ePoster
Presentation Description
Institution: Nepean Hospital - NSW, Australia
Purpose
The da Vinci® Surgical System modular training program and dual console system allow trainees to achieve stepwise mastery of operative steps with supervision from consultants from the secondary console. We assessed trainee teaching outcomes by comparing pathological and peri-operative outcomes of robotic-assisted laparoscopic prostatectomy (RALP) performed predominantly by urology trainees to local and international results.
Methodology
RALPs were divided into 13 steps from port placement to specimen retrieval. In ‘registrar-lead’ cases, a registrar completed more than 75% of steps from anterior bladder neck transection to urethrovesical anastomosis formation (inclusive). We assessed performance, peri-operative parameters, and pathological outcomes or RALPs performed between February 2016 and August 2018.
Results
Of 126 cases, 39 were considered trainee-lead. There was no difference in pre-operative patient characteristics or disease profile between trainee-lead and consultant lead cases. There was no significant difference in operative time, frequency of intraoperative adverse events, ICU admissions, post-operative complications, length of stay, detectable PSA post-operatively, or positive margin rate between consultant-lead and trainee-lead cases where trainees were supported by modular training.
Conclusion
With appropriate case selection, trainees undergoing modular training in RALP supported by the dual console produce comparable surgical and oncological outcomes to specialist-lead cases. Modular training and dual console teaching is safe and effective in initiating robotic training. A robotic curriculum could be incorporated into SET training using the modular system which may shorten robotic learning curves.
Presenters
Authors
Authors
Dr Zoe Williams - , Dr Jeremy Saad - , Dr Henry Wang - , Dr Wenjie Zhong - , Dr Rasha Gendy - , Dr Mohan Arianayagam - , Dr Bertram Canagasingham - , Dr Ahmed Goolam - , Dr Nicola Jeffery - , Dr Jonathan Kam - , Prof Mohamed Khadra - , Prof Raymond Ko - , Dr Nicholas Mehan - , Prof Celalettin Varol - , Dr Isaac Thangasamy -