Real-world utilization and safety of ipilimumab plus nivolumab (I+N) in metastatic renal cell carcinoma (mRCC) patients: Results from the Canadian Kidney Cancer Information System (CKCis).
Bibliographic record
Abstract
633 Background: I+N is now standard of care for first line treatment of intermediate/poor risk mRCC patients (pts). Real world data is vital to understand drug usage, toxicity and outcomes in non-trial pts. This project describes the amount and tolerability of treatment delivered including discontinuation rates, reasons for discontinuation and outcomes from the CKCis database. Methods: Pts in CKCis, a prospective Canadian database from 15 academic centers, who received first line I+N were included. The number of doses of I+N, number of pts who received single-agent nivolumab (N) and duration of single agent N were determined. Reasons for treatment discontinuation, including the rate, type, and grade of toxicities were identified. Efficacy outcomes included time to failure (TTF – time to progression, death, or second line therapy), overall response rate (ORR) and overall survival (OS). Results: The cohort consists of 182 pts. Median age was 63 yrs, 71% had clear cell histology, 11% were on a clinical trial, the IMDC risk distribution was 5% good, 63% intermediate, 32% poor. Median follow up was 8.8 m. All 4 I+N doses were received by 30% of pts of which 78% went on to receive single-agent N. Less than 4 doses of I+N were received by 70% of pts of which 28% went on to receive single-agent N. The median time on single agent N was 5.7 m. In the entire cohort, 21% of patients discontinued therapy due to toxicity. The most common toxicity events were colitis (56% of all events), pneumonitis (19%), and hepatitis (8%). There were no toxicity-related deaths. Median OS has not been reached (22 events to date). Median TTF was 12.4 m. ORR was 32% (5% complete responses). 26% of pts received second line treatment, the most common being sunitinib in 79%. Conclusions: In this real world cohort, the majority of mRCC pts did not receive all 4 doses of I+N, contrasting with clinical trial reporting, yet many of these pts went on to receive single agent N. Discontinuation rates due to toxicity were similar to those reported in CheckMate 214. Further follow up is ongoing and efficacy outcomes analyzed on the basis of treatment quantity/duration will be presented.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".