The participants of both groups viewed video files showing fiberscope-guided intubation on mannequins, with instructors, and familiarized themselves with the bronchoscope, its basic operation, and key points in its maintenance. Group VRS participants then performed fiberoptic manipulation from mouth to carina on the VRS 25 times, whereas group M participants did the same on the mannequin. Both groups received oral instructions and facilitation where necessary, from the same instructor. Brief summaries of their performance and comments were given to each participant after each practice. After the 25 training manipulations, participants in both groups performed fiberoptic bronchoscope manipulation from mouth to carina five times on a mannequin, as an examination.
The procedure time of each bronchoscope examination from mouth to carina was recorded. A validated five-point global rating scale (GRS) of fiberoptic bronchoscope manipulation (score 1 to 5, where 5 is best; Appendix 1)7 was used to assess the performance of the trainees. Procedure time and GRS were assessed by the same investigator, who was blinded to the allocation of groups. Performance confidence before and after simulation training of each participant was measured using a five-point Likert rating scale (score 1 to 5, where 5 is highest).8
There were no significant differences between the two groups in sex, age, working experience, and previous exposure to fiberoptic intubation (Table 1).
The efficacy of the training was measured by comparing the procedure time and GRS scores within the two groups and between them. There was no time effect within the five attempts in terms of procedure time or GRS score in either of the groups (Table 2). There were no statistically significant differences in mean procedure time [13.7 (6.6) vs. 11.9 (4.1) seconds, t′ = 1.101, P = 0.278] or GRS score [3.9 (0.4) vs. 3.8 (0.4), t = 0.791, P = 0.433] between the groups, when analyzed using Student's t test.
A validated method of generating a learning curve from fiberoptic intubation data, by Smith et al,9 was used. The log version of an exponential equation was followed, which was modified to fit the highly skewed data:
ln(γ) = γ0e−kn+ γ∞
where γ is procedure time from inserting the bronchoscopy to view the carina, γ0 is additional time required when there is no previous experience, n = previous experiences, γ∞ is asymptote, and k is learning constant, which measures the steepness of the curve.
To restore the original units of measurement, the following values were anti-logged:
Novice time = EXP (γ0 + γ∞), where novice time is the time taken by an anesthesia resident with no fiberoptic intubation experience;
Expert time = EXP (γ∞), where expert time is the time taken by a fully trained anesthetist.
The learning constant k was used to develop the half-life (t1/2) of the learning curve:
t1/2 = −ln(1/2)/k = 0.693/k.
Half-life represented the number of practice sessions needed to halve the distance between the actual time and the expert time. Thus, the training was half completed after the first half-life time. The plateau in the learning curve was achieved after five half-life times, which meant that the training was more than 95% completed. Figure 4 shows the learning curves fitted to the pooled data. Through the learning curves, the hypothetical expert time and estimate of the number of trainings needed to complete the training, which was defined as five half-life times in this study, could be calculated. In group VRS, the best-fit values were the following: γ∞ = 2.233, γ0 = 1.139, k = 0.184, t1/2 = 3.766, and goodness of fit (R2) = 94.4%. Expert time was 9.3 [95% confidence interval (CI) = 8.7–10.0] seconds. Nineteen (95% CI = 15–26) endoscopies were needed to achieve plateau in the learning curve. In group M, the best-fit values were the following: γ∞ = 2.369, γ0 = 1.273, k = 0.143, t1/2 = 4.846, and goodness of fit (R2) = 96.5%. Expert time was 10.7 (95% CI 9.8–11.6) seconds. Twenty-four (95% CI = 20–32) endoscopies were required to complete the training (Table 3).
Analyzed using paired-samples t test, the self-confidence of trainees within both groups improved significantly [VRS: 1.8 (0.7) vs. 3.9 (0.8), t = 8.321, P < 0.001; M = 2.0 (0.7) vs. 4.0 (0.6), t = 13.948, P < 0.001]. However, no statistically significant difference in the changes of self-confidence was found between the two groups [Student's t test = 2.1 (1.2) vs. 2.0 (0.7), t′ = 0.454, P = 0.653].
Simulation-based training is widely used in the training of fiberoptic intubation and has been proven effective.3–5 It has been suggested that fiberoptic intubation simulation should be separated into different tasks (part-task training),10 although one recent study5 found that part-task training did not provide any additional benefit, indicating that fiberoptic intubation could be trained in one comprehensive process (whole-task training). Comprehensive simulation training relies on the use of high-fidelity anatomic simulation such as VRS. However, because of its expensive price, VRS cannot be widely used in developing countries such as China, where cost remains a determining factor in many situations. High-fidelity mannequins are also an effective simulation-based teaching modality to evaluate the training efficacy of fiberoptic intubation. Although mannequins also have precise designs, they are relatively inexpensive. Although either modality can be used in fiberoptic intubation training, the efficacy and efficiency of these methods have never been detailed or compared. By generating learning curves, our study determined the number of training sessions needed to complete the training task for both simulation modalities, to provide crucial information for future training curricula.
