Laparoscopic cholecystectomy critical view of safety (LC-CVS): a multi-national validation study of an objective, procedure-specific assessment using video-based assessment (VBA)

Background A novel 6-item objective, procedure-specific assessment for laparoscopic cholecystectomy incorporating the critical view of safety (LC-CVS OPSA) was developed to support trainee formative and summative assessments. The LC-CVS OPSA included two retraction items (fundus and infundibulum retraction) and four CVS items (hepatocystic triangle visualization, gallbladder-liver separation, cystic artery identification, and cystic duct identification). The scoring rubric for retraction consisted of poor (frequently outside of defined range), adequate (minimally outside of defined range) and excellent (consistently inside defined range) and for CVS items were “poor—unsafe”, “adequate—safe”, or “excellent—safe”. Methods A multi-national consortium of 12 expert LC surgeons applied the OPSA—LC CVS to 35 unique LC videos and one duplicate video. Primary outcome measure was inter-rater reliability as measured by Gwet’s AC 2 , a weighted measure that adjusts for scales with high probability of random agreement. Analysis of the inter-rater reliability was conducted on a collapsed dichotomous scoring rubric of “poor—unsafe” vs. “adequate/excellent—safe”. Results Inter-rater reliability was high for all six items ranging from 0.76 (hepatocystic triangle visualization) to 0.86 (cystic duct identification). Intra-rater reliability for the single duplicate video was substantially higher across the six items ranging from 0.91 to 1.00. Conclusions The novel 6-item OPSA LC CVS demonstrated high inter-rater reliability when tested with a multi-national consortium of LC expert surgeons. This brief instrument focused on safe surgical practice was designed to support the implementation of entrustable professional activities into busy surgical training programs. Instrument use coupled with video-based assessments creates novel datasets with the potential for artificial intelligence development including computer vision to drive assessment automation. Graphical abstract