Comorbidity Development and Mortality During 10 Years of Follow-up in a Danish Nationwide Cohort of 3178 Patients With Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: We estimated the incidence of various comorbidities and mortality in a large national cohort of patients with newly diagnosed systemic lupus erythematosus (SLE) compared with matched population comparators. METHODS: All patients aged ≥ 18 years with a first-time diagnosis of SLE in the Danish National Patient Register from 1996 to 2018 were included (n = 3178). For each patient with SLE, 19 age- and sex-matched population comparators were identified (n = 60,090). Comorbidity diagnoses and mortality data were retrieved from national Danish registries. For comorbidities and mortality, incidence rates per 1000 person-years and age- and sex-adjusted incidence rate ratios (IRRs) were estimated during the following time intervals following SLE diagnosis: year 1, year 2, years 3-5, and years 6-10. RESULTS: A total of 84.3% of patients with SLE and general population comparators were female, and the mean age at baseline was 47.4 years. Patients with SLE had a significantly increased risk of developing comorbidities during follow-up. The following highest first-year IRRs were seen for typical features of SLE: coagulopathy, renal disease, and pulmonary embolism. Renal disease, coagulopathy, and osteoporosis had the highest IRRs during the 5-10 years of follow-up. Patients with SLE had 4.1-times increased mortality risk during the first year of follow-up compared with matched population controls, and 1.6- to 2.1-times increased mortality risk during subsequent follow-up periods. CONCLUSION: The study provides a comprehensive overview of risk estimates and the timing of comorbidities and mortality in a nationwide cohort of adult patients with SLE. These data may be a valuable reference for upcoming works on evaluating comorbidity 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".