Novel bimanual haptic simulator for epidural loss-of-resistance detection: a pilot study assessing movement strategies and performance across anesthesiologist experience levels

Abstract

Background

Correct identification of the epidural space requires extensive training for technical proficiency. This study explores a novel bimanual haptic simulator designed for the precise insertion of an epidural needle based on loss-of-resistance (LOR) detection, providing realistic dual-hand force feedback.

Methods

The simulator, equipped with two haptic devices connected to a Tuohy needle and an LOR syringe, was designed to simulate the tissues’ resistive forces felt by the user during the procedure, offer anatomical variability and record detailed performance metrics for personalized feedback. We assessed the simulator’s validity through attempts conducted by anesthesiologists of varying experience levels, analyzing success rates, error sizes, the impact of patient weight, and movement strategies.

Results

Performance varied significantly with the expertise level of the anesthesiologists. Experts achieved higher success rates and smaller errors, demonstrating the simulator’s construct validity. Patient weight influenced outcomes, with higher weights leading to more failed epidurals and lower weights resulting in increased accidental dural punctures. Successful attempts typically involved more probing movements, particularly near the epidural space.

Conclusions

The innovative bimanual haptic simulator shows significant potential as a tool for assessing epidural skills and differentiating expertise levels. Its ability to provide realistic, concurrent feedback for both hands, adapt to patient anatomical variations, and generate precise metrics for performance evaluation distinguishes it from existing simulators. However, further research is necessary to establish its value as a training tool. Planned studies will focus on developing an effective training protocol and evaluating the long-term educational impact of the simulator, determining whether its integration into residency programs can improve patient outcomes.