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Cross-national comparative performance of three versions of the ICD-10 Charlson index
Vijaya Sundararajan1, Hude Quan, Patricia Halfon
1Victorian Department of Human Servicest, Royal Melbourne Hospital, Australia. vijaya.sundararajan@dhs.vic.gov.au
Insights
The Quan version of the Charlson comorbidity index using International Statistical Classification of Diseases, Tenth Revision (ICD-10) codes showed a trend toward better prediction of hospital mortality compared to other versions. All tested ICD-10 Charlson algorithms performed satisfactorily.
Area of Science:
- Health Services Research
- Medical Informatics
- Epidemiology
Background:
- The Charlson comorbidity index is crucial for risk adjustment in health administrative data.
- Recent advancements include three International Statistical Classification of Diseases, Tenth Revision (ICD-10) translations for the Charlson comorbidities.
- Evaluating these ICD-10 versions is essential for accurate outcome studies.
Purpose of the Study:
- To compare the predictive performance of three ICD-10 coded Charlson comorbidity index algorithms: Halfon, Sundararajan, and Quan.
- To assess these algorithms using administrative health data from four diverse countries.
Main Methods:
- Analysis of administrative data from Australia, Canada, Switzerland, and Japan.
- Inclusion criteria: first admission, age ≥18 years, length of stay ≥2 days.
- Logistic regression models used hospital mortality as the outcome, with c-statistics evaluating predictive performance.
Main Results:
- All three ICD-10 Charlson algorithm translations demonstrated similar comorbidity distribution patterns.
- The Quan version exhibited slightly higher median c-statistics across all datasets compared to Halfon and Sundararajan.
- Probability distributions indicated overlap between Quan and Sundararajan, but not between Quan and Halfon.
Conclusions:
- All evaluated ICD-10 versions of the Charlson algorithm performed satisfactorily, with c-statistics ranging from 0.70 to 0.86.
- The Quan version showed a consistent trend of superior predictive performance across all analyzed datasets.
- These findings support the use of ICD-10 coded Charlson algorithms for risk adjustment in health outcome research.
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.
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