Cross-National Comparative Performance of Three Versions of ... : Medical Care (original) (raw)

Brief Report

Cross-National Comparative Performance of Three Versions of the ICD-10 Charlson Index

Sundararajan, Vijaya MD, MPH*†; Quan, Hude MD, PhD‡§; Halfon, Patricia MD¶; Fushimi, Kiyohide MD, PhD∥; Luthi, Jean-Christophe MD, PhD¶; Burnand, Bernard MD, MPH¶; Ghali, William A. MD, MPH‡§** for the International Methodology Consortium for Coded Health Information (IMECCHI)

From the *Victorian Department of Human Services; †Clinical Epidemiology and Health Services Evaluation Unit, Royal Melbourne Hospital, Australia; ‡Department of Community Health Sciences; §Centre for Health and Policy Studies, University of Calgary, Calgary, Alberta, Canada; ¶Health Care Evaluation Unit, Institut Universitaire de Médecine Sociale et Préventive, University of Lausanne, Switzerland; ∥Department of Health Care Informatics, Tokyo Medical and Dental University Graduate School, Japan; and **Department of Medicine, University of Calgary, Calgary, Alberta, Canada.

Dr. Quan is supported by a Population Health Investigator Award from the Alberta Heritage Foundation for Medical Research, Edmonton, Alberta, Canada and by a New Investigator Award from the Canadian Institutes of Health Research.

Dr. Ghali is supported by a Health Scholar Award from the Alberta Heritage Foundation for Medical Research, Alberta, Canada, and by a Government of Canada Research Chair in Health Services Research.

Reprints: Dr. Vijaya Sundararajan, MD, MPH, Victorian Department of Human Services, 50 Lonsdale Street, Melbourne, Victoria 3000, Australia. E-mail: [email protected].

Abstract

Objective:

The Charlson comorbidity index has been widely used for risk adjustment in outcome studies using administrative health data. Recently, 3 International Statistical Classification of Diseases, Tenth Revision (ICD-10) translations have been published for the Charlson comorbidities. This study was conducted to compare the predictive performance of these versions (the Halfon, Sundararajan, and Quan versions) of the ICD-10 coding algorithms using data from 4 countries.

Methods:

Data from Australia (N = 2000–2001, max 25 diagnosis codes), Canada (N = 2002–2003, max 16 diagnosis codes), Switzerland (N = 1999–2001, unlimited number of diagnosis codes), and Japan (N = 2003, max 11 diagnosis codes) were analyzed. Only the first admission for patients age 18 years and older, with a length of stay of ≥2 days was included. For each algorithm, 2 logistic regression models were fitted with hospital mortality as the outcome and the Charlson individual comorbidities or the Charlson index score as independent variables. The c-statistic (representing the area under the receiver operating characteristic curve) and its 95% probability bootstrap distribution were employed to evaluate model performance.

Results:

Overall, within each population's data, the distribution of comorbidity level categories was similar across the 3 translations. The Quan version produced slightly higher median c-statistics than the Halfon or Sundararajan versions in all datasets. For example, in Japanese data, the median c-statistics were 0.712 (Quan), 0.709 (Sundararajan), and 0.694 (Halfon) using individual comorbidity coefficients. In general, the probability distributions between the Quan and the Sundararajan versions overlapped, whereas those between the Quan and the Halfon version did not.

Conclusions:

Our analyses show that all of the ICD-10 versions of the Charlson algorithm performed satisfactorily (c-statistics 0.70–0.86), with the Quan version showing a trend toward outperforming the other versions in all data sets.