Andrew J Hung1, Paul J Oh1, Jian Chen1, Saum Ghodoussipour1, Christianne Lane2, Anthony Jarc3, Inderbir S Gill1. 1. Center for Robotic Simulation and Education, Catherine and Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles, CA, USA. 2. Southern California Clinical and Translational Science Institute, Los Angeles, CA, USA. 3. Medical Research, Intuitive Surgical, Inc., Norcross, GA, USA.
Abstract
OBJECTIVES: To evaluate automated performance metrics (APMs) and clinical data of experts and super-experts for four cardinal steps of robot-assisted radical prostatectomy (RARP): bladder neck dissection; pedicle dissection; prostate apex dissection; and vesico-urethral anastomosis. SUBJECTS AND METHODS: We captured APMs (motion tracking and system events data) and synchronized surgical video during RARP. APMs were compared between two experience levels: experts (100-750 cases) and super-experts (2100-3500 cases). Clinical outcomes (peri-operative, oncological and functional) were then compared between the two groups. APMs and outcomes were analysed for 125 RARPs using multi-level mixed-effect modelling. RESULTS: For the four cardinal steps selected, super-experts showed differences in select APMs compared with experts (P < 0.05). Despite similar PSA and Gleason scores, super-experts outperformed experts clinically with regard to peri-operative outcomes, with a greater lymph node yield of 22.6 vs 14.9 nodes, respectively (P < 0.01), less blood loss (125 vs 130 mL, respectively; P < 0.01), and fewer readmissions at 30 days (1% vs 13%, respectively; P = 0.02). A similar but nonsignificant trend was seen for oncological and functional outcomes, with super-experts having a lower rate of biochemical recurrence compared with experts (5% vs 15%, respectively; P = 0.13) and a higher continence rate at 3 months (36% vs 18%, respectively; P = 0.14). CONCLUSION: We found that experts and super-experts differed significantly in select APMs for the four cardinal steps of RARP, indicating that surgeons do continue to improve in performance even after achieving expertise. We hope ultimately to identify associations between APMs and clinical outcomes to tailor interventions to surgeons and optimize patient outcomes.
OBJECTIVES: To evaluate automated performance metrics (APMs) and clinical data of experts and super-experts for four cardinal steps of robot-assisted radical prostatectomy (RARP): bladder neck dissection; pedicle dissection; prostate apex dissection; and vesico-urethral anastomosis. SUBJECTS AND METHODS: We captured APMs (motion tracking and system events data) and synchronized surgical video during RARP. APMs were compared between two experience levels: experts (100-750 cases) and super-experts (2100-3500 cases). Clinical outcomes (peri-operative, oncological and functional) were then compared between the two groups. APMs and outcomes were analysed for 125 RARPs using multi-level mixed-effect modelling. RESULTS: For the four cardinal steps selected, super-experts showed differences in select APMs compared with experts (P < 0.05). Despite similar PSA and Gleason scores, super-experts outperformed experts clinically with regard to peri-operative outcomes, with a greater lymph node yield of 22.6 vs 14.9 nodes, respectively (P < 0.01), less blood loss (125 vs 130 mL, respectively; P < 0.01), and fewer readmissions at 30 days (1% vs 13%, respectively; P = 0.02). A similar but nonsignificant trend was seen for oncological and functional outcomes, with super-experts having a lower rate of biochemical recurrence compared with experts (5% vs 15%, respectively; P = 0.13) and a higher continence rate at 3 months (36% vs 18%, respectively; P = 0.14). CONCLUSION: We found that experts and super-experts differed significantly in select APMs for the four cardinal steps of RARP, indicating that surgeons do continue to improve in performance even after achieving expertise. We hope ultimately to identify associations between APMs and clinical outcomes to tailor interventions to surgeons and optimize patient outcomes.
Authors: Jessica H Nguyen; Jian Chen; Sandra P Marshall; Saum Ghodoussipour; Andrew Chen; Inderbir S Gill; Andrew J Hung Journal: World J Urol Date: 2019-07-25 Impact factor: 4.226
Authors: Jan Ebbing; Peter N Wiklund; Olof Akre; Stefan Carlsson; Mats J Olsson; Jonas Höijer; Maurice Heimer; Justin W Collins Journal: Int J Med Robot Date: 2020-11-13 Impact factor: 2.547