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MP10-17 DEVELOPMENT AND VALIDATION OF THE END-TO-END ASSESSMENT OF SUTURING EXPERTISE (EASE)

Taseen F. Haque, Alvin Hui, Jonathan You, Runzhuo Ma, Steven Cen, Xiaomeng Li, Monish Aron, Justin Collins, Hooman Djaladat, Ahmed Ghazi, Kenneth A. Yates, Andre Luis Abreu, Siamak Danseshmand, Mihir Desai, Alvin C. Goh, Jim C. Hu, Amir H. Lebastchi, Thomas S. Lendvay, James I. Porter, Anne Schuckman

Year
2022
Citations
2

Abstract

You have accessJournal of UrologyCME1 May 2022MP10-17 DEVELOPMENT AND VALIDATION OF THE END-TO-END ASSESSMENT OF SUTURING EXPERTISE (EASE) Taseen F. Haque, Alvin Hui, Jonathan You, Runzhuo Ma, Steven Cen, Xiaomeng Li, Monish Aron, Justin W. Collins, Hooman Djaladat, Ahmed Ghazi, Kenneth A. Yates, Andre L. Abreu, Siamak Danseshmand, Mihir M. Desai, Alvin C. Goh, Jim C. Hu, Amir H. Lebastchi, Thomas S. Lendvay, James Porter, Anne K. Schuckman, Rene Sotelo, Chandru P. Sundaram, Jessica H. Nguyen, Inderbir Gill, and Andrew J. Hung Taseen F. HaqueTaseen F. Haque More articles by this author , Alvin HuiAlvin Hui More articles by this author , Jonathan YouJonathan You More articles by this author , Runzhuo MaRunzhuo Ma More articles by this author , Steven CenSteven Cen More articles by this author , Xiaomeng LiXiaomeng Li More articles by this author , Monish AronMonish Aron More articles by this author , Justin W. CollinsJustin W. Collins More articles by this author , Hooman DjaladatHooman Djaladat More articles by this author , Ahmed GhaziAhmed Ghazi More articles by this author , Kenneth A. YatesKenneth A. Yates More articles by this author , Andre L. AbreuAndre L. Abreu More articles by this author , Siamak DanseshmandSiamak Danseshmand More articles by this author , Mihir M. DesaiMihir M. Desai More articles by this author , Alvin C. GohAlvin C. Goh More articles by this author , Jim C. HuJim C. Hu More articles by this author , Amir H. LebastchiAmir H. Lebastchi More articles by this author , Thomas S. LendvayThomas S. Lendvay More articles by this author , James PorterJames Porter More articles by this author , Anne K. SchuckmanAnne K. Schuckman More articles by this author , Rene SoteloRene Sotelo More articles by this author , Chandru P. SundaramChandru P. Sundaram More articles by this author , Jessica H. NguyenJessica H. Nguyen More articles by this author , Inderbir GillInderbir Gill More articles by this author , and Andrew J. HungAndrew J. Hung More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002532.17AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Current skills assessment tools do not encompass all aspects of suturing and therefore may omit key insights to help trainees improve. This study aimed to create a global suturing skills assessment tool that comprehensively defines criteria around relevant sub-skills of suturing and to evaluate its validity. METHODS: In Stage 1 (Development), 4 expert surgeons and an educational psychologist participated in a cognitive task analysis (CTA) to deconstruct robotic suturing into its most basic maneuvers, describe the accompanying “sub-skills”, and define the differing proficiencies on a scale of 1-3. Using the Delphi method, each CTA element was then systematically revised by a multi-institutional panel of 16 leading surgical educators. Sub-skill descriptions that reached a content validity index (CVI) ≥0.80 were included in the final product. In Stage 2 (Validation), 3 blinded reviewers independently scored 8 training videos and 39 vesicourethral anastomoses (VUA) using EASE. Inter-rater reliability was measured with intra-class correlation (ICC) for normally distributed values and prevalence-adjusted bias-adjusted Kappa (PABAK) for skewed distributions. Expert (≥100 prior robotic cases) and trainee (<100 cases) EASE scores from the non-training cases were compared using a generalized linear mixed model to adjust for data nesting within surgeons. RESULTS: Stage 1: The 16 surgeon panelists for the Delphi method had a median H-index of 23 (range 11-107). In Round 1 of the Delphi method, 60/64 (94%) of proposed sub-skill descriptions met the CVI threshold. In Round 2, the number of sub-skill descriptions decreased to 61 as panelists suggested combining two sub-skill categories; these remaining descriptions all reached CVI threshold

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MedicineLibrary scienceComputer science

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