Neonatal Endotracheal Intubation: Enhancing Training Through Computer Simulation and Automated Evaluation

Neonatal endotracheal intubation (ETI) is a time-sensitive resuscitation procedure essential for ventilation of newborns. It requires an unusually high level of skill due to the narrow airways, relatively large tongue, anterior glottic position, and low respiratory reserve of neonates . Given the difficulty of the procedure and the high rate of complications in untrained hands, effective training is crucial. However, intubation success rates for pediatric residents are low under current resuscitation training programs and show little improvement between years 1-3 of residency (23-25%). There is a pressing need to understand the factors that lead to poor training results and for innovative training modalities that can bridge the gap left by traditional training and thereby allow rapid skill acquisition. We hypothesize that current training and assessment methods suffer from 4 key weaknesses: (1) Poor realism: manikin and simulator-based training typically provide little variation in anatomy or difficulty level, which are key requirements for developing expertise and do not realistically model the look, feel, and motions of real tissue. (2) Subjective, highly variable, and resource-intensive assessment methods: training opportunities are limited by the availability of expert instructors. (3) Poor visualization: learners have poor knowledge about what went wrong and how to improve; they cannot see exactly what is going on inside the manikin or the patient and cannot directly monitor their actions relative to idealized, expert performance. (4) Assessment under artificially ideal conditions: assessments of ETI performance in classroom settings likely overestimate trainees' skill level because they do not mimic the stressors and distractions that are inherent in the real clinical environment. Technology-enhanced ETI simulators can resolve all of these key weaknesses: We have conducted preliminary work (Hahn, Li et al. 2016; Soghier, Li et al. 2014) on an augmented reality (AR) manikin simulator driven by the motions of the trainee and physical manikin in real time that 1) provides a quantitative assessment of ETI technique and 2) allows the trainee to visualize the motion of the laryngoscope inside the manikin. The assessment score can provide feedback during the performance, as well as constitute part of the evaluation of the trainee's skill. Work under this proposal will build on this preliminary work.

The specific aims are to: 1) extend the current augmented reality (AR) manikin simulator to a virtual reality (VR) computer simulator and validate; 2) extend and validate automated assessment and visualization algorithm for ETI; 3) study training effectiveness by testing groups of pediatric residents across 3 years to quantify the effect of technology-enhanced methods relative to the current training regimen in terms of both intubation performance on simulators and clinical outcomes in patients; and 4) assess performance under more realistic conditions.

VR Simulator

vr_simulator_teaser

A demo of the procedure

AR Simulator

A standard neonatal resuscitation mannequin head, laryngoscope and 3.0 endotracheal tube (ETT) were fitted with electromagnetic trackers to capture mannequin head motion and the motion of the laryngoscope and ETT with 6 degrees of freedom. Replica 3D computer models of the head, laryngoscope and endotracheal tube were then developed and registered to align completely with their physical counterparts. All motions were captured and mirrored by the 3D model.

Following a warm up period, participants were recorded performing endotracheal intubation three times. Participants recruited included expert neonatology attendings (more-than 60 patient intubations), novice nurse practitioners and novice pediatric residents (less-than 25 intubations). Data was processed and simultaneously sent to a laptop screen for continuous, real-time display of the mannequin, laryngoscope and ETT position and orientation. The software recorded each procedure allowing later review by the instructor.

Participants: Xiao Xiao, Shang Zhao, Meng Yan, James Hahn, Xiaoke Zhang, John Philbeck, Lamia Soghier MD, Wei Li, Rehab Alahmadi, Randall Burd MD [Publications]