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A systematic review of the Charlson comorbidity index using Canadian administrative databases: a perspective on risk
Dale M Needham1, Damon C Scales, Andreas Laupacis
1Department of Pulmonary and Critical Care Medicine, Johns Hopkins University, Baltimore, MD 21205, USA. dale.needham@utoronto.ca
Insights
The Charlson index, used for risk adjustment, shows good agreement with chart reviews in administrative databases. While APACHE II is better for critically ill patients, the Charlson index remains valuable for population research.
Area of Science:
- Health Services Research
- Critical Care Medicine
- Epidemiology
Background:
- The Charlson index is a widely used tool for risk adjustment in health services research, particularly in critical care.
- Existing literature supporting administrative database adaptations of the Charlson index requires thorough exploration.
Purpose of the Study:
- To systematically review and analyze the literature on administrative database adaptations of the Charlson index.
- To evaluate the validity and reliability of different Charlson index adaptations in various healthcare settings.
Main Methods:
- Systematic literature review of studies using administrative databases for Charlson index comorbidity data.
- Comparison of Canadian administrative databases with chart review for data accuracy.
- Analysis of agreement and mortality prediction between different Charlson index adaptations (e.g., Deyo, Dartmouth-Manitoba).
- Validation of the Charlson index against APACHE II in critically ill patients.
Main Results:
- Substantial agreement (kappa > 0.70) was found between Canadian administrative databases and chart reviews for Charlson comorbidity data, with similar mortality prediction.
- The Deyo and Dartmouth-Manitoba adaptations are most popular, showing substantial agreement and similar predictive ability for mortality.
- The Charlson index demonstrated lower discrimination for in-hospital mortality (AUC 0.67) compared to APACHE II (AUC 0.87) in critically ill patients.
Conclusions:
- Administrative database adaptations of the Charlson index, particularly Deyo and Dartmouth-Manitoba, offer a reliable and practical approach for population-based research.
- Despite lower predictive ability than physiology-based scores like APACHE II for specific populations (critically ill), the Charlson index's advantages in time and cost efficiency are significant for large-scale studies.
- Future research should focus on updating the Charlson index to reflect current disease prognoses and ICD-10 coding for improved accuracy.
Abstract:
The Charlson index is commonly used for risk adjustment in critical care health services research. However, the literature supporting this methodology has not been thoroughly explored. We systematically reviewed the literature related to administrative database adaptations of the Charlson index. Our review has 3 major findings. First, 2 studies compared Canadian administrative databases with chart review for obtaining Charlson comorbidity data. Agreement between the database and chart review was substantial (kappa > 0.70), and mortality prediction did not differ. Second, 5 database adaptations were identified with the Deyo and Dartmouth-Manitoba adaptations being most popular. Three studies directly compared these 2 popular adaptations and demonstrated substantial agreement (kappa > 0.70) and similar predictive ability for mortality. Third, one study validated the Charlson index for critically ill patients but demonstrated that APACHE (Acute Physiology and Chronic Health Evaluation) II better discriminates inhospital mortality (area under curve 0.67 vs 0.87). Time and cost barriers prevent widespread use of physiology-based risk adjustment in population-based research. The decreased predictive ability of the Charlson index must be weighed against the advantages of using this instrument for population-based research. Future research should focus on updating the Charlson index for recent changes in the prognosis of comorbid diseases and introduction of International Statistical Classification of Diseases, 10th Revision coding of discharge abstracts.
