Introduction: Endotracheal intubation (ETI) is a procedure that varies in difficulty because of patient characteristics and clinical conditions. Existing physical simulators do not encompass these variations. The Virtual Airway Skills Trainer for Endotracheal Intubation (VAST-ETI) was developed to provide different patient characteristics and high-fidelity haptic feedback to improve training.
Methods: We demonstrate the effectiveness of VAST-ETI as a training and evaluation tool for ETI. Construct validation was evaluated by scoring the performance of experts ( N = 15) and novices ( N = 15) on the simulator to ensure its ability to distinguish technical proficiency. Convergent and predictive validity were evaluated by performing a learning curve study, in which a group of novices ( N = 7) were trained for 2 weeks using VAST-ETI and then compared with a control group ( N = 9).
Results: The VAST-ETI was able to distinguish between expert and novice based on mean simulator scores ( t [88] = -6.61, P < 0.0005). When used during repeated practice, individuals demonstrated a significant increase in their score on VAST-ETI over the learning period ( F [11,220] = 7206, P < 0.001); however when compared with a control group, there was not a significant interaction effect on the simulator score. There was a significant difference between the simulator-trained and control groups ( t [12.85] = -2.258, P = 0.042) when tested in the operating room.
Conclusions: Our results demonstrate the effectiveness of virtual simulation with haptic feedback for assessing performance and training of ETI. The simulator was not able to differentiate performance between more experienced trainees and experts because of limits in simulator difficulty.
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