Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data.
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.
Quan
Hude
H
Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada. hquan@ucalgary.ca
Sundararajan
Vijaya
V
Halfon
Patricia
P
Fong
Andrew
A
Burnand
Bernard
B
Luthi
Jean-Christophe
JC
Saunders
L Duncan
LD
Beck
Cynthia A
CA
Feasby
Thomas E
TE
Ghali
William A
WA
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