Anakinra for corticosteroid-dependent and colchicine-resistant pericarditis: The IRAP (International Registry of Anakinra for Pericarditis) study
Bibliographic record
Abstract
Aims Novel therapies are needed for recurrent pericarditis, particularly when corticosteroid dependent and colchicine resistant. Based on limited data, interleukin-1 blockade with anakinra may be beneficial. The aim of this multicentre registry was to evaluate the broader effectiveness and safety of anakinra in a ‘real world’ population. Methods and results This registry enrolled consecutive patients with recurrent pericarditis who were corticosteroid dependent and colchicine resistant and treated with anakinra. The primary outcome was the pericarditis recurrence rate after treatment. Secondary outcomes included emergency department visits, hospitalisations, corticosteroid use and adverse events. Among 224 patients (46 ± 14 years old, 63% women, 75% idiopathic), the median duration of disease was 17 months (interquartile range 9–33). Most patients had elevated C-reactive protein (91%) and pericardial effusion (88%). After a median treatment of 6 months (3–12), pericarditis recurrences were reduced six-fold (2.33–0.39 per patient per year), emergency department admissions were reduced 11-fold (1.08–0.10 per patient per year), hospitalisations were reduced seven-fold (0.99–0.13 per patient per year). Corticosteroid use was decreased by anakinra (respectively from 80% to 27%; P < 0.001). No serious adverse events occurred; adverse events consisted mostly of transient skin reactions (38%) at the injection site. Adverse events led to discontinuation in 3%. A full-dose treatment duration of over 3 months followed by a tapering period of over 3 months were the therapeutic schemes associated with a lower risk of recurrence. Conclusion In patients with recurrent pericarditis, anakinra appears efficacious and safe in reducing recurrences, emergency department admissions and hospitalisations.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".