Multisystem immune-related adverse events from dual agent immunotherapy use.
Bibliographic record
Abstract
2635 Background: Immune-related adverse events (irAEs) are commonly seen in cancer patients who undergo immunotherapy treatment. Increasingly, there has been recognition that multiple different irAEs can occur during or/and after a single course of immunotherapy treatment, known as multisystem irAEs, though much remains unknown. The primary objective of this study was to assess for rate of multisystem irAEs in patients who received dual agent ipilimumab and nivolumab for cancer treatment. Methods: A retrospective cohort review was completed that included all cancer patients seen at Juravinski Cancer Centre who received at least one dose of ipilimumab and nivolumab from January 1, 2018 to May 31, 2022. Patient characteristics, cancer types, and number and types of irAEs were recorded. Multivariate logistic regressions were also completed comparing those who developed multisystem irAEs, single irAE, and no irAE. Results: A total of 123 patients were included in this study. Median age was 59 years old, with 69% male. 72 out of 123 patients (59%) had melanoma, 50/123 (41%) had renal cell carcinoma (RCC), and 1/123 (1%) had breast cancer. At least one irAE was seen in 72% of patients who were treated with ipilimumab and nivolumab, and most irAEs occurred during the combination phase (89% of first irAE, 59% of second irAE, 75% of third irAE, and 50% of fourth irAE). Multisystem irAEs were seen in 40% of the overall cohort. The most common irAE type was dermatitis (22%), followed by colitis (19%), hepatitis (17%), and thyroiditis (15%). 30 out of 89 patients (34%) who experienced irAE(s) required hospital admission for inpatient treatment of irAE(s). Patients who experienced multisystem irAEs had significantly higher rates of response to immunotherapy irAE (p < 0.001) and improved overall survival (OS) than those who experienced single or no irAE. Conclusions: From our single-centre cohort study, there appeared to be a higher than previously quoted number of patients who experienced irAEs after receiving ipilimumab and nivolumab, with majority of them developing multisystem irAEs. Many patients experienced debilitating associated symptoms and hospital admission relating to irAEs. It is important for both the counselling and consent of patients, and for patient education, to discuss the possibility of multiple irAEs prior to dual immunotherapy initiation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".