Improving the identification of high-risk non-metastatic castration-resistant prostate cancer patients in clinical practice.

Non-metastatic castration-resistant prostate cancer (nmCRPC) represents a challenging disease state in prostate cancer care. nmCRPC patients with a high risk of progression to metastatic disease who are identified by a prostate-specific antigen doubling time (PSADT) ≤10 months are eligible for treatment with the novel androgen receptor inhibitors (ARIs), shown to delay disease progression and extend survival. However, nmCRPC is often unexploited in clinical practice due to a lack of standardization in the methodology and in the tools used for its identification. In this article, a group of Urology and Oncology specialists with acknowledged expertise in prostate cancer reviews the state of the art in the management of high-risk nmCRPC patients, identifies gaps and unmet needs, and proposes strategies to optimize the identification of this patient subgroup in the clinical practice and improve their health outcomes.

Barthel's Index: A Better Predictor for COVID-19 Mortality Than Comorbidities.

BACKGROUND: The most consistently identified mortality determinants for the new coronavirus 2019 (COVID-19) infection are aging, male sex, cardiovascular/respiratory diseases, and cancer. They were determined from heterogeneous cohorts that included patients with different disease severity and previous conditions. The main goal of this study was to determine if activities of daily living (ADL) dependence measured by Barthel's index could be a predictor for COVID-19 mortality. METHODS: A prospective cohort study was performed with a consecutive sample of 340 COVID-19 patients representing patients from all over the northern region of Portugal from October 2020 to March 2021. Mortality risk factors were determined after controlling for demographics, ADL dependence, admission time, comorbidities, clinical manifestations, and delay-time for diagnosis. Central tendency measures were used to analyze continuous variables and absolute numbers (proportions) for categorical variables. For univariable analysis, we used t test, chi-square test, or Fisher exact test as appropriate (α=0.05). Multivariable analysis was performed using logistic regression. IBM SPSS version 27 statistical software was used for data analysis. RESULTS: The cohort included 340 patients (55.3% females) with a mean age of 80.6±11.0 years. The mortality rate was 19.7%. Univariate analysis revealed that aging, ADL dependence, pneumonia, and dementia were associated with mortality and that dyslipidemia and obesity were associated with survival. In multivariable analysis, dyslipidemia (odds ratio [OR], 0.35; 95% confidence interval [CI], 0.17-0.71) was independently associated with survival. Age ≥86 years (pooled OR, 2.239; 95% CI, 1.100-4.559), pneumonia (pooled OR, 3.00; 95% CI, 1.362-6.606), and ADL dependence (pooled OR, 6.296; 95% CI, 1.795-22.088) were significantly related to mortality (receiver operating characteristic area under the curve, 82.1%; p<0.001). CONCLUSION: ADL dependence, aging, and pneumonia are three main predictors for COVID-19 mortality in an elderly population.