Validation of an International Classification of Disease, 10th revision coding adaptation for the Charlson Comorbidity Index in United States healthcare claims data
Bibliographic record
Abstract
PURPOSE: An International Classification of Disease (ICD-10) Charlson Comorbidity Index (CCI) adaptation had not been previously developed and validated for United States (US) healthcare claims data. Many researchers use the Canadian adaption by Quan et al (2005), not validated in US data. We sought to evaluate the predictive validity of a US ICD-10 CCI adaptation in US claims and compare it with the Canadian standard. METHODS: Diverse patient cohorts (rheumatoid arthritis, hip/knee replacement, lumbar spine surgery, acute myocardial infarction [AMI], stroke, pneumonia) in the IBM® MarketScan® Research Databases were linked with the IBM MarketScan Mortality file. Predictive performance was measured using c-statistics for binary outcomes (1-year and postoperative mortality, in-hospital complications) and root mean square prediction error (RMSE) for continuous outcomes (1-year all-cause medical costs, index hospitalization costs, length of stay [LOS]), after adjusting for age and sex. C-statistics were compared by the method of DeLong and colleagues (1988); RMSEs, by resampling. RESULTS: C-statistics were generally high (≥ ~ 0.8) for mortality but lower for in-hospital complications (~0.6-0.7). RMSEs for costs and hospitalization LOS were relatively large and comparable to standard deviations. Results were similar overall between the US and Canadian adaptations, with relative differences typically <1%. CONCLUSIONS: This US-based coding adaptation and a previously published Canadian adaptation resulted in similar predictive ability for all outcomes evaluated but may have different construct validity (not evaluated in our study). We recommend using adaptations specific to the country of data origin based on good research practice.
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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 | 0.057 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".