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Related Concept Videos

Response Surface Methodology01:16

Response Surface Methodology

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Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
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Development and validation of metrics for a new RAPN training model.

Rui Jorge Dos Santos Almeida Farinha1,2, Adele Piro3, Angelo Mottaran4

  • 1Orsi Academy, Proefhoevestraat 12, 9090, Ghent, Belgium. ruifarinhaurologia@gmail.com.

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|April 2, 2024
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Summary

New metrics for robot-assisted partial nephrectomy (RAPN) training models were validated. Expert surgeons showed significantly fewer errors than novices, supporting simulation-based training for this complex procedure.

Keywords:
Construct validityRobot-assisted partial nephrectomySurgical trainingTraining model

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Area of Science:

  • Urology
  • Surgical Education
  • Medical Simulation

Background:

  • Robot-assisted partial nephrectomy (RAPN) is a complex surgical procedure requiring specialized training.
  • Currently, no validated performance metrics exist for RAPN training models (TM).

Purpose of the Study:

  • To develop and validate performance metrics for a novel RAPN training model.
  • To assess the construct and discriminative validity of these metrics.

Main Methods:

  • A Core Metrics Group adapted human RAPN metrics for a new RAPN TM.
  • A modified Delphi meeting achieved consensus on metric validation (8 Phases, 32 Steps, 136 Errors, 64 Critical Errors).
  • Novice and expert surgeons' performance was evaluated in the TM using these metrics.

Main Results:

  • No significant difference in procedure steps completed between novice and expert surgeons.
  • Experienced surgeons made 34% fewer total errors than novices.
  • Subgroup analysis showed expert surgeons with fewer errors significantly outperformed novices with fewer errors.

Conclusions:

  • The developed RAPN TM and its metrics demonstrated face, content, construct, and discriminative validity.
  • These findings support the implementation of simulation-based proficiency-based progression (PBP) training for RAPN.