Immune checkpoint inhibitor associated vasculitis and polymyalgia rheumatica: a case series and systematic review
Bibliographic record
Abstract
BACKGROUND: Immune checkpoint inhibitors (ICIs) have been associated with immune-related adverse events (irAEs), including ICI associated vasculitis (ICI-vasculitis) and ICI associated PMR (ICI-polymyalgia rheumatica (PMR)-like syndromes). We sought to describe the characteristics of ICI-vasculitis and ICI-PMR in individuals treated with ICIs through a systematic review and local case series. METHODS: We searched MEDLINE and Embase from inception to July 2023 for cases of ICI-vasculitis and ICI-PMR in ICI-treated individuals. Our series included cases obtained from clinicians at the Universities of Toronto, McMaster, and Western in Ontario, Canada. RESULTS: One hundred and forty-four patients were identified: 130 from the systematic review in 76 articles and 14 from our case series. This included 73 patients with ICI-vasculitis (14 large vessel, 7 ANCA-associated, 52 other) and 71 with ICI-PMR. Ninety-five per cent of patients were treated with glucocorticoids and 17% received disease-modifying antirheumatic drugs (DMARDs). Among 4 individuals with vasculitis who continued the ICI, two remitted. Of five who paused and restarted ICIs, one experienced a recurrence. Among four individuals with vasculitis who continued ICIs, two remitted. All 11 ICI-PMR cases that continued ICIs remitted; among 3 patients who paused and restarted two did not relapse and one had a relapse that improved with an increase in their dose of prednisone. CONCLUSION: ICI-vasculitis and ICI-PMR are underrecognized complications of ICIs. Glucocorticoids are effective for ICI-vasculitis and ICI-PMR. It is unclear if ICIs can be safely continued or restarted after remission of ICI-vasculitis or ICI-PMR. Limited data suggests ICIs may be resumed in individuals with non-severe ICI-vasculitis or ICI-PMR.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".