The Charlson comorbidity index can be used prospectively to identify patients who will incur high future costs

Area of Science:

• Health economics
• Predictive analytics in healthcare
• Patient stratification

Background:

• Identifying high-cost patients is crucial for healthcare cost reduction.
• The Charlson comorbidity index is a widely used measure of disease burden.
• Evaluating predictive accuracy of comorbidity scores is essential for resource allocation.

Purpose of the Study:

• To assess the Charlson comorbidity index's ability to predict subsequent year high healthcare costs.
• To compare the predictive performance of the Charlson comorbidity index against other common predictors like prior costs and Diagnostic Cost Groups (DCG).

Main Methods:

• Retrospective analysis of claims data from a large health benefits fund.
• Compared Charlson comorbidity index, prior year costs, DCG, and hospitalization as predictors of subsequent year total costs.
• Regression analysis was used to determine the explained variance in predicting total costs.

Main Results:

• A significant positive association was observed between increasing comorbidity index and total yearly costs (P<.001).
• The Charlson comorbidity index, prior costs, and DCG demonstrated nearly identical explained variance in predicting total costs for both adults and children.
• At lower comorbidity levels, healthcare costs were similar across various chronic diseases.

Conclusions:

• The Charlson comorbidity index is a valuable tool for prospectively identifying patients likely to incur high healthcare costs.
• Its predictive performance is comparable to established metrics like prior costs and DCG, particularly for identifying the top 5% or 10% of high-cost individuals.
• The index offers a reliable method for risk stratification in healthcare management.