Integrating Multimorbidity Assessment into Rheumatology Care: Prognostic Role of the Charlson Comorbidity Index in Systemic Lupus Erythematosus
Bibliographic record
Abstract
Background/Objectives: Systemic lupus erythematosus (SLE) is a chronic autoimmune disease with significant morbidity and premature mortality. As patients with SLE often suffer from multiple comorbid conditions, evaluating the overall health burden is critical for improving risk stratification and long-term outcomes. The Charlson Comorbidity Index (CCI) is a widely used tool for quantifying the burden of comorbidity. This systematic review and meta-analysis aimed to assess the prognostic value of the CCI for all-cause mortality in adult patients with SLE. Methods: We conducted a systematic review and meta-analysis in accordance with the PRISMA 2020 guidelines. Three databases (PubMed, Embase, and Web of Science) were searched up to May 2025. Three studies (n = 1175 participants) met the inclusion criteria. Eligible studies included adult SLE populations that evaluated the comorbidity burden using the CCI and reported all-cause mortality. Study characteristics and effect sizes were extracted, and a fixed-effects model (after considering both random- and fixed-effects approaches) was applied to calculate pooled odds ratios (ORs). Risk of bias was assessed using the Newcastle–Ottawa Scale. Results: Three observational studies (n = 1175 participants) met the inclusion criteria. All demonstrated a significant association between higher CCI scores and increased all-cause mortality. The pooled OR for mortality in patients with a high comorbidity burden was 3.92 (95% CI: 2.74–5.60), with no observed heterogeneity (I2 = 0%). The risk of bias was moderate to high across all studies. Conclusions: Multimorbidity, as measured by the CCI, is a strong independent predictor of mortality in SLE. Integrating comorbidity assessment into rheumatology care may enhance prognostic evaluation, guide personalized treatment, and support interdisciplinary management strategies for patients with complex disease profiles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".