An Assessment Tool to Provide Targeted Feedback to Robotic Surgical Trainees : Development and Validation of the End-To-End Assessment of Suturing Expertise (EASE)
Purpose: To create a suturing skills assessment tool that comprehensively defines criteria around relevant sub-skills of suturing and to confirm its validity.
Materials and Methods: 5 expert surgeons and an educational psychologist participated in a cognitive task analysis (CTA) to deconstruct robotic suturing into an exhaustive list of technical skill domains and sub-skill descriptions. Using the Delphi methodology, each CTA element was systematically reviewed by a multi-institutional panel of 16 surgical educators and implemented in the final product when content validity index (CVI) reached ≥0.80. In the subsequent validation phase, 3 blinded reviewers independently scored 8 training videos and 39 vesicourethral anastomoses (VUA) using EASE; 10 VUA were also scored using Robotic Anastomosis Competency Evaluation (RACE), a previously validated, but simplified suturing assessment tool. 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.
Results: After two rounds of Delphi process, panelists agreed on 7 domains, 18 sub-skills, and 57 detailed sub-skill descriptions with CVI ≥ 0.80. Inter-rater reliability was moderately high (ICC median: 0.69, range: 0.51-0.97; PABAK: 0.77, 0.62-0.97). Multiple EASE sub-skill scores were able to distinguish surgeon experience. The Spearman's rho correlation between overall EASE and RACE scores was 0.635 (p=0.003).
Conclusions: Through a rigorous CTA and Delphi process, we have developed EASE, whose suturing sub-skills can distinguish surgeon experience while maintaining rater reliability.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:9 |
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| Enthalten in: |
Urology practice - 9(2022), 6 vom: 21. Nov., Seite 532-539 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Haque, Taseen F [VerfasserIn] |
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| Links: |
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| Themen: |
Assessment tool |
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| Anmerkungen: |
Date Revised 25.05.2023 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1097/upj.0000000000000344 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM353511161 |
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| 100 | 1 | |a Haque, Taseen F |e verfasserin |4 aut | |
| 245 | 1 | 3 | |a An Assessment Tool to Provide Targeted Feedback to Robotic Surgical Trainees |b Development and Validation of the End-To-End Assessment of Suturing Expertise (EASE) |
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| 500 | |a Date Revised 25.05.2023 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a Purpose: To create a suturing skills assessment tool that comprehensively defines criteria around relevant sub-skills of suturing and to confirm its validity | ||
| 520 | |a Materials and Methods: 5 expert surgeons and an educational psychologist participated in a cognitive task analysis (CTA) to deconstruct robotic suturing into an exhaustive list of technical skill domains and sub-skill descriptions. Using the Delphi methodology, each CTA element was systematically reviewed by a multi-institutional panel of 16 surgical educators and implemented in the final product when content validity index (CVI) reached ≥0.80. In the subsequent validation phase, 3 blinded reviewers independently scored 8 training videos and 39 vesicourethral anastomoses (VUA) using EASE; 10 VUA were also scored using Robotic Anastomosis Competency Evaluation (RACE), a previously validated, but simplified suturing assessment tool. 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 | ||
| 520 | |a Results: After two rounds of Delphi process, panelists agreed on 7 domains, 18 sub-skills, and 57 detailed sub-skill descriptions with CVI ≥ 0.80. Inter-rater reliability was moderately high (ICC median: 0.69, range: 0.51-0.97; PABAK: 0.77, 0.62-0.97). Multiple EASE sub-skill scores were able to distinguish surgeon experience. The Spearman's rho correlation between overall EASE and RACE scores was 0.635 (p=0.003) | ||
| 520 | |a Conclusions: Through a rigorous CTA and Delphi process, we have developed EASE, whose suturing sub-skills can distinguish surgeon experience while maintaining rater reliability | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a assessment tool | |
| 650 | 4 | |a prostatectomy | |
| 650 | 4 | |a robotics | |
| 650 | 4 | |a surgical education | |
| 650 | 4 | |a suturing skill | |
| 700 | 1 | |a Hui, Alvin |e verfasserin |4 aut | |
| 700 | 1 | |a You, Jonathan |e verfasserin |4 aut | |
| 700 | 1 | |a Ma, Runzhuo |e verfasserin |4 aut | |
| 700 | 1 | |a Nguyen, Jessica H |e verfasserin |4 aut | |
| 700 | 1 | |a Lei, Xiaomeng |e verfasserin |4 aut | |
| 700 | 1 | |a Cen, Steven |e verfasserin |4 aut | |
| 700 | 1 | |a Aron, Monish |e verfasserin |4 aut | |
| 700 | 1 | |a Collins, Justin W |e verfasserin |4 aut | |
| 700 | 1 | |a Djaladat, Hooman |e verfasserin |4 aut | |
| 700 | 1 | |a Ghazi, Ahmed |e verfasserin |4 aut | |
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