In Europe, an estimated 151 297 new cases of bladder cancer were diagnosed in 2012, with an age-standardised incidence rate (per 100 000 persons) of 17.7 for males and 3.5 for females. Overall, the annual crude incidence rate is 20.4/100 000. In 2012, there were 52 395 deaths from bladder cancer with an annual crude mortality rate of 7.1/100 000 [1]. Approximately 70% of patients with bladder cancer are >65 years of age.

Programmed death-1 (PD-1) receptor/PD-1 ligand (PD-L1) pathway negatively regulates T-cell-mediated responses. The prognostic impact of PD-L1 expression needs to be defined in urothelial carcinoma (UC). Formalin-fixed paraffin-embedded tumor samples from 160 patients with UC were retrieved. PD-L1 expression was evaluated by immunohistochemistry using a mouse monoclonal anti-PD-L1 antibody (405.9A11). PD-L1 positivity on tumor cell membrane was defined as ≥5% of tumor cell membrane staining. The extent of tumor-infiltrating mononuclear cells (TIMCs) as well as PD-L1 expression on TIMCs was scored from 0 to 4. A score of 2, 3, or 4 was considered PD-L1-positive. Clinico-pathological variables were documented. The Cox regression model was used to assess the association of PD-L1 expression with overall survival (OS) in patients who developed metastases. TIMCs were present in 143 of the 160 patient samples. Out of 160 samples, 32 (20%) had positive PD-L1 expression in tumor cell membrane. Out of 143 samples with TIMCs, 58 (40%) had positive PD-L1 expression in TIMCs. Smoking history, prior BCG use and chromosome 9 loss did not correlate with PD-L1 expression in either tumor cell membrane or TIMCs. PD-L1 positivity was not different between non-invasive or invasive UC. In patients who developed metastases (M1) and were treated with systemic therapy (n = 100), PD-L1 positivity on tumor cell membrane was seen in 14% of patients and did not correlate with OS (P = 0.45). Out of 89 M1 patients who had evaluable PD-L1 on TIMCs, PD-L1 expression was seen in 33% of patients and was significantly associated with longer OS on multivariate analysis (P = 0.0007). PD-L1 is widely expressed in tumor cell membrane and TIMCs in UC. PD-L1 in tumor cells was not predictive of OS. However, positive PD-L1 expression in TIMCs was significantly associated with longer survival in those patients who developed metastases.

Risk-adjusted analyses are critical in evaluating trauma outcomes. The National Trauma Data Bank (NTDB) is a statistically robust registry that allows such analyses; however, analytical techniques are not yet standardized. In this study, we examined peer-reviewed manuscripts published using NTDB data, with particular attention to characteristics strongly associated with trauma outcomes. Our objective was to determine if there are substantial variations in the methodology and quality of risk-adjusted analyses and therefore, whether development of best practices for risk-adjusted analyses is warranted. A database of all studies using NTDB data published through December 2010 was created by searching PubMed and Embase. Studies with multivariate risk-adjusted analyses were examined for their central question, main outcomes measures, analytical techniques, covariates in adjusted analyses, and handling of missing data. Of 286 NTDB publications, 122 performed a multivariable adjusted analysis. These studies focused on clinical outcomes (51 studies), public health policy or injury prevention (30), quality (16), disparities (15), trauma center designation (6), or scoring systems (4). Mortality was the main outcome in 98 of these studies. There were considerable differences in the covariates used for case adjustment. The 3 covariates most frequently controlled for were age (95%), Injury Severity Score (85%), and sex (78%). Up to 43% of studies did not control for the 5 basic covariates necessary to conduct a risk-adjusted analysis of trauma mortality. Less than 10% of studies used clustering to adjust for facility differences or imputation to handle missing data. There is significant variability in how risk-adjusted analyses using data from the NTDB are performed. Best practices are needed to further improve the quality of research from the NTDB.