title: Use of a self-report-generated Charlson Comorbidity Index for predicting mortality.
abstract: BACKGROUND
  The Charlson Comorbidity Index, a popular tool for risk adjustment, often is constructed from medical record abstracts or administrative data. Limitations in both sources have fueled interest in using patient self-report as an alternative. However, little data exist on whether self-reported Charlson Indices predict mortality.
  
  
  OBJECTIVE
  We sought to determine whether a self-reported Charlson Index predicts mortality, its performance relative to indices derived from administrative data, and whether using study-specific weights instead of Charlson's original weights enhances model fit.
  
  
  METHODS
  We surveyed 7761 patients admitted to a university medical service over the course of 4 years and extracted their administrative data. We constructed 6 different Charlson indices by using 2 weighting schemes (original Charlson weights and study-specific weights) and 3 different datasources (ICD-9CM data for index hospitalization, ICD-9CM data with a 1-year look-back period, and patient self-report of comorbidities.) Multivariate models were constructed predicting 1-year mortality, log total costs, and log length of stay.
  
  
  RESULTS
  The 6 measures of the Charlson index all predicted 1-year mortality. Models with age and gender, with or without diagnosis-related group, had approximately the same predictive power regardless of which of the 6 Charlson indices were used. Nevertheless, there were small improvements in model fit using administrative data versus self-report, or study-specific versus original weights. All models obtained areas under the receiver operating curve of 0.70 to 0.77.
  
  
  CONCLUSIONS
  Overall, self-reported Charlson indices predict 1-year mortality comparably with indices based on administrative data. Administrative data may offer some small improvements in predictive ability and may be preferred when readily available.
authors.affiliation: University of Chicago, Chicago, Illinois, USA. schaudhr@nshs.edu
authors.forename: Saima
authors.initials: S
authors.lastname: Chaudhry
authors.forename: Lei
authors.initials: L
authors.lastname: Jin
authors.forename: David
authors.initials: D
authors.lastname: Meltzer
mesh_terms: D000328:Adult; D002641:Chicago / Q000453:epidemiology; D015897:Comorbidity; D003953:Diagnosis-Related Groups; D005260:Female; D005544:Forecasting; D006760:Hospitalization; D006785:Hospitals, University / Q000706:statistics & numerical data; D006801:Humans; D038801:International Classification of Diseases; D007407:Interviews as Topic; D008297:Male; D008875:Middle Aged; D009026:Mortality; D012044:Regression Analysis; D020379:Risk Adjustment / Q000379:methods*; D012650:Self Disclosure; D011795:Surveys and Questionnaires / Q000592:standards*