Exploring the Diversity and Predictors of Histopathological Findings Across the European Association of Urology Guidelines Office Rapid Reaction Group Priority Groups for Patients with Renal Tumors: Implications for Individualized Prioritization of Renal Cancer Care

Development and validation of a prognostic nomogram for predicting cancer-specific survival in patients with metastatic clear cell renal carcinoma: A study based on SEER database

Objectives

Clear cell renal cell carcinoma (ccRCC) is highly prevalent, prone to metastasis, and has a poor prognosis after metastasis. Therefore, this study aimed to develop a prognostic model to predict the individualized prognosis of patients with metastatic clear cell renal cell carcinoma (mccRCC).

Patients and Methods

Data of 1790 patients with mccRCC, registered from 2010 to 2015, were extracted from the Surveillance, Epidemiology and End Results (SEER) database. The included patients were randomly divided into a training set (n = 1253) and a validation set (n = 537) based on the ratio of 7:3. The univariate and multivariate Cox regression analyses were used to identify the important independent prognostic factors. A nomogram was then constructed to predict cancer specific survival (CSS). The performance of the nomogram was internally validated by using the concordance index (C-index), calibration plots, receiver operating characteristic curves, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA). We compared the nomogram with the TNM staging system. Kaplan–Meier survival analysis was applied to validate the application of the risk stratification system.

Results

Diagnostic age, T-stage, N-stage, bone metastases, brain metastases, liver metastases, lung metastases, chemotherapy, radiotherapy, surgery, and histological grade were identified as independent predictors of CSS. The C-index of training and validation sets are 0.707 and 0.650 respectively. In the training set, the AUC of CSS predicted by nomogram in patients with mccRCC at 1-, 3- and 5-years were 0.770, 0.758, and 0.757, respectively. And that in the validation set were 0.717, 0.700, and 0.700 respectively. Calibration plots also showed great prediction accuracy. Compared with the TNM staging system, NRI and IDI results showed that the predictive ability of the nomogram was greatly improved, and DCA showed that patients obtained clinical benefits. The risk stratification system can significantly distinguish the patients with different survival risks.

Conclusion

In this study, we developed and validated a nomogram to predict the CSS rate in patients with mccRCC. It showed consistent reliability and clinical applicability. Nomogram may assist clinicians in evaluating the risk factors of patients and formulating an optimal individualized treatment strategy.

How the First Year of COVID-19 Affected Elective Pediatric Urology Patients: A Longitudinal Study Based on Waiting Lists and Surveys From 10 European Centers

Introduction

COVID-19 impacted healthcare systems worldwide, and elective surgical activity was brought to a minimum. Although children were not primarily affected by the disease, pediatric urology was halted by clinical closedown and staff allocation. We aimed to document how these prioritizations affected waiting lists, and to investigate how European centers dealt with the challenge of these logistical and financial prioritizations.

Materials and Methods

This was a 1-year prospective study, starting March 2020. Participants were surveyed at 3-month intervals about waiting lists for several common procedures as well as OR capacity and funding. Further, centers retrospectively reported on surgical and outpatient activity rates during 2019–2021. Waiting list tendencies were evaluated in relation to study baseline.

Results

A marked decrease in surgical and outpatient activity was seen in the spring of 2020. Some included pediatric urology centers were able to increase their budget (15%) and staff working hours (20%) during part of the study period. Still, at the end of the study, the centers had increased the total number of patients on waiting lists with 11%, whereas the average days on waiting lists had accumulated with 73%, yielding a total of 6,102 accumulated waiting days in the study population. Centers with decreased resources had markedly negative effects on waiting lists.

Conclusions

Correlations between COVID-19 derived burdening of healthcare systems and the availability of pediatric urology greatly depends on the prioritizations made at individual centers. Ongoing monitoring of these correlations is warranted to safely avoid unnecessary negative impact on the pediatric population.