Rituximab-associated Vasculitis Flare: Incidence, Predictors, and Outcome
Bibliographic record
Abstract
OBJECTIVE: To report the incidence, predictors, and outcome of rituximab (RTX)-associated autoimmune disease flare. METHODS: We conducted a retrospective study in a tertiary referral center from 2005 to 2015. Disease flare was defined as the onset of a new organ involvement or worsening of autoimmune disease within 4 weeks following RTX. RESULTS: Among the 185 patients, we identified 7 disease flares (3.4%). All were due to type II mixed cryoglobulinemia vasculitis. Vasculitis flare occurred after a median time of 8 days (range 2-16) following RTX infusion and included acute kidney insufficiency (n = 7), purpura with cutaneous (n = 7), gastrointestinal (GI) tract involvement (n = 4), and myocarditis (n = 1). Patients with RTX-associated cryoglobulinemia vasculitis flare had these conditions more frequently: renal involvement (p = 0.0008), B cell lymphoproliferation (p = 0.015), higher level of cryoglobulin (2.1 vs 0.4 g/l, p = 0.0004), and lower level of C4 (0.02 vs 0.05, p = 0.023) compared to patients without flare after RTX (n = 43). Four patients (57%) died after a median time of 3.3 months. The 1-year survival rate was poorer in patients with vasculitis flare after RTX compared to their negative counterpart [43% (95% CI 18-100) vs 97% (95% CI 92-100), p < 0.001]. Immunofluorescence analysis of kidney biopsy in patients with worsening RTX-associated vasculitis highlighted the presence of RTX-, IgM-, and IgG1-positive staining of endomembranous deposits and thrombi within kidney lesions. CONCLUSION: RTX-associated cryoglobulinemia vasculitis flare is associated with high mortality rate. We provided evidence that kidney lesions are due to immune complex deposition and to glomerular obstruction by cryoglobulinemia and RTX.
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 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 | 0.000 |
| 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) | 0.001 | 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".