Comparison of the Elixhauser and Charlson/Deyo Methods of Comorbidity Measurement in Administrative Data
Bibliographic record
Abstract
BACKGROUND: Comorbidity risk adjustment methods have been used widely with administrative data, and the Charlson/Deyo method is perhaps the most commonly used in the literature. However, a new method defined by Elixhauser et al. has been introduced recently and could be superior, although it has not been validated widely. OBJECTIVES: We compared the Charlson/Deyo and Elixhauser methods using Canadian administrative data on patients with myocardial infarction (MI). RESEARCH DESIGN: We conducted a historical cohort study. SUBJECTS: We used administrative hospital discharge data from a large Canadian city for all cases with acute MI coded as most responsible diagnosis between January 1, 1995, and March 31, 2001. MEASURES: We used each of the 2 methods to define comorbidity variables based on the International Classification of Diseases, 9th Revision, Clinical Modification codes present in each case record. We then compared 2 models predicting in-hospital mortality based on presence or absence of the variables defined by each of the methods. Frequency tables were produced and c-statistics and changes in -2 log likelihood (-2LogL) were calculated. We also visually assessed model performance by plotting observed and expected percentages of death for increasing risk categories defined by the 2 models. RESULTS: The Elixhauser model outperformed the Charlson/Deyo model in predicting mortality, with higher c-statistic values (0.793 vs. 0.704). Superior performance of the Elixhauser method is confirmed when plotting the expected and observed risks of death across groupings of increasing risk, in which the Elixhauser method yields a wider range of predicted and observed probabilities of death across groupings (2.5%-33%) than does the Charlson/Deyo method (5%-25%). CONCLUSIONS: The Elixhauser comorbidity measurement method performs better than the widely used Charlson/Deyo method in the Canadian acute MI cases studied.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.091 | 0.168 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".