
authors.initials: J
authors.lastname: Torner
authors.forename: Mervyn D I
authors.initials: MD
authors.lastname: Vergouwen
authors.forename: George K C
authors.initials: GK
authors.lastname: Wong
authors.forename: Jeff
authors.initials: J
authors.lastname: Singh
authors.forename: R Loch
authors.initials: RL
authors.lastname: Macdonald
mesh_terms: D003661:Decision Support Techniques; D006801:Humans; D020127:Recovery of Function; D013345:Subarachnoid Hemorrhage / Q000401:mortality*; D016896:Treatment Outcome