title: Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data.
abstract: OBJECTIVE
  Implementation of the International Statistical Classification of Disease and Related Health Problems, 10th Revision (ICD-10) coding system presents challenges for using administrative data. Recognizing this, we conducted a multistep process to develop ICD-10 coding algorithms to define Charlson and Elixhauser comorbidities in administrative data and assess the performance of the resulting algorithms.
  
  
  METHODS
  ICD-10 coding algorithms were developed by "translation" of the ICD-9-CM codes constituting Deyo's (for Charlson comorbidities) and Elixhauser's coding algorithms and by physicians' assessment of the face-validity of selected ICD-10 codes. The process of carefully developing ICD-10 algorithms also produced modified and enhanced ICD-9-CM coding algorithms for the Charlson and Elixhauser comorbidities. We then used data on in-patients aged 18 years and older in ICD-9-CM and ICD-10 administrative hospital discharge data from a Canadian health region to assess the comorbidity frequencies and mortality prediction achieved by the original ICD-9-CM algorithms, the enhanced ICD-9-CM algorithms, and the new ICD-10 coding algorithms.
  
  
  RESULTS
  Among 56,585 patients in the ICD-9-CM data and 58,805 patients in the ICD-10 data, frequencies of the 17 Charlson comorbidities and the 30 Elixhauser comorbidities remained generally similar across algorithms. The new ICD-10 and enhanced ICD-9-CM coding algorithms either matched or outperformed the original Deyo and Elixhauser ICD-9-CM coding algorithms in predicting in-hospital mortality. The C-statistic was 0.842 for Deyo's ICD-9-CM coding algorithm, 0.860 for the ICD-10 coding algorithm, and 0.859 for the enhanced ICD-9-CM coding algorithm, 0.868 for the original Elixhauser ICD-9-CM coding algorithm, 0.870 for the ICD-10 coding algorithm and 0.878 for the enhanced ICD-9-CM coding algorithm.
  
  
  CONCLUSIONS
  These newly developed ICD-10 and ICD-9-CM comorbidity coding algorithms produce similar estimates of comorbidity prevalence in administrative data, and may outperform existing ICD-9-CM coding algorithms.
authors.affiliation: Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada. hquan@ucalgary.ca
authors.forename: Hude
authors.initials: H
authors.lastname: Quan
authors.forename: Vijaya
authors.initials: V
authors.lastname: Sundararajan
authors.forename: Patricia
authors.initials: P
authors.lastname: Halfon
authors.forename: Andrew
authors.initials: A
authors.lastname: Fong
authors.forename: Bernard
authors.initials: B
authors.lastname: Burnand
authors.forename: Jean-Christophe
authors.initials: JC
authors.lastname: Luthi
authors.forename: L Duncan
authors.initials: LD
authors.lastname: Saunders
authors.forename: Cynthia A
authors.initials: CA
authors.lastname: Beck
authors.forename: Thomas E
authors.initials: TE
authors.lastname: Feasby
authors.forename: William A
authors.initials: WA
authors.lastname: Ghali
mesh_terms: D000465:Algorithms; D002170:Canada / Q000453:epidemiology; D015897:Comorbidity; D004194:Disease / Q000145:classification; D005260:Female; D005568:Forms and Records Control / Q000379:methods*; D017052:Hospital Mortality; D006801:Humans; D038801:International Classification of Diseases; D008297:Male; D008499:Medical Records / Q000145:classification; D008875:Middle Aged; D015233:Models, Statistical; D020379:Risk Adjustment