Predictors of Immunotherapy-Induced Immune-Related Adverse Events
Bibliographic record
Abstract
Purpose: We aimed to elucidate predictive factors for the development of immune-related adverse events (iraes) in patients receiving immunotherapies for the management of advanced solid cancers. Methods: This retrospective study involved all patients with histologically confirmed metastatic or inoperable melanoma, non-small-cell lung cancer, or renal cell carcinoma receiving immunotherapy at the Cancer Centre of Southeastern Ontario. The type and severity of iraes, as well as potential protective and exacerbating factors, were collected from patient charts. Results: The study included 78 patients receiving ipilimumab (32%), nivolumab (33%), or pembrolizumab (35%). Melanoma, non-small-cell lung cancer, and renal cell carcinoma accounted for 70%, 22%, and 8% of the cancers in the study population. In 41 patients (53%) iraes developed, with multiple iraes developing in 12 patients (15%). In most patients (70%), the iraes were of severity grade 1 or 2. Female sex [adjusted odds ratio (oradj): 0.094; 95% confidence interval (ci): 0.021 to 0.415; p = 0.002] and corticosteroid use before immunotherapy (oradj: 0.143; 95% ci: 0.036 to 0.562; p = 0.005) were found to be associated with a protective effect against iraes. In contrast, a history of autoimmune disease (oradj: 9.55; 95% ci: 1.34 to 68.22; p = 0.025), use of ctla-4 inhibitors (oradj: 6.25; 95% ci: 1.61 to 24.25; p = 0.008), and poor kidney function of grade 3 or greater (oradj: 10.66; 95% ci: 2.41 to 47.12; p = 0.025) were associated with a higher risk of developing iraes. A Hosmer–Lemeshow goodness-of-fit test demonstrated that the logistic regression model was effective at predicting the development of iraes (chi-square: 1.596; df = 7; p = 0.979). Conclusions: Our study highlights several factors that affect the development of iraes in patients receiving immunotherapy. Although future studies are needed to validate the resulting model, findings from the study can help to guide risk stratification, monitoring, and management of iraes in patients given immunotherapy for advanced cancer.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".