Learning-curve-based Simulation Training for Bronchoscopic Intubation
Study Details
Study Description
Brief Summary
This study aims to determine whether a new learning-curve-based simulation training modality could contribute to a noninferiority regarding bronchoscopic-guided intubation time in patients compared with the reference fixed-training-time simulation training modality, albeit with less training time.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
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N/A |
Detailed Description
Questions remain concerning the optimal duration of simulator training for flexible optical bronchoscopic (FOB) intubation. The investigators' previous study demonstrated that an exponential curve could fit the learning curve of FOB intubation training on a high-fidelity simulator after logarithmical transformation of the procedure time. In brief, trainees could achieve a plateau of the learning curve after several procedures, i.e., further training might provide only negligible progress. According to the investigators' preliminary study, the training time for the majority might be less than one hour which is the dominant duration of simulator training in previous studies. By generating a learning curve from the initial several procedures on a simulator, it is possible to predict when a trainee could grow saturated individually. It is hypothesized that this new learning-curve-based training modality could contribute to non-inferior FOB intubation time in patients compared with the reference fixed-training-time (1 hour) training modality, albeit with less training time. The noninferiority margin is defined according to the reported FOB intubation time and the training effect in previous studies. Moreover, it is plausible that this new training modality might even present a higher success rate and better performance of FOB intubation, considering each trainee following the new training has to achieve the individual goal that precludes an outlier from failing to have enough training that might occur in reference fixed-training-time training.
Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| Experimental: new training In this new learning-curve-based training modality, after participants complete 16 procedures on a high-fidelity simulator, an individual learning curve will be generated using the previously validated equation: ln(γ)=γ_0 e^(-kn)+γ_∞ where γ is procedure time, n is previous experiences.[2] Other parameters and their 95% Confidence Interval (CI) can be obtained after curve fitting. And e^(γ_∞ ) is the asymptote of this curve. Then the trainees will continue the training. If the following procedure time falls into the 95% CI of the asymptote for three consecutive times,[6] the individual training goal is considered achieved.[2] |
Other: learning-curve-based training modality
It is an individual duration of simulation training that is based on the previous performance of FOB on a simulator.
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| Active Comparator: reference training In this reference fixed-training-time training modality, participants will receive training with a high-fidelity simulator for 1h. |
Other: fixed-training-time training modality
It is a training duration of 1 hour which is the dominant duration of simulator training in previous studies.[1]
|
Outcome Measures
Primary Outcome Measures
- time to complete FOB intubation [Just after the completion of the intubation, in one minute]
It is a noninferiority outcome. After training, one FOB intubation will be performed within one week. Patients scheduled for elective surgery which requires tracheal intubation will be included. Those with an anticipated or known difficult airway or American Society of Anesthesiology (ASA) Physical Status Classification equaling or exceeding III will be excluded. General anesthesia is performed by the attending anesthetists who does not involve in this study. Patients are mask ventilated for 2min after induction. Assistance with jaw thrust is provided during intubation. Criteria for termination of intubation are SpO 2 of 90% or less, airway tissue trauma during the intubation attempt, attempt time exceeding 4min, or the anesthetist in the charge being unwilling to continue.
Secondary Outcome Measures
- duration of the training [From the training initiating to its ending, a sum will be calculated in 24 hours after the training]
The total duration of the simulation training on a high-fidelity simulator
- total number of procedures on the simulators [From the training initiating to its ending, a sum will be calculated in 24 hours after the training]
The total number of procedures performing FOB on a high-fidelity simulator
- success rate of intubation [Just after the completion of the intubation, in one minute]
Successful intubation in patients
- performance of FOB intubation on simulators [After the training, the scores will be acquired from the data storage of the simulators in 24 hours]
The last 3 intubations on simulators will be assessed. The simulator assesses the performance based on the validated five-point global rating scale (GRS) of fiberoptic bronchoscope manipulation, which is standardized to a 100-scale score (0=worst, 100=best) automatically by the simulators.
- performance of FOB intubation in patients [One week]
The intubation will be recorded and sent to an assessor, who will assess the performance using the five-point global rating scale (GRS, 8 items, 5 points each, up to a total of 40 points, 0=worst, 40=best) of fiberoptic bronchoscope manipulation in one week. To unify the results, the GRS will be standardized to a 100-scale score (0=worst, 100=best) in the analysis.
Eligibility Criteria
Criteria
Inclusion Criteria:
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anesthesia residents or interns at Peking University People's Hospital, Beijing, China or Tibet Autonomous Region People's Hospital, Lhasa, Tibet, China;
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have performed less than five FOB intubations in patients;
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consent to this study. -
Exclusion Criteria:
Those who have received standardized training will be excluded.
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Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | Peking University People's Hospital | Beijing | Beijing | China | 100044 |
| 2 | Tibet autonomous region people's hospital | Lhasa | Tibet | China |
Sponsors and Collaborators
- Peking University People's Hospital
- Tibet Autonomous Region People's Hospital
Investigators
- Principal Investigator: Yi Feng, Peking University People's Hospital
Study Documents (Full-Text)
None provided.More Information
Publications
- Chandra DB, Savoldelli GL, Joo HS, Weiss ID, Naik VN. Fiberoptic oral intubation: the effect of model fidelity on training for transfer to patient care. Anesthesiology. 2008 Dec;109(6):1007-13. doi: 10.1097/ALN.0b013e31818d6c3c.
- Jiang B, Ju H, Zhao Y, Yao L, Feng Y. Comparison of the Efficacy and Efficiency of the Use of Virtual Reality Simulation With High-Fidelity Mannequins for Simulation-Based Training of Fiberoptic Bronchoscope Manipulation. Simul Healthc. 2018 Apr;13(2):83-87. doi: 10.1097/SIH.0000000000000299.
- Mahmood K, Wahidi MM, Shepherd RW, Argento AC, Yarmus LB, Lee H, Shojaee S, Berkowitz DM, Van Nostrand K, Lamb CR, Shofer SL, Gao J, Davoudi M. Variable Learning Curve of Basic Rigid Bronchoscopy in Trainees. Respiration. 2021;100(6):530-537. doi: 10.1159/000514627. Epub 2021 Apr 13.
- Naik VN, Matsumoto ED, Houston PL, Hamstra SJ, Yeung RY, Mallon JS, Martire TM. Fiberoptic orotracheal intubation on anesthetized patients: do manipulation skills learned on a simple model transfer into the operating room? Anesthesiology. 2001 Aug;95(2):343-8.
- Roh GU, Kang JG, Han JY, Chang CH. Utility of oxygen insufflation through working channel during fiberoptic intubation in apneic patients: a prospective randomized controlled study. BMC Anesthesiol. 2020 Nov 10;20(1):282. doi: 10.1186/s12871-020-01201-9.
- Wong DT, Mehta A, Singh KP, Leong SM, Ooi A, Niazi A, You-Ten E, Okrainec A, Patel R, Singh M, Wong J. The effect of virtual reality bronchoscopy simulator training on performance of bronchoscopic-guided intubation in patients: A randomised controlled trial. Eur J Anaesthesiol. 2019 Mar;36(3):227-233. doi: 10.1097/EJA.0000000000000890.
- 2018PHB088