Impact of concomitant medications on the oncologic efficacy of systemic therapy in patients with advanced or metastatic urothelial carcinoma: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Immune checkpoint inhibitors (ICI) and chemotherapy, including antibody-drug conjugates, are widely used for the treatment of patients with advanced unresectable or metastatic urothelial carcinoma (UC). The majority of elderly patients receive concomitant medications to address various comorbidities. We aimed to evaluate the impact of concomitant medications on oncological outcomes in patients with advanced unresectable or metastatic UC treated with systemic therapy. MATERIAL & METHODS: In August 2024, three datasets were queried for studies evaluating concomitant medications in patients with advanced unresectable or metastatic UC. The review protocol was registered in PROSPERO (CRD42024547335). The primary outcome was overall survival (OS). A fixed- or random-effects model was used for meta-analysis depending on the heterogeneity. RESULTS: We identified 16 eligible studies (3 prospective and 13 retrospective) comprising 4,816 patients. Most reported concomitant medications included proton pump inhibitors (PPIs), antibiotics, steroids, and opioids. The use of concomitant PPIs, antibiotics, steroids or opioids during ICI therapy was associated with worsened OS (PPIs: HR: 1.43, 95% CI: 1.31-1.57, p < 0.001; antibiotics: HR: 1.2, 95% CI: 1.04-1.38, p = 0.01; steroids: HR: 1.45, 95% CI: 1.25-1.67, p < 0.001; and opioids: HR: 1.74, 95% CI: 1.46-2.07, p < 0.001). Concomitant use of antibiotics during chemotherapy did not impact OS (HR: 1.01, 95% CI: 0.67-1.51). CONCLUSIONS: When treating advanced unresectable or metastatic UC with ICI therapy, we need to pay attention to concomitant medications, such as PPIs and antibiotics to avoid reducing the efficacy of ICI therapy. The mechanism of action of these drugs on ICI efficacy requires further examination.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.039 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".