Impact of concurrent medications on outcomes with PD1/PD-L1 inhibitors for metastatic urothelial carcinoma.
Bibliographic record
Abstract
435 Background: The impact of concurrent medications (meds) on outcomes with PD1/PD-L1 inhibitors in metastatic urothelial carcinoma (mUC) is unclear. We investigated whether candidate concurrent meds (NSAIDS [N], metformin [M], antibiotics [A], statins [S] and corticosteroids [C]) have an association with outcomes in mUC patients (pts) receiving a PD1/PD-L1 inhibitor. We hypothesized that A and C compromise outcomes, while N, M and S improve outcomes. Methods: Data from mUC pts who received PD1/PD-L1 inhibitors at the Dana-Farber Cancer Institute (DFCI) was obtained. The concurrent medication was required to be administered within 1 month before starting to anytime during PD1/PD-L1 inhibitor therapy. A Cox regression analysis was done to study the association of variables with response and survival. Results: Data was available for 101 pts with mUC who received atezolizumab [n = 52], pembrolizumab [n = 39], nivolumab [n = 9] and durvalumab [n = 1]. Prior platinum had been administered in 74 pts (73.2%), 25 were chemonaive (24.8%) and prior therapy status was unknown in 2 pts (2%). The concurrent meds were N (n = 30), M (n = 7), A (n = 26), S (n = 33) and C (n = 12). The median survival was 57.9 weeks. Response was seen in 26 pts [25.7%]. A was associated with a lower probability of response (11.5%) than those not on A (30.7%), and worse survival (HR = 1.93, 95% CI 1.93 – 3.42, P = 0.024). Pts who received neither A nor C, one of them or both had a response rate (RR) of 30.6%, 20% and 0%, and median survival of 65.3, 53.1 and 14.9 weeks, respectively (HR = 3.02, 95% CI = 1.34-6.83, p = 0.027). Pts who did not receive N, M and S (n = 52) exhibited a median OS of 39.6 weeks, while those who received ≥1 of these meds (n = 49) exhibited a median survival of 160.3 weeks (p = NS). The study is limited by the retrospective design and modest sample size. Conclusions: In this hypothesis-generating study, concurrent antibiotics or corticosteroids compromised outcomes in mUC pts receiving a PD1/PD-L1 inhibitor and receiving both further compromised outcomes. The numerically higher survival with concurrent N, M or S did not attain statistical significance, but requires further study in larger datasets.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".