Serious Infections Among Adult Medicaid Beneficiaries With Systemic Lupus Erythematosus and Lupus Nephritis
Bibliographic record
Abstract
OBJECTIVE: To examine the epidemiology of serious infections, a significant cause of morbidity and mortality in systemic lupus erythematosus (SLE), in a nationwide cohort of SLE and lupus nephritis (LN) patients. METHODS: Using the Medicaid Analytic eXtract database for the years 2000-2006, we identified patients ages 18-64 years who had SLE and the subset who had LN. We ascertained cases of serious hospitalized infections using validated algorithms, and we determined 30-day mortality rates. Poisson regression was used to calculate infection incidence rates and multivariable Cox proportional hazards models were used to calculate hazard ratios (HRs) for the first infection, adjusted for sociodemographic variables, medication use, and an SLE-specific risk adjustment index. RESULTS: We identified 33,565 patients with SLE, 7,113 of whom had LN. There were 9,078 serious infections in 5,078 SLE patients and 3,494 infections in 1,825 LN patients. The infection incidence rate per 100 person-years was 10.8 in the SLE cohort and 23.9 in the LN subcohort. In adjusted models for the SLE cohort, we observed increased risks of infection in men as compared to women (HR 1.33 [95% confidence interval (95% CI) 1.20-1.47]), in blacks as compared to whites (HR 1.14 [95% CI 1.06-1.21]), and in users of glucocorticoids (HR 1.51 [95% CI 1.43-1.61]) and immunosuppressive drugs (HR 1.11 [95% CI 1.03-1.20]) as compared to never users. Hydroxychloroquine users had a reduced risk of infection as compared to never users (HR 0.73 [95% CI 0.68-0.77]). The 30-day mortality rate per 1,000 person-years among those hospitalized with infections was 21.4 in the SLE cohort and 38.6 in the LN subcohort. CONCLUSION: In this diverse, nationwide cohort of SLE patients, we observed a substantial burden of serious infections with many subsequent deaths, particularly among those with LN.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".