Risk Factors and Incidence of Serious Infections in Patients With Systemic Lupus Erythematosus Undergoing Rituximab Therapy
Bibliographic record
Abstract
Objective To evaluate the risk and protective factors of serious infection (SI) in patients with systemic lupus erythematosus (SLE) within 180 days of rituximab (RTX) treatment. Methods Patients with SLE treated with RTX were analyzed. SI was defined as any infectious disease requiring hospitalization. The clinical characteristics, laboratory profiles, medications, and incidence rate (IR) are presented. Multivariate Cox proportional hazards models and Kaplan-Meier analysis for risk factors of SI were performed. Results A total of 174 patients with SLE receiving RTX treatment were enrolled. The overall IR of SIs was 51.0/100 patient-years (PYs). Pneumonia (30.4/100 PYs), followed by soft tissue infections, intra-abdominal infections, andPneumocystis jirovecipneumonia (all 6.1/100 PYs) were the leading types of SIs. Twelve patients died during the 180-day follow-up (crude mortality rate: 14.6/100 PYs). Chronic kidney disease (CKD), defined as an estimated glomerular filtration rate < 60 mL/min/1.73 m2(hazard ratio [HR] 2.88, 95% CI 1.30-6.38), and a background prednisolone (PSL) equivalent dosage ≥ 15 mg/day (HR 3.50, 95% CI 1.57-7.78) were risk factors for SIs among all patients with SLE. Kaplan-Meier analysis confirmed the risk of SI for patients with SLE with CKD and a background PSL equivalent dosage ≥ 15 mg/day (log-rankP= 0.001 and 0.02, respectively). Hydroxychloroquine (HCQ) reduced the risk of SIs in patients with SLE (HR 0.35, 95% CI 0.15-0.82; log-rankP= 0.003). Conclusion SI was prevalent in patients with SLE after RTX treatment. Patients with SLE with CKD and high-dose glucocorticoid use required constant vigilance. HCQ may reduce the risk of SI among patients with SLE administered RTX.
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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 | 0.000 | 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 | 0.001 |
| 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".