Association of the Charlson comorbidity index with mortality in systemic lupus erythematosus
Bibliographic record
Abstract
OBJECTIVE: To investigate whether comorbidity as assessed by the Charlson Comorbidity Index (CCI) is associated with mortality in a long-term followup of systemic lupus erythematosus (SLE) patients. METHODS: Data were collected from 499 SLE patients attending the Lupus Clinic at the McGill University Health Center, Montreal, Quebec, Canada, and 170 SLE patients from the Department of Rheumatology at Lund University Hospital, Lund, Sweden. This included data on comorbidity, demographics, disease activity, the Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index (SDI), and antiphospholipid antibody syndrome (APS). Variables were entered into a Cox proportional hazards survival model. RESULTS: Mortality risk in the Montreal cohort was associated with the CCI (hazard ratio [HR] 1.57 per unit increase in the CCI, 95% confidence interval [95% CI] 1.18-2.09) and age (HR 1.04 per year increase in age, 95% CI 1.00-1.09). The CCI and age at diagnosis were also associated with mortality in the Lund cohort (CCI: HR 1.35, 95% CI 1.13-1.60; age: HR 1.09, 95% CI 1.05-1.12). Furthermore, the SDI was associated with mortality in the Lund cohort (HR 1.40, 95% CI 1.19-1.64), while a wide CI for the estimate in the Montreal cohort prevented a definitive conclusion (HR 1.20, 95% CI 0.97-1.48). We did not find a strong association between mortality and sex, race/ethnicity, disease activity, or APS in either cohort. CONCLUSION: In this study, comorbidity as measured by the CCI was associated with decreased survival independent of age, lupus disease activity, and damage. This suggests that the CCI may be useful in capturing comorbidity for clinical research in SLE.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".