Comparison of the Elixhauser and Charlson/Deyo methods of comorbidity measurement in administrative data.
BACKGROUND
Comorbidity risk adjustment methods have been used widely with administrative data, and the Charlson/Deyo method is perhaps the most commonly used in the literature. However, a new method defined by Elixhauser et al. has been introduced recently and could be superior, although it has not been validated widely.


OBJECTIVE
We compared the Charlson/Deyo and Elixhauser methods using Canadian administrative data on patients with myocardial infarction (MI).


METHODS
We conducted a historical cohort study.


METHODS
We used administrative hospital discharge data from a large Canadian city for all cases with acute MI coded as most responsible diagnosis between January 1, 1995, and March 31, 2001.


METHODS
We used each of the 2 methods to define comorbidity variables based on the International Classification of Diseases, 9th Revision, Clinical Modification codes present in each case record. We then compared 2 models predicting in-hospital mortality based on presence or absence of the variables defined by each of the methods. Frequency tables were produced and c-statistics and changes in -2 log likelihood (-2LogL) were calculated. We also visually assessed model performance by plotting observed and expected percentages of death for increasing risk categories defined by the 2 models.


RESULTS
The Elixhauser model outperformed the Charlson/Deyo model in predicting mortality, with higher c-statistic values (0.793 vs. 0.704). Superior performance of the Elixhauser method is confirmed when plotting the expected and observed risks of death across groupings of increasing risk, in which the Elixhauser method yields a wider range of predicted and observed probabilities of death across groupings (2.5%-33%) than does the Charlson/Deyo method (5%-25%).


CONCLUSIONS
The Elixhauser comorbidity measurement method performs better than the widely used Charlson/Deyo method in the Canadian acute MI cases studied.
Southern
Danielle A
DA
Department of Community Health Sciences and the Centre for Health and Policy Studies, University of Calgary, Calgary, Alberta, Canada.
Quan
Hude
H
Ghali
William A
WA
D000368:Aged; D000416:Alberta / Q000453:epidemiology; D015897:Comorbidity; D003627:Data Interpretation, Statistical; D005260:Female; D017052:Hospital Mortality; D006801:Humans; D038801:International Classification of Diseases; D016015:Logistic Models; D008297:Male; D015233:Models, Statistical; D015999:Multivariate Analysis; D009203:Myocardial Infarction / Q000145:classification / Q000150:complications / Q000453:epidemiology; D016017:Odds Ratio; D009934:Organization and Administration; D010351:Patient Discharge / Q000706:statistics & numerical data; D011237:Predictive Value of Tests; D015995:Prevalence; D011379:Prognosis; D020379:Risk Adjustment / Q000379:methods*