Use of a self-report-generated Charlson Comorbidity Index for predicting mortality
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Published on: January 8, 2020
Saima Chaudhry1, Lei Jin, David Meltzer
1University of Chicago, Chicago, Illinois, USA. schaudhr@nshs.edu
Insights
Self-reported Charlson Comorbidity Index effectively predicts mortality, comparable to administrative data. While administrative data offers slight predictive improvements, patient self-report is a viable alternative for risk adjustment.
Area of Science:
- Medical informatics
- Epidemiology
- Health services research
Background:
- Charlson Comorbidity Index (CCI) is crucial for risk adjustment but typically uses administrative data.
- Limitations of administrative data necessitate exploring patient self-report as an alternative for CCI construction.
- Limited evidence exists on the predictive accuracy of self-reported CCI for mortality.
Purpose of the Study:
- To evaluate if self-reported CCI predicts 1-year mortality.
- To compare the performance of self-reported CCI against indices derived from administrative data.
- To assess if study-specific weights improve model fit compared to original Charlson weights.
Main Methods:
- Surveyed 7761 patients, collecting self-reported comorbidities and administrative data.
- Constructed six CCI variations using original and study-specific weights across three data sources (index hospitalization ICD-9CM, 1-year look-back ICD-9CM, self-report).
- Developed multivariate models to predict 1-year mortality, log total costs, and log length of stay.
Main Results:
- All six CCI measures demonstrated predictive ability for 1-year mortality (AUC 0.70-0.77).
- Models showed similar predictive power irrespective of the CCI source or weighting scheme when including age and gender.
- Minor improvements in model fit were observed with administrative data over self-report and study-specific weights over original weights.
Conclusions:
- Self-reported Charlson indices offer comparable 1-year mortality prediction to those derived from administrative data.
- Administrative data may provide marginal enhancements in predictive accuracy.
- Patient self-report serves as a practical and effective alternative for CCI when administrative data is not readily accessible.
Background:
The Charlson Comorbidity Index, a popular tool for risk adjustment, often is constructed from medical record abstracts or administrative data. Limitations in both sources have fueled interest in using patient self-report as an alternative. However, little data exist on whether self-reported Charlson Indices predict mortality.
Objectives:
We sought to determine whether a self-reported Charlson Index predicts mortality, its performance relative to indices derived from administrative data, and whether using study-specific weights instead of Charlson's original weights enhances model fit.
Methods:
We surveyed 7761 patients admitted to a university medical service over the course of 4 years and extracted their administrative data. We constructed 6 different Charlson indices by using 2 weighting schemes (original Charlson weights and study-specific weights) and 3 different datasources (ICD-9CM data for index hospitalization, ICD-9CM data with a 1-year look-back period, and patient self-report of comorbidities.) Multivariate models were constructed predicting 1-year mortality, log total costs, and log length of stay.
Results:
The 6 measures of the Charlson index all predicted 1-year mortality. Models with age and gender, with or without diagnosis-related group, had approximately the same predictive power regardless of which of the 6 Charlson indices were used. Nevertheless, there were small improvements in model fit using administrative data versus self-report, or study-specific versus original weights. All models obtained areas under the receiver operating curve of 0.70 to 0.77.
Conclusions:
Overall, self-reported Charlson indices predict 1-year mortality comparably with indices based on administrative data. Administrative data may offer some small improvements in predictive ability and may be preferred when readily available.