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Erschienen in: World Journal of Urology 7/2020

14.11.2019 | Topic Paper

Comparison of clinical outcomes and automated performance metrics in robot-assisted radical prostatectomy with and without trainee involvement

verfasst von: Andrew Chen, Saum Ghodoussipour, Micha B. Titus, Jessica H. Nguyen, Jian Chen, Runzhuo Ma, Andrew J. Hung

Erschienen in: World Journal of Urology | Ausgabe 7/2020

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Abstract

Purpose

In this study, we investigate the effect of trainee involvement on surgical performance, as measured by automated performance metrics (APMs), and outcomes after robot-assisted radical prostatectomy (RARP).

Methods

We compared APMs (instrument tracking, EndoWrist® articulation, and system events data) and clinical outcomes for cases with varying resident involvement. Four of 12 standardized RARP steps were designated critical (“cardinal”) steps. Comparison 1: cases where the attending surgeon performed all four cardinal steps (Group A) and cases where a trainee was involved in at least one cardinal step (Group B). Comparison 2, where Group A is split into Groups C and D: cases where attending performs the whole case (Group C) vs. cases where a trainee performed at least one non-cardinal step (Group D). Mann–Whitney U and Chi-squared tests were used for comparisons.

Results

Comparison 1 showed significant differences in APM profiles including camera movement time, third instrument usage, dominant instrument moving time, velocity, articulation, as well as non-dominant instrument moving time and articulation (all favoring Group A p < 0.05). There was a significant difference in re-admission rates (10.9% in Group A vs 0% in Group B, p < 0.02), but not for post-operative outcomes. Comparison 2 demonstrated a significant difference in dominant instrument articulation (p < 0.05) but not in post-operative outcomes.

Conclusions

Trainee involvement in RARP is safe. The degree of trainee involvement does not significantly affect major clinical outcomes. APM profiles are less efficient when trainees perform at least one cardinal step but not during non-cardinal steps.
Literatur
11.
Zurück zum Zitat Hung AJ, Chen J, Ghodoussipour S, Oh PJ, Liu Z, Nguyen J, Purushotham S, Gill IS, Liu Y (2019) A deep-learning model using automated performance metrics and clinical features to predict urinary continence recovery after robot-assisted radical prostatectomy. BJU Int. https://doi.org/10.1111/bju.14735 CrossRefPubMed Hung AJ, Chen J, Ghodoussipour S, Oh PJ, Liu Z, Nguyen J, Purushotham S, Gill IS, Liu Y (2019) A deep-learning model using automated performance metrics and clinical features to predict urinary continence recovery after robot-assisted radical prostatectomy. BJU Int. https://​doi.​org/​10.​1111/​bju.​14735 CrossRefPubMed
Metadaten
Titel
Comparison of clinical outcomes and automated performance metrics in robot-assisted radical prostatectomy with and without trainee involvement
verfasst von
Andrew Chen
Saum Ghodoussipour
Micha B. Titus
Jessica H. Nguyen
Jian Chen
Runzhuo Ma
Andrew J. Hung
Publikationsdatum
14.11.2019
Verlag
Springer Berlin Heidelberg
Erschienen in
World Journal of Urology / Ausgabe 7/2020
Print ISSN: 0724-4983
Elektronische ISSN: 1433-8726
DOI
https://doi.org/10.1007/s00345-019-03010-3

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