Feasibility of expert and crowd-sourced review of intraoperative video for quality improvement of intracorporeal urinary diversion during robotic radical cystectomy

Mitchell G. Goldenberg, Jamal Nabhani, Christopher J.D. Wallis, Sameer Chopra, Andrew J. Hung, Anne Schuckman, Hooman Djaladat, Siamak Daneshmand, Mihir M. Desai, Monish Aron, Inderbir S. Gill, Raj Satkunasivam

Abstract


Introduction: Development of uretero-ileal stricture (UIS) after robotic-assisted radical cystectomy (RARC) may be dependent on surgical technique. Video review of intraoperative technique is an emerging paradigm for surgical quality improvement. We examined whether surgeon-perceived risk of UIS or crowd-sourced assessment of robotic skill are associated with the development of UIS.

Methods: We conducted a case-control study comparing the operative technique of uretero-ileal anastomoses resulting in clinically significant UIS with the contralateral anastomosis for the same patient. De-identified videos were analyzed by 1) five high-volume surgeons; and 2) crowd workers (Crowd-Sourced Assessment of Technical Skill, C-SATS) to determine Global Evaluative Assessment of Robotic Skill (GEARS) score. Mantel-Haenszel common odds ratio (OR) estimates were calculated to assess the association between surgeon performance and the development of UIS. Logistic regression models were used to examine the association between GEARS scores and the development of UIS.

Results: A total of 10 UIS videos were compared to eight control videos by five surgeons and 2142 crowd workers. Expert surgeons systematically evaluated intraoperative footage, however, no association between the expert mode response and UIS (OR 0.42; 95% confidence interval [CI] 0.05‒3.45; p=0.91) was identified. Crowd-sourced assessment was not predictive of UIS (p=0.62).

Conclusions: We used video review to systematically analyze procedure-specific content and technique. The inability of surgeons to predict UIS may reflect the questionnaire, uncontrolled patient factors, or a lack of power. Crowd-sourced GEARS score was unsuccessful in predicting UIS after RARC.


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DOI: http://dx.doi.org/10.5489/cuaj.4442

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