Conditional immune adverse event rate in urothelial and renal cell carcinoma patients treated with immune checkpoint inhibitors.
Bibliographic record
Abstract
481 Background: Immune checkpoint inhibitors (ICIs) are associated with immune-related adverse events (irAEs). While the incidence and prevalence of irAEs have been well characterized in the literature, much less is known about the cumulative incidence (CI) rate of irAEs. We sought to evaluate the CI of irAEs in metastatic urothelial carcinoma (mUC) and metastatic renal cell carcinoma (mRCC) patients (pts) treated with ICIs. Methods: We identified a cohort of mUC and mRCC pts who received ICIs at DFCI. irAEs were classified using CTCAE v.5.0 guidelines. The CI rate was a defined measure that accounted for elapsed time since treatment initiation and estimated the risk of irAE development conditioned on time elapsed without experiencing an irAE, accounting for the competing risk of death. Incidence and CI of irAEs at each monthly landmark time was calculated. Prognostic factors of irAE were assessed using the Fine and Gray method. Results: A total of 470 pts was treated with ICIs between July 2013 and October 2018 [mUC: 199 (42.3%); mRCC: 271 (57.7%)]. 341 (72.6%) pts received ICI monotherapy, 86 (18.3%) received ICIs in combination with targeted therapies, and 43 (9.2%) received a combination of two ICIs. Overall, 186 pts (39.5%) experienced any irAE at any time point. Common irAEs included hypothyroidism (n=42 [22.6%]), skin (n=36 [19.4%]), colitis (n=35 [18.8%]), transaminitis (n=32 [17.2%]), and pneumonitis (n=14 [7.5%]). The risk of developing an irAE over time was as follows: 33.5% if no irAE within the 1st month(mo), 27.3% if no irAE in 3mo, 18.8% if no irAE in 6mo, and 16.4% if no irAE by 12mo. No difference was observed in CI based on type of cancer (mUC vs mRCC) or agent (PD1 vs. PD-L1). Multivariable analysis showed that ICI combined with ICI or other agents vs. ICI monotherapy (p<0.001), firstline therapy (p=0.013) and PD-1 vs. PD-L1 inhibitors (p=0.008) were statistically correlated with the development of irAEs. Conclusions: This study quantitates the incidence of developing irAEs with ICI conditioned on time elapsed without irAE development. Although the incidence of irAEs decreased over time on therapy, irAEs require continuous vigilant monitoring because of the long tail in its incidence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".