Incidence of<i>Pneumocystis jirovecii</i>Pneumonia and Prophylaxis-Associated Adverse Events Among Patients With Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: pneumonia (PJP) is an opportunistic infection that may affect patients with systemic lupus erythematosus (SLE). The objective of this project was to describe the incidence of PJP among patients with SLE. METHODS: A retrospective cohort analysis of the TriNetX database was conducted. Included patients had ≥ 2 International Classification of Diseases, 9th or 10th revision, Clinical Modification (ICD-9-CM/ICD-10-CM) codes for SLE separated by at least 30 days and were new users of mycophenolate mofetil (MMF) and/or cyclophosphamide (CYC). The incidence of PJP over the first 6 months of therapy was calculated; adverse events were assessed using incidence rate ratios (IRR) and Cox proportional hazards regressions. RESULTS: A total of 6017 patients with SLE were identified. Most were female (n = 5176, 86%) and Black or African American (n = 2138, 35.5%). Induction medications included MMF (n = 5208, 86.6%), CYC (n = 505, 8.4%), or both (n = 304, 5.1%); the most common PJP prophylaxis was trimethoprim-sulfamethoxazole (n = 1126, 18.7%). Five PJP cases were identified over 2752 person-years (PYs), one of whom received PJP prophylaxis, for an incidence rate of 1.8 cases/1000 PYs. In the adjusted analysis, patients who received prophylaxis had a higher risk of neutropenia (hazard ratio [HR] 2.5, 95% CI 1.4-4.4), leukopenia (HR 1.9, 95% CI 1.3-2.8), nephropathy (HR 1.7, 95% CI 1.4-2.1), and hyperkalemia (HR 1.4, 95% CI 0.9-2.0). CONCLUSION: PJP rarely affects patients with SLE undergoing therapy with MMF and/or CYC; prophylaxis against PJP is associated with adverse events. The majority of patients with SLE and PJP had structural lung disease. These data do not support universal prescribing of PJP prophylaxis for patients with SLE without lung disease.
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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 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".