Background: Laparoscopic suturing can be technically challenging and requires extensive training to achieve competency. To date no specific and objective assessment method for laparoscopic suturing and knot tying is available that can guide training and monitor performance in these complex surgical skills. In this study we aimed to develop a laparoscopic suturing competency assessment tool (LS-CAT) and assess its inter-observer reliability. Methods: We developed a bespoke CAT tool for laparoscopic suturing through a structured, mixed methodology approach, overseen by a steering committee with experience in developing surgical assessment tools. A wide Delphi consultation with over twelve experts in laparoscopic surgery guided the development stages of the tool. Following, subjects with different levels of laparoscopic expertise were included to evaluate this tool, using a simulated laparoscopic suturing task which involved placing of two surgical knots. A research assistant video recorded and anonymised each performance. Two blinded expert surgeons assessed the anonymised videos using the developed LS-CAT. The LS-CAT scores of the two experts were compared to assess the inter-observer reliability. Lastly, we compared the subjects’ LS-CAT performance scores at the beginning and end of their learning curve. Results: This study evaluated a novel LS-CAT performance tool, comprising of four tasks. Thirty-six complete videos were analysed and evaluated with the LS-CAT, of which the scores demonstrated excellent inter-observer reliability. Cohen’s Kappa analysis revealed good to excellent levels of agreement for almost all tasks of both instrument handling and tissue handling (0.87; 0.77; 0.75; 0.86; 0.85, all with p < 0.001). Subjects performed significantly better at the end of their learning curve compared to their first attempt for all LS-CAT items (all with p < 0.001). Conclusions: We developed the LS-CAT, which is a laparoscopic suturing grading matrix, with excellent inter-rater reliability and to discriminate between experience levels. This LS-CAT has a potential for wider use to objectively assess laparoscopic suturing skills.

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Introduction: There has been a wide uptake in the use of Minimal Invasive Surgery (MIS) globally across different surgical specialties. Whilst evidence exists for a structured training curriculum for basic laparoscopic surgery, there is little agreement on a complete framework for an advanced MIS training curriculum, defining the essential elements of the curriculum including the optimal assessment methods. The aim of this study is to obtain a consensus on the essential elements of a training curriculum for advanced MIS. Materials and Methods: A Delphi study was carried out involving 57 international experts in advanced MIS across different surgical specialties. A three round survey was conducted to reach consensus on the essential domains of a curriculum. This included defining the learners, trainers and training centres; curriculum content and competency based assessment. Results: Unanimous agreement was reached for the completion of basic laparoscopic training before entry into advanced training. A trainer should have reached competency in advanced MIS and attended a ‘Train the trainer’ course. The curriculum should be delivered as modular training, including a multi-modal approach with a structured clinical proctorship programme. Formative assessment was considered as an integral part of learning and should be performed using objective work based assessment tools such as global assessment scale (GAS) forms. Accreditation in advanced MIS can be achieved by objective assessment of technical performance of unedited videos in addition to key clinical performance outcomes. Conclusion: A consensus on the framework of an advanced MIS training curriculum has been achieved defining the essential elements of entry criteria, selection of trainers and training units and curriculum content. Multimodal learning, clinical proctorship programme and competency based assessment are integral parts of the curriculum.@en