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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.
Journal of Robotic Surgery
|April 2, 2024
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.
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.
