Why summary comorbidity measures such as the Charlson Comorbidity Index and Elixhauser score work
By S. Austin, Y. Wong, R. Uzzo, J. Robert Beck, Brian L. Egleston. Medical Care, 2015
664 citations recorded by the source corpus.
Background:Comorbidity adjustment is an important component of health services research and clinical prognosis. When adjusting for comorbidities in statistical models, researchers can include comorbidities individually or through the use of summary measures such as the Charlson Comorbidity Index or Elixhauser score. We examined the conditions under which individual versus summary measures are most appropriate. Methods:We provide an analytic proof of the utility of comorbidity summary measures when used in place of individual comorbidities. We compared the use of the Charlson and Elixhauser scores versus individual comorbidities in prognostic models using a SEER-Medicare data example. We examined the ability of summary comorbidity measures to adjust for confounding using simulations. Results:We devised a mathematical proof that found that the comorbidity summary measures are appropriate prognostic or adjustment mechanisms in survival analyses. Once one knows the comorbidity score, no other information about the comorbidity variables used to create the score is generally needed. Our data example and simulations largely confirmed this finding. Conclusions:Summary comorbidity measures, such as the Charlson Comorbidity Index and Elixhauser scores, are commonly used for clinical prognosis and comorbidity adjustment. We have provided a theoretical justification that validates the use of such scores under many conditions. Our simulations generally confirm the utility of the summary comorbidity measures as substitutes for use of the individual comorbidity variables in health services research. One caveat is that a summary measure may only be as good as the variables used to create it.
Subfields: Epidemiology, General Health Professions, Health Information Management
View Paper: https://www.semanticscholar.org/paper/b16844b47c2a7d3d31df6697491990f97ee9ef93
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Epidemiology
Citations in the corpus
- Identifying Increased Risk of Readmission and In-hospital Mortality Using Hospital Administrative Data: The AHRQ Elixhauser Comorbidity Index (2017, background): “Recent evidence suggests that comorbidity indices can be valid substitutes for the full set of measures under a wide set of conditions and can provide a way to condense comorbidity information into an easy-to-use metric.(9) Previous research on comorbidity indices focused on predicting mortality in ”