Chronic immune-related adverse events in patients with cancer receiving immune checkpoint inhibitors: a systematic review
Bibliographic record
Abstract
Immune-related adverse events (irAEs) are toxicities resulting from use of immune checkpoint inhibitors (ICIs). These side effects persist in some patients despite withholding therapy and using immunosuppressive and immune-modulating agents. Little is known about chronic irAEs and they are felt to be rare. We performed a systematic review to characterize non-endocrine chronic irAEs reported in the literature and describe their management. Ovid MEDLINE and Embase databases were searched for reports of adult patients with solid cancers treated with ICIs who experienced chronic (>12 weeks) non-endocrine irAEs. Patient, treatment and toxicity data were collected. Of 6843 articles identified, 229 studies including 323 patients met our inclusion criteria. The median age was 65 (IQR 56-72) and 58% were male. Most patients (75%) had metastatic disease and the primary cancer site was melanoma in 43% and non-small cell lung cancer in 31% of patients. The most common ICIs delivered were pembrolizumab (24%) and nivolumab (37%). The chronic irAEs experienced were rheumatological in 20% of patients, followed by neurological in 19%, gastrointestinal in 16% and dermatological in 14%. The irAE persisted for a median (range) of 180 (84-2370) days and 30% of patients had ongoing symptoms or treatment. More than half (52%) of patients had chronic irAEs that persisted for >6 months. The ICI was permanently discontinued in 60% of patients and 76% required oral and/or intravenous steroids. This is the first systematic review to assess and report on moderate/severe chronic non-endocrine irAEs after treatment with ICI in the literature. These toxicities persisted for months-years and the majority required discontinuation of therapy and initiation of immunosuppression. Further research is needed to better understand chronic irAEs, which hold potential substantial clinical significance considering the expanded use of ICIs and their integration into the (neo)adjuvant settings.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.008 | 0.010 |
| 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) | 0.003 | 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".