Introduction: The learning curve for retzius sparing robotic radical prostatectomy is not fully understood. This study attempts to identify the learning-curve across the first 130 cases of a single surgeon. Methods: All retzius sparing robotic radical prostatectomy cases performed by a single surgeon between April 2019 and July 2022 were included. Cases were divided chronologically into 3 groups. Results: 130 RS-RARP cases were identified. Statistically significant differences were found between groups in several areas. Median patient age increased between group 1 (59yrs) and Group 3 (66.5yrs) (P=0.04). Proportion of patients with stage >T2 increased between Group 1 (27.9%) and Group 2 (41.9%) (P=0.036). Median console time increased between Group 1 (120 mins) and Group 2 (150 mins,) (P=0.01). Median gland weight increased between Group 1 (28g) and Group 3 (35.5g) (P<0.001). Positive surgical margin rate fell between Group 1 (30.2%) and Group 3 (9.1%). Conclusions: The complexity of cases increased over the learning curve, reflected in older patients, larger prostates and higher stage disease, but the positive surgical margin rate improved with experience. Safety and functional outcomes are excellent throughout. The learning curve might be facilitated by careful case selection favouring smaller prostates with less advanced disease.
Aim Patients with low and intermediate risk prostate cancer must decide whether to undergo radical treatment. The PredictProstate tool uses patient characteristics to quantify the relative benefit of radical treatment. It has been introduced in our prostate cancer specialist multidisciplinary team meeting (pcSMDT) and in subsequent communication with patients to facilitate informed decision-making. The aim of this study was to assess the utilisation and utility of PredictProstate in informing treatment decisions for men referred to Cambridge University Hospitals (CUH) for consideration of radical prostatectomy (RARP). Method A retrospective chart review was conducted of patients referred to the CUH pcSMDT and robotic prostatectomy clinic (ROPD) between Sep 2019 and Aug 2021 for consideration of RARP. Data on patient characteristics, use of PredictProstate, and management decisions was collected from the EPIC EMR. A total of 841 patients were included in the analysis. Results The usage of PredictProstate in the pcSMDT increased in the second half of the study period (34.5% vs 23.8%, p<0.001). The use of PredictProstate for men with low and intermediate risk prostate cancer was associated with an increased likelihood of attending ROPD (75% vs 61%, p<0.001), but a reduced likelihood of RARP (41% vs 55%, p<0.01). These effects were most pronounced for men of favourable intermediate risk (80% vs 63%, p<0.004 and 34% vs 54%, p<0.07 respectively). Conclusions PredictProstate provides personalised prognostic data for patients. Its increased use for men with low and intermediate risk prostate cancer is associated with increased attendance at specialist surgical clinic and a reduced chance of undergoing RARP.
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