Learning Curves for Two Fiberscopes in Simulated Difficult Airway Scenario With Cumulative Sum Method
Author
Özkan-Seyhan, Tülay
Sıvrıkoz, Nükhet
Çamcı, E
Orhan-Sungur, Mukadder
Altun, Demet
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Introduction The aims of this randomized prospective mannequin study were to determine the amount of attempts required for successful intubation using different fiberscopes (Bonfils and SensaScope) by inexperienced anesthesiologists in a difficult airway scenario and to build the associated learning curves. Methods Difficult airway simulation was achieved with tongue edema in mannequin. After approval of volunteers, we asked 15 anesthesiology residents without any experience with fiberscopes to intubate with each device in a random order. Intubation success (endotracheal intubation within 120 seconds), the degree of difficulty of intubation, and reality of simulation using a 10-point scale were recorded. Learning curves were generated with cumulative sum method. Results With Bonfils, 13 volunteers were able to pass lower decision boundary with a median number of 26 [95% confidence interval (CI) = 21.4-25.9] attempts, whereas in SensaScope, the same outcome was observed in 10 residents with a median number of attempts of 26 (95% CI = 23.5-32.5). Total success rate was found significantly higher with Bonfils compared with SensaScope (550/600 vs 512/600, respectively, P < 0.001). Intubation with Bonfils was considered as less difficult compared with SensaScope [median = 4 (95% CI = 3.32-4.42) and 6 (95% CI = 4.96-6.64), P = 0.01, respectively]. The reality of the simulation was rated as a median of 5 (95% CI = 4.37-5.8). Conclusions Although a similar number of attempts were required to reach predetermined competency for both fiberscopes, only 10 of residents were able to obtain the targeted success using SensaScope as compared with 13 with Bonfils. Inexperienced residents found intubation via Bonfils less difficult than SensaScope. High individual variability in obtaining competency observed in this study with cumulative sum analysis underlines the importance of defining success a priori to simulation, the need for follow-up of individual progress, and the need to offer adequate trials to achieve competency. Therefore, learning opportunities should be adapted accordingly.
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