In the final examination of our study, there was no time effect within the five attempts in terms of procedure time or GRS score in either group. This means that after training, trainees in both groups did not show any further improvement, indicating that they had all reached a plateau in learning curve in fiberoptic bronchoscope manipulation and that the teaching goal of the simulation had been achieved. Although participants in group VRS were not familiar with the mannequin before the final test, the decreases in time of each procedure during testing were not large enough to be significant. Thus, we inferred that sufficient training with VRS enabled the participants to use the new modality without significant study effects. Although the group M participants were trained on the mannequin, this did not confer any significant advantage over the VRS group in the final assessment.
Comparing the efficacy of training between the two groups, there were no significant differences in operating time and capability, indicating that after adequate training, there was no difference in teaching efficacy between the models.
According to the learning curve, trainees in the VRS group became saturated after 19 attempts at fiberoptic manipulation, that is, they achieved the teaching goal. The same was true after 24 attempts in group M. This suggests that the use of VRS enables the achievement of a learning plateau in the teaching goal earlier than a mannequin, implying superior teaching efficiency for VRS.
Goals in fiberoptic intubation teaching include not only speed but also accuracy and lack of harm. Maintenance of the best view and other fine manipulation skills were repeatedly addressed during training and assessed by the GRS scores in the final examination. Most of the trainees obtained sufficient accuracy and improved manipulation ability. Fiberoptic intubation in anesthesia differs to other fiberscopy, for example, that used by the pulmonary therapist, in that anesthesiologists need to perform it precisely and harmlessly within a limited period. Performing the procedure promptly is particularly important in anesthesia, because airways may be difficult to manage in clinical practice, especially when spontaneous breathing of the patient is no longer preserved. Speedy and successful fiberoptic intubation decreases the chance of hypoxia and any subsequent harm and is the key factor in patient safety. Some studies have indicated that performance confidence should be included as a marker of competence.8 In our study, trainees in both groups improved their performance confidence after training, with no statistical difference between the groups.
There are some limitations to this study. First, the mannequin used by group M for training and in the final evaluation was the same, which may have generated bias and flawed the study. It would be more convincing if a new method was chosen for the examination. In addition, we did not follow up the residents' performance in clinical practice, acknowledging that their learning outcome might not translate equally in the long term. Finally, this was a single institution study with relatively small number of participants, and the comparison was only between one type of VRS and one type of high-fidelity mannequin. The results may not be broadly applicable to high-fidelity mannequins or VRS produced by other companies.
Mannequins and VRS are both valid learning modalities for fiberoptic manipulation, achieving similar effects after adequate deliberate practice. High-fidelity mannequin-based simulation training might be inferior to VRS in terms of teaching efficiency; however, VRS is more expensive. A balance between time commitment and equipment costs should be considered when designing sustainable training curricula for fiberoptic intubation.
The authors thank Dr. Yue Dong at Mayo Clinic for his critically reading and valuable suggestions.
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3. De Oliveira GS Jr, Glassenberg R, Chang R, et al. Virtual airway simulation to improve dexterity among novices performing fibreoptic intubation. Anaesthesia
4. Giglioli S, Boet S, De Gaudio AR, et al. Self-directed deliberate practice with virtual fiberoptic intubation improves initial skills for anesthesia residents. Minerva Anestesiol
5. Nilsson PM, Russell L, Ringsted C, et al. Simulation-based training
in flexible fibreoptic intubation: a randomised study. Eur J Anaesthesiol
6. Failor E, Bowdle A, Jelacic S, et al. High-fidelity simulation of lung isolation with double-lumen endotracheal tubes and bronchial blockers in anesthesiology resident training. J Cardiothorac Vasc Anesth
7. Marsland C, Larsen P, Segal R, et al. Proficient manipulation of fibreoptic bronchoscope to carina by novices on first clinical attempt after specialized bench practice. Br J Anaesth
8. Jiang G, Chen H, Wang S, et al. Learning curves
and long-term outcome of simulation-based thoracentesis training for medical students. BMC Med Educ
9. Smith JE, Jackson AP, Hurdley J, et al. Learning curves
for fibreoptic nasotracheal intubation when using the endoscopic video camera. Anaesthesia
10. Boet S, Bould MD, Diemunsch PA. Looking beyond model fidelity. Anesthesiology
APPENDIX 1. Five-Point GRS of Fiberoptic Bronchoscopic Manipulation Ability
Keywords:© 2018 Society for Simulation in Healthcare
Simulation-based training; flexible fiberoptic intubation; virtual reality; mannequins; learning curves