Development and Cross-Validation of a Nomogram for Chronic Kidney Disease Following Robot-Assisted Radical Cystectomy.

Journal: Journal of endourology
Published Date:

Abstract

We sought to identify the factors associated with deterioration of renal functions after robot-assisted radical cystectomy, and to develop a nomogram to detect the probability of progression to chronic kidney disease (CKD). A retrospective review of our prospectively maintained database. Estimated glomerular filtration rate (eGFR) was calculated using the CKD-Epidemiology Collaboration creatinine formula utilizing all follow-up creatinine values. CKD was defined as stage 3b (eGFR <45 mL/minute/1.73 m) based on the National Kidney Foundation classification. Kaplan-Meier curves were used to depict CKD-free survival. A multivariate Cox regression model was used to determine predictors for CKD and to build the perioperative nomogram. The data set comprised 442 patients with a median follow-up of 25 months (12-59). Thirty-seven percent developed CKD at a median of 9 months (4-18). CKD-free survival rates at 1, 3, and 5 years were 75%, 58%, and 50%, respectively. CKD was significantly associated with preoperative eGFR (hazards ratio [HR]: 0.96, 95% confidence interval [CI]: 0.95-0.97,  < 0.01), body mass index (HR: 1.03, 95% CI: 1.01-1.05,  = 0.03), Charlson Comorbidity Index ≥3 (HR: 2.20, 95% CI: 1.35-3.58,  < 0.01), diabetes (HR: 1.59, 95% CI: 1.09-2.31,  = 0.02), 90 days postoperative strictures (HR: 4.04, 95% CI: 1.76-9.30,  < 0.01), 90 days postoperative hydronephrosis (HR: 2.26, 95% CI: 1.34-3.79,  < 0.01), 90 days recurrent urinary tract infection (HR: 1.84, 95% CI: 1.08-3.14,  = 0.02), 90 days acute kidney injury (HR: 1.70, 95% CI: 1.19-2.43,  < 0.01), and node positive disease (HR: 1.94, 95% CI: 1.31-2.86,  < 0.01). A 5-year CKD-free survival nomogram was developed. We have developed and cross-validated a nomogram for detecting CKD-free survival. This nomogram may have a role in counseling and follow up of patients. This study was done after the approval of the IRB committee (I-79606).

Authors

  • Ahmed S Elsayed
    1 A.T.L.A.S (Applied Technology Laboratory for Advanced Surgery) Program, Department of Urology, Roswell Park Comprehensive Cancer Center, Buffalo, New York.
  • Zhe Jing
    1 A.T.L.A.S (Applied Technology Laboratory for Advanced Surgery) Program, Department of Urology, Roswell Park Comprehensive Cancer Center, Buffalo, New York.
  • Deniz Demirbas
    A.T.L.A.S (Applied Technology Laboratory for Advanced Surgery) Program, Department of Urologic Oncology, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.
  • Mohammad Durrani
    Department of Urology, Roswell Park Cancer Institute, Buffalo, New York.
  • Kristopher Attwood
    Department of Biostatistics and Bioinformatics, Roswell Park Cancer Institute, Buffalo, NY, USA.
  • Joseph Cilento
    A.T.L.A.S (Applied Technology Laboratory for Advanced Surgery) Program, Department of Urologic Oncology, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.
  • Jennifer A Osei
    Roswell Park Comprehensive Cancer Center, NY, USA.
  • Sean Gibson
    A.T.L.A.S (Applied Technology Laboratory for Advanced Surgery) Program, Department of Urologic Oncology, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.
  • Michael Mostowy
    Department of Otolaryngology, Jacobs School of Medicine and Biomedical Sciences at the University of Buffalo, Buffalo, New York, USA.
  • Amylisa Christophe
    A.T.L.A.S (Applied Technology Laboratory for Advanced Surgery) Program, Department of Urologic Oncology, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.
  • Ahmed A Hussein
    Department of Urology, Applied Technology Laboratory for Advanced Surgery (ATLAS) Program at Roswell Park Cancer Institute, Buffalo, NY; Department of Urology, Cairo University, Cairo, Egypt.
  • Khurshid A Guru