Comparing Charlson and Elixhauser Comorbidity Measures in Predicting Mortality
Author Information
Author(s): Li Bing, Evans Dewey, Faris Peter, Dean Stafford, Hude Quan
Primary Institution: University of Calgary
Hypothesis
The study aims to assess the performance of Charlson and Elixhauser comorbidity measures in predicting in-hospital and 1-year mortality using ICD-10 data.
Conclusion
The change in coding algorithms did not influence the performance of either the Charlson or Elixhauser comorbidity measures in predicting mortality outcomes.
Supporting Evidence
- The performance of the Charlson and Elixhauser measures was similar across ICD-9 and ICD-10 datasets.
- C-statistics for predicting in-hospital mortality ranged from 0.62 to 0.82 for the Charlson measure and 0.62 to 0.83 for the Elixhauser measure.
- Both comorbidity measures were valid prognostic indicators in the ICD-10 data.
- Mortality rates were similar for chronic conditions across both coding systems.
Takeaway
This study looked at how well two methods for measuring health conditions predict if patients will survive after hospital treatment, and found both methods work just as well with new coding systems.
Methodology
The study used a Canadian provincial hospital discharge administrative database to define comorbidities and calculated C-statistic values to evaluate performance.
Limitations
The study's findings may not apply to other diseases or procedures, and it assessed comorbidity measures in two different periods which could affect model performance.
Participant Demographics
The study included patients with congestive heart failure, diabetes, chronic renal failure, stroke, and those undergoing coronary artery bypass grafting, with exclusions for non-residents and those under 20 years old.
Digital Object Identifier (DOI)
Want to read the original?
Access the complete publication on the publisher's website