Prediction of laparoscopic skills: objective learning curve analysis

Abstract

Introduction: Prediction of proficiency of laparoscopic skills is essential to establish personalized training programs. Objective assessment of laparoscopic skills has been validated in a laparoscopic box trainer with force, motion and time recognition. The aim of this study is to investigate whether acquiring proficiency of laparoscopic skills can be predicted based on performance in such a training box.

Methods: Surgical residents in their first year of training performed six different tasks in the Lapron box trainer. Force, motion and time data, three objective measures of tissue manipulation and instrument handling, were collected and analyzed for the six different tasks. Linear regression tests were used to predict the learning curve and the number of repetitions required to reach proficiency.

Results: A total of 6010 practice sessions performed by 42 trainees from 13 Dutch hospitals were assessed and included for analysis. Proficiency level was determined as a mean result of seven experts performing 42 trials. Learning curve graphs and prediction models for each task were calculated. A significant relationship between force, motion and time during six different tasks and prediction of proficiency was present in 17 out of 18 analyses.

Conclusion: The learning curve of proficiency of laparoscopic skills can accurately be predicted after three repetitions of six tasks in a training box with force, path length and time recognition. This will facilitate personalized training programs in laparoscopic surgery.

Keywords: Laparoscopy; Learning curve; Minimally invasive surgery; Prediction of skill; Simulation; Training.

Fig. 1

Post and Sleeve learning curves

Fig. 2

Example of prediction calculation