title: Comorbidity scores for administrative data benefited from adaptation to local coding and diagnostic practices.
abstract: OBJECTIVE
  The Charlson and Elixhauser indices are the most commonly used comorbidity indices with risk prediction models using administrative data. Our objective was to compare the original Charlson index, a modified set of Charlson codes after advice from clinical coders, and a published modified Elixhauser index in predicting in-hospital mortality.
  
  
  METHODS
  Logistic regression using two separate years of administrative hospital data for all acute nonspecialist public hospitals in England.
  
  
  RESULTS
  For all admissions combined, discrimination was similar for the Charlson index using the original codes and weights and the Charlson index using the original codes but England-calibrated weights (c=0.73), although model fit was superior for the latter. The new Charlson codes improved discrimination (c=0.76), model fit, and consistency of recording between admissions. The modified Elixhauser had the best performance (c=0.80). For admissions for acute myocardial infarction and chronic obstructive pulmonary disease, the weights often differed, although the patterns were broadly similar.
  
  
  CONCLUSIONS
  Recalibration of the original Charlson index yielded only modest benefits overall. The modified Charlson codes and weights offer better fit and discrimination for English data over the original version. The modified Elixhauser performed best of all, but its weights were perhaps less consistent across the different patient groups considered here.
authors.affiliation: Dr Foster Unit, Department of Primary Care and Public Health, Imperial College London, 12 Smithfield Street, London EC1A 9LA, UK. robert.bottle@imperial.ac.uk
authors.forename: Alex
authors.initials: A
authors.lastname: Bottle
authors.forename: Paul
authors.initials: P
authors.lastname: Aylin
mesh_terms: D000465:Algorithms; D015897:Comorbidity; D004739:England / Q000453:epidemiology; D017052:Hospital Mortality; D006779:Hospitals, Public; D006801:Humans; D038801:International Classification of Diseases; D016015:Logistic Models; D009203:Myocardial Infarction / Q000175:diagnosis / Q000453:epidemiology*; D010343:Patient Admission / Q000706:statistics & numerical data*; D011237:Predictive Value of Tests; D015995:Prevalence; D029424:Pulmonary Disease, Chronic Obstructive / Q000175:diagnosis / Q000453:epidemiology*; D020379:Risk Adjustment