The Impact of Concomitant Medications on the Overall Survival of Patients Treated with Systemic Therapy for Advanced or Metastatic Renal Cell Carcinoma: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Although immune checkpoint inhibitors (ICI) and/or tyrosine kinase inhibitors (TKI) are the standard treatment of advanced unresectable or metastatic renal cell carcinoma (RCC), the impact of concomitant medications remains unclear. We aimed to evaluate the impact of concomitant medications on survival outcomes in patients treated with systemic therapy for advanced unresectable or metastatic RCC. In August 2024, PubMed, Scopus, and Web of Science were queried for studies evaluating concomitant medications in patients with advanced unresectable or metastatic RCC (PROSPERO: CRD42024573252). The primary outcome was overall survival (OS). A fixed- or random-effects model was used for meta-analysis according to heterogeneity. We identified 22 eligible studies (5 prospective and 17 retrospective) comprising 16,072 patients. Concomitant medications included proton pump inhibitors (PPI) (n = 3959), antibiotics (n = 571), statins (n = 5466), renin-angiotensin system inhibitors (RASi) (n = 6615), and beta-blockers (n = 1964). Both concomitant PPI and antibiotics were significantly associated with worse OS in patients treated with ICI (PPI: HR: 1.22, P = .01, and antibiotics: HR: 2.09, P < .001). Concomitant statins, RASi, or beta-blocker were significantly associated with improved OS in patients treated with TKI (statins: HR: 0.81, P = .03, RASi: HR: 0.63, P < .001, beta-blocker: HR: 0.69, P < .001, respectively). In patients treated with ICI, RASi was significantly associated with improved OS (HR: 0.64, P = .02). Concomitant use of antibiotics or PPI with ICI can reduce its oncologic efficacy. Conversely, concomitant statins, RASi, or beta-blockers can enhance the oncologic efficacy of TKI. When initiating systemic therapy for metastatic RCC, it may be important for clinicians to assess baseline co-medications and recognize their possible positive or negative effects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.006 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".