Chronicity of Immune Checkpoint Inhibitor–Associated Inflammatory Arthritis After Immunotherapy Discontinuation: Results From the Canadian Research Group of Rheumatology in Immuno‐Oncology Database
Bibliographic record
Abstract
OBJECTIVE: Immune checkpoint inhibitors (ICIs) improve overall survival (OS) and progression-free survival (PFS) in many types of malignancies but can result in off-target immune-related adverse events including inflammatory arthritis (ICI-associated inflammatory arthritis [ICI-IA]), which can persist even after ICI cessation. We aimed to examine the proportion of patients with ICI-IA who develop chronic ICI-IA and describe characteristics and outcomes associated with chronic ICI-IA. METHODS: We identified patients from the Canadian Research Group of Rheumatology in Immuno-Oncology retrospective cohort who developed de novo ICI-IA with at least three months of follow-up after ICI cessation. Chronic ICI-IA was defined as symptoms or ongoing immunosuppression lasting beyond three months after ICI discontinuation. Acute ICI-IA was defined as resolution of ICI-IA symptoms and discontinuation of immunosuppression within three months of ICI discontinuation. OS and PFS were assessed with Kaplan-Meier curves. Landmark multivariable Cox proportional hazard models for OS and PFS were conducted. RESULTS: The study cohort included 119 patients. A total of 15 patients (13%) had acute ICI-IA, whereas 104 (87%) had chronic ICI-IA. Patients with chronic ICI-IA were more likely to be White and to have polyarthritis at presentation. After adjusting for age, sex, tumor type, stage of cancer, ICI-IA treatment, and time from ICI initiation to ICI-IA onset, patients with chronic ICI-IA had greater PFS from ICI initiation (adjusted hazard ratio 0.27, 95% confidence interval 0.08-0.98; P = 0.046). Adjusted hazard ratio for OS was similar between those with acute versus chronic ICI-IA. CONCLUSION: ICI-IA frequently persists after ICI discontinuation. Chronic ICI-IA is associated with improved PFS, but not OS, as compared to acute ICI-IA.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".