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Updated: Jan 22, 2026

Retzius-Sparing Robot-Assisted Radical Prostatectomy
Published on: May 19, 2022
Radical cystectomy pentafecta: a proposal for standardisation of outcomes reporting following robot-assisted radical
Giovanni E Cacciamani1, Matthew Winter1, Luis G Medina1
1USC Institute of Urology, Keck Medicine of USC, University of Southern California, Los Angeles, CA, USA.
Objective:
To propose a standardisable composite method for reporting outcomes of radical cystectomy (RC) that incorporates both perioperative morbidity and oncological adequacy.
Patients And Methods:
From July 2010 to December 2017, 277 consecutive patients who underwent robot-assisted RC with intracorporeal urinary diversion (UD) for bladder cancer at our Institution were prospectively analysed. Patients who simultaneously demonstrated negative soft tissue surgical margins (STSMs), ≥16 lymph node (LN) yield, absence of major (grade III-IV) complications at 90 days, absence of UD-related long-term sequelae and absence of clinical recurrence at ≤12 months, were considered as having achieved the RC-pentafecta. A multivariable logistic regression model was assessed to measure predictors for achieving RC-pentafecta.
Results And Limitations:
Since 2010, 270 of 277 patients that had completed at least 12 months of follow-up were included. Over a mean follow-up of 22.3 months, ≥16 LN yield, negative STSMs, absence of major complications at 90 days, and absence of UD-related surgical sequelae and clinical recurrence at ≤12 months were observed in 93.0%, 98.9%, 76.7%, 81.5% and 92.2%, patients, respectively, resulting in a RC-pentafecta rate of 53.3%. Multivariable logistic regression analysis revealed age (odds ratio [OR] 0.95; P = 0.002), type of UD (OR 2.19; P = 0.01) and pN stage (OR 0.48; P = 0.03) as independent predictors for achieving RC-pentafecta.
Conclusions:
We present a RC-pentafecta as a standardisable composite endpoint that incorporates perioperative morbidity and oncological adequacy as a potential tool to assess quality of RC. This tool may be useful for assessing the learning curve and calculating cost-effectiveness amongst others but needs to be externally validated in future studies.
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