Impaired estimated glomerular filtration rate is a significant predictor for non-muscle-invasive bladder cancer recurrence and progression--introducing a novel prognostic model for bladder cancer recurrence.

Purpose: To evaluate the influence of preoperative patient-associated parameters and comorbidities, with special focus on preoperative renal function on non-muscle-invasive bladder carcinoma (NMIBC) recurrence and progression.
Patients and Methods: Medical records of 192 patients with first diagnosis of NMIBC from 1996 to 2006 treated at our department were reviewed for tumor stage and grade, comorbidities, and putative preoperative risk factors (diabetes mellitus, smoking habits, C-reactive protein levels, estimated glomerular filtration rate [eGFR] by chronic kidney disease-epidemiology collaboration formula, patient age, and sex).
Results: Median follow-up was 80 months. Tumor recurrence was observed in 104 patients (54%); progression to higher pT category or grade was found in 43 patients (22%). Overall, 20 (10%) patients had progression to category pT2, with a median time to progression of 11 months (95% CI: 3.16-55.77). Overall, median time to recurrence was 11.91 months (8.00-14.94), while median time to progression to higher category/grade was 15 months (8.60-36.95). Cancer-specific death was recorded for 12 patients (6%). Univariate analysis revealed category, grade, and eGFR < 60 ml/min as significant predictive determinants for recurrence, overall progression, and progression to pT2-category disease. Based on the results of multivariate analysis, a risk factor model was created to classify patients with high and low risk of recurrence.
Conclusions: eGFR is a significant predictive variable of NMIBC recurrence and progression. Integrating eGFR into NMIBC risk calculation helped to discriminate individuals with high and low risk of cancer recurrence. Confirmatory studies and external validation are needed to corroborate these findings. Furthermore, the role of tumor multifocality and size should be analyzed for the proposed prediction model.
(Copyright © 2014 Elsevier Inc. All rights reserved.)