A Roman1, K Ahmed1, B Challacombe2. 1. MRC Centre for Transplantation, King's College London, Urology Centre, Guy's Hospital, London UK. 2. MRC Centre for Transplantation, King's College London, Urology Centre, Guy's Hospital, London UK. Electronic address: benchallacombe@doctors.org.uk.
Abstract
INTRODUCTION: Although Robotic partial nephrectomy (RPN) is an emerging technique for the management of small renal masses, this approach is technically demanding. To date, there is limited data on the nature and progression of the learning curve in RPN. AIMS: To analyse the impact of case mix on the RPN LC and to model the learning curve. METHODS: The records of the first 100 RPN performed, were analysed at our institution that were carried out by a single surgeon (B.C) (June 2010-December 2013). Cases were split based on their Preoperative Aspects and Dimensions Used for an Anatomical (PADUA) score into the following groups: 6-7, 8-9 and >10. Using a split group (20 patients in each group) and incremental analysis, the mean, the curve of best fit and R(2) values were calculated for each group. RESULTS: Of 100 patients (F:28, M:72), the mean age was 56.4 ± 11.9 years. The number of patients in each PADUA score groups: 6-7, 8-9 and >10 were 61, 32 and 7 respectively. An increase in incidence of more complex cases throughout the cohort was evident within the 8-9 group (2010: 1 case, 2013: 16 cases). The learning process did not significantly affect the proxies used to assess surgical proficiency in this study (operative time and warm ischaemia time). CONCLUSIONS: Case difficulty is an important parameter that should be considered when evaluating procedural learning curves. There is not one well fitting model that can be used to model the learning curve. With increasing experience, clinicians tend to operate on more difficult cases.
INTRODUCTION: Although Robotic partial nephrectomy (RPN) is an emerging technique for the management of small renal masses, this approach is technically demanding. To date, there is limited data on the nature and progression of the learning curve in RPN. AIMS: To analyse the impact of case mix on the RPN LC and to model the learning curve. METHODS: The records of the first 100 RPN performed, were analysed at our institution that were carried out by a single surgeon (B.C) (June 2010-December 2013). Cases were split based on their Preoperative Aspects and Dimensions Used for an Anatomical (PADUA) score into the following groups: 6-7, 8-9 and >10. Using a split group (20 patients in each group) and incremental analysis, the mean, the curve of best fit and R(2) values were calculated for each group. RESULTS: Of 100 patients (F:28, M:72), the mean age was 56.4 ± 11.9 years. The number of patients in each PADUA score groups: 6-7, 8-9 and >10 were 61, 32 and 7 respectively. An increase in incidence of more complex cases throughout the cohort was evident within the 8-9 group (2010: 1 case, 2013: 16 cases). The learning process did not significantly affect the proxies used to assess surgical proficiency in this study (operative time and warm ischaemia time). CONCLUSIONS: Case difficulty is an important parameter that should be considered when evaluating procedural learning curves. There is not one well fitting model that can be used to model the learning curve. With increasing experience, clinicians tend to operate on more difficult cases.