title: Prognostic Tools for Early Mortality in Hemorrhagic Stroke: Systematic Review and Meta-Analysis.
abstract: OBJECTIVE
  Several risk scores have been developed to predict mortality in intracerebral hemorrhage (ICH). We aimed to systematically determine the performance of published prognostic tools.
  
  
  METHODS
  We searched MEDLINE and EMBASE for prognostic models (published between 2004 and April 2014) used in predicting early mortality (<6 months) after ICH. We evaluated the discrimination performance of the tools through a random-effects meta-analysis of the area under the receiver operating characteristic curve (AUC) or c-statistic. We evaluated the following components of the study validity: study design, collection of prognostic variables, treatment pathways, and missing data.
  
  
  RESULTS
  We identified 11 articles (involving 41,555 patients) reporting on the accuracy of 12 different tools for predicting mortality in ICH. Most studies were either retrospective or post-hoc analyses of prospectively collected data; all but one produced validation data. The Hemphill-ICH score had the largest number of validation cohorts (9 studies involving 3,819 patients) within our systematic review and showed good performance in 4 countries, with a pooled AUC of 0.80 [95% confidence interval (CI)=0.77-0.85]. We identified several modified versions of the Hemphill-ICH score, with the ICH-Grading Scale (GS) score appearing to be the most promising variant, with a pooled AUC across four studies of 0.87 (95% CI=0.84-0.90). Subgroup testing found statistically significant differences between the AUCs obtained in studies involving Hemphill-ICH and ICH-GS scores (p=0.01).
  
  
  CONCLUSIONS
  Our meta-analysis evaluated the performance of 12 ICH prognostic tools and found greater supporting evidence for 2 models (Hemphill-ICH and ICH-GS), with generally good performance overall.
authors.affiliation: Health Evidence Synthesis Group, Norwich Medical School, University of East Anglia, Norwich Research Park, Norwich, UK.
authors.forename: Katharina
authors.initials: K
authors.lastname: Mattishent
authors.affiliation: Institute of Cardiovascular Sciences, University of Manchester, Manchester, UK.
authors.forename: Chun Shing
authors.initials: CS
authors.lastname: Kwok
authors.affiliation: Health Evidence Synthesis Group, Norwich Medical School, University of East Anglia, Norwich Research Park, Norwich, UK.
authors.forename: Liban
authors.initials: L
authors.lastname: Ashkir
authors.affiliation: Department of Elderly Medicine, Southend University Hospital Trust, Westcliff-on-Sea, Essex, UK.
authors.forename: Kelum
authors.initials: K
authors.lastname: Pelpola
authors.affiliation: Epidemiology Group, Institute of Applied Health Sciences, School of Medicine & Dentistry, University of Aberdeen, Aberdeen, Scotland, UK.
authors.forename: Phyo Kyaw
authors.initials: PK
authors.lastname: Myint
authors.affiliation: Health Evidence Synthesis Group, Norwich Medical School, University of East Anglia, Norwich Research Park, Norwich, UK. y.loke@uea.ac.uk.
authors.forename: Yoon Kong
authors.initials: YK
authors.lastname: Loke
keywords: mortality; prognostic scores; risk prediction model; stroke