Radical cystectomy pentafecta: a proposal for standardisation of outcomes reporting following robot‐assisted radical cystectomy. (26th July 2019) - Cacciamani, Giovanni E.; Winter, Matthew; Medina, Luis G.; Ashrafi, Akhbar N.; Miranda, Gus; Tafuri, Alessandro; Landsberger, Hannah; Lin‐Brande, Michael; Rajarubendra, Nieroshan; De Castro Abreu, Andre; Berger, Andre; Aron, Monish; et al.
Abstract : 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.