RESUMO
BACKGROUND: Few studies have investigated the safety and efficacy of anti-PD-(L)1 antibodies in metastatic urothelial carcinoma (mUC) in daily clinical practice. Knowledge about the influence of baseline clinical and analytical factors on therapy outcomes is scarce. PATIENTS AND METHODS: We conducted a multicenter retrospective study involving 119 previously treated or untreated mUC patients under anti-PD-(L)1 therapy in a real-world scenario. The objectives of this study were to confirm the safety and efficacy of anti-PD-(L)1 monotherapy and to identify pretreatment factors influencing therapy outcomes. In addition, an independent prognostic model for overall survival (OS) was developed and internally validated. RESULTS: Median OS was 7.8 months [95% confidence interval (CI), 5.4-10.4], median progression-free survival (PFS) was 2.80 months (95% CI, 2.4-3.4), disease control rate (DCR) was 40% (95% CI, 31-49), and overall response rate (ORR) was 24% (95% CI, 15-31). Presence of peritoneal metastases was associated with poor OS [hazard ratio (HR) = 2.40, 95% CI, 1.08-5.33; P = 0.03]. Use of proton-pump inhibitors (PPI) was associated with poor OS (HR = 1.83, 95% CI, 1.11-3.02; P = 0.02) and PFS (HR = 1.94, 95% CI, 1.22-3.09; P = 0.005), and lower DCR (OR = 0.38, 95% CI, 0.17-0.89; P = 0.03) and ORR (OR = 0.18, 95% CI, 0.02-1.60; P = 0.002). The three risk category prognostic model developed included Eastern Cooperative Oncology Group performance status, PPI use, albumin level, presence of liver metastases, and presence of peritoneal metastases variables and was associated with higher risk of death (HR = 3.00, 95% CI, 1.97-4.56; P = 0.0001). CONCLUSIONS: This study confirms anti-PD-(L)1 monotherapy as a safe and effective treatment option in daily clinical practice for mUC patients. It also describes the presence of peritoneal metastases as an independent prognostic factor for OS and underlines the association between PPI use and worse therapeutic outcomes. Finally, it proposes a new easy-to-use risk-assessment model for OS prediction.