title: A combined comorbidity score predicted mortality in elderly patients better than existing scores.
abstract: OBJECTIVE
  To develop and validate a single numerical comorbidity score for predicting short- and long-term mortality, by combining conditions in the Charlson and Elixhauser measures.
  
  
  METHODS
  In a cohort of 120,679 Pennsylvania Medicare enrollees with drug coverage through a pharmacy assistance program, we developed a single numerical comorbidity score for predicting 1-year mortality, by combining the conditions in the Charlson and Elixhauser measures. We externally validated the combined score in a cohort of New Jersey Medicare enrollees, by comparing its performance to that of both component scores in predicting 1-year mortality, as well as 180-, 90-, and 30-day mortality.
  
  
  RESULTS
  C-statistics from logistic regression models including the combined score were higher than corresponding c-statistics from models including either the Romano implementation of the Charlson Index or the single numerical version of the Elixhauser system; c-statistics were 0.860 (95% confidence interval [CI]: 0.854, 0.866), 0.839 (95% CI: 0.836, 0.849), and 0.836 (95% CI: 0.834, 0.847), respectively, for the 30-day mortality outcome. The combined comorbidity score also yielded positive values for two recently proposed measures of reclassification.
  
  
  CONCLUSIONS
  In similar populations and data settings, the combined score may offer improvements in comorbidity summarization over existing scores.
authors.affiliation: Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, 1620 Tremont Street, Suite 3030, Boston, MA 02120, USA. jgagne1@partners.org
authors.forename: Joshua J
authors.initials: JJ
authors.lastname: Gagne
authors.forename: Robert J
authors.initials: RJ
authors.lastname: Glynn
authors.forename: Jerry
authors.initials: J
authors.lastname: Avorn
authors.forename: Raisa
authors.initials: R
authors.lastname: Levin
authors.forename: Sebastian
authors.initials: S
authors.lastname: Schneeweiss
mesh_terms: D000368:Aged; D015331:Cohort Studies; D015897:Comorbidity; D005260:Female; D017052:Hospital Mortality; D006801:Humans; D016015:Logistic Models; D008297:Male; D009026:Mortality; D009515:New Jersey / Q000453:epidemiology; D010414:Pennsylvania / Q000453:epidemiology; D011237:Predictive Value of Tests; D020379:Risk Adjustment