Developing an adapted Charlson comorbidity index for ischemic stroke outcome studies
Bibliographic record
Abstract
BACKGROUND: The Charlson comorbidity index (CCI) is commonly used to adjust for patient casemix. We reevaluated the CCI in an ischemic stroke (IS) cohort to determine whether the original seventeen comorbidities and their weights are relevant. METHODS: We identified an IS cohort (N = 6988) from the Ontario Stroke Registry (OSR) who were discharged from acute hospitals (N = 100) between April 1, 2012 and March 31, 2013. We used hospital discharge ICD-10-CA data to identify Charlson comorbidities. We developed a multivariable Cox model to predict one-year mortality retaining statistically significant (P < 0.05) comorbidities with hazard ratios ≥1.2. Hazard ratios were used to generate revised weights (1-6) for the comorbid conditions. The performance of the IS adapted Charlson comorbidity index (ISCCI) mortality model was compared to the original CCI using the c-statistic and continuous Net Reclassification Index (cNRI). RESULTS: Ten of the 17 Charlson comorbid conditions were retained in the ISCCI model and 7 had reassigned weights when compared to the original CCI model . The ISCCI model showed a small but significant increase in the c-statistic compared to the CCI for 30-day mortality (c-statistic 0.746 vs. 0.732, p = 0.009), but no significant increase in c-statistic for in-hospital or one-year mortality. There was also no improvement in the cNRI when the ISCCI model was compared to the CCI. CONCLUSIONS: The ISCCI model had similar performance to the original CCI model. The key advantage of the ISCCI model is it includes seven fewer comorbidities and therefore easier to implement in situations where coded data is unavailable.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".