Long-term mepolizumab treatment reduces relapse rates in super-responders with eosinophilic granulomatosis with polyangiitis
Bibliographic record
Abstract
BACKGROUND: The mainstay of treatment for eosinophilic granulomatosis with polyangiitis (EGPA) is systemic corticosteroid therapy; some patients also receive intravenous immunoglobulins, other immunosuppressive agents, and biologics. Mepolizumab, an anti-interleukin-5 monoclonal antibody, induces remission and decreases the daily corticosteroid dose; however, the clinical efficacy of mepolizumab in EGPA and the prognosis with long-term treatment with this drug are unknown. METHODS: Seventy-one EGPA patients were treated at Hiratsuka City Hospital, Japan, between April 2018 and March 2022. We administered mepolizumab for a mean of 2.8 ± 1.7 years to 43 patients in whom remission could not be induced by conventional treatment. After excluding 18 patients who had received mepolizumab for less than 3 years, we classified 15 patients into a "super-responder group" (the daily dose of corticosteroids or other immunosuppressant could be decreased, or the interval between IVIG treatments could be prolonged) and 10 patients into a "responder group" (neither of these changes was achievable). Eosinophil numbers, serum IgG levels, daily doses of corticosteroids and other immunosuppressants, Birmingham Vasculitis Activity Score (BVAS), and relapse frequency before and after mepolizumab initiation were determined. RESULTS: Blood eosinophil count at diagnosis and the lowest serum IgG level before mepolizumab treatment were significantly higher in super-responders than in responders (p < 0.05). In super-responders, the prednisolone dose at last visit on mepolizumab treatment was lower than that before treatment (p < 0.01) and lower than that at last visit in the responders (p < 0.01). In both groups, peripheral blood eosinophil numbers and BVAS were lower after starting mepolizumab than before (p < 0.01). BVAS before mepolizumab (p < 0.05) and at last visit (p < 0.01) were lower in super-responders than in responders. Relapse rates every year after the start of mepolizumab were lower in super-responders than in responder groups (p < 0.01). In super-responders, relapse rates were lower during the 3 years following mepolizumab initiation (p < 0.01) and at last visit (p < 0.01) were significantly lower than after 1 year of treatment. CONCLUSION: Mepolizumab treatment of super-responders sustainably reduced the relapse rate.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".