Presentation
Characterizing the Learning Curve for Performing Surgical Airway Management
DescriptionThis study investigated the learning curve of behavioral performance and associated physiological changes during cricothyrotomy (CCT) skill training. CCT is a lifesaving emergency airway procedure with a rare incidence, required in only 1% of critical airway cases in the emergency department. This rarity poses significant challenges for training and retaining expertise, as opportunities to observe and practice in real-world settings are limited, leading to the poor success rate of 66% in the US military and 75% in UK hospitals. To address the limited evidence on CCT skill training, this study integrated multimodal measures to characterize the learning curve of skill acquisition and changes in psychophysiological activities across a longitudinal training process.
Ten medical students completed three days of CCT training, performing six repetitions per day on a SimMan 3G manikin. Each trial was limited to two minutes, and was separated by a two-minute rest period. Heart rate was recorded as inter-beat intervals (IBI) using a Polar H10 chest strap and gaze data was collected via Tobii Pro Glasses 2. Completion time across the 18 repetitions was modeled using Pegels’ discrete exponential learning curve model (Pegels, 1969). Heart rate variability (HRV) and gaze metrics were analyzed for the first repetition and the last repetitions of each day using repeated-measures ANOVA. They were further compared, through a one sample t-test, with data of emergency medicine program residents from our prior study conducted under the same protocol.
The behavioral learning curve over eighteen training trials showed a dynamic pattern in which completion time dropped rapidly during the first day, reached a level comparable to that of the emergency medicine residents by the sixth repetition, and shifted to a more gradual rate of improvement thereafter. A modest increase in completion time was observed at the onset of the second and third days, suggesting a potential forgetting effect between days. The exponential learning curve model, implemented within a nonlinear mixed-effects framework with random intercepts, provided a strong fit to the pooled trial-level data (R2 = 0.83, RMSE = 6.35). It estimated the learning rate parameter alpha at 0.68 (95% CI [0.64, 0.72]), with performance approaching an asymptotic completion time at 39.4 seconds (95% CI [38.0, 40.8]). Physiological measures revealed significant reductions in heart rate and the ratio of low-frequency to high-frequency power of the IBI (p < 0.05), suggesting a shift toward parasympathetic predominance, reflecting stress adaptation as well as improved autonomic regulation. The ratio of saccade to fixation count decreased significantly, indicating enhanced fixation stability and reduced scanning behavior. No significant differences were observed in pupil diameter or in the root mean square of successive differences (RMSSD).
These findings highlight the dynamic pattern of technical surgical skill acquisition rate, associated with the cognitive changes reflected by physiological activities. Post-hoc analyses showed that behavioral improvement was fastest during the initial repetitions, with a significant reduction on the first day (p < 0.05), whereas subsequent gains were not significant (p > 0.05). By contrast, stress adaptation and attentional regulation, although emerging from the outset, required a longer timeframe, showing significant adaptation only by the second or the third day. These insights can inform the design of evidence-based training programs for emergency procedures, addressing the limitations of traditional behavioral metrics in capturing psychological and cognitive factors, and providing a more comprehensive perspective that ultimately enhances clinician preparedness and patient safety.
Reference
Pegels, C. C. (1969). On startup or learning curves: An expanded view. AIIE Transactions, 1(3), 216-222.
Ten medical students completed three days of CCT training, performing six repetitions per day on a SimMan 3G manikin. Each trial was limited to two minutes, and was separated by a two-minute rest period. Heart rate was recorded as inter-beat intervals (IBI) using a Polar H10 chest strap and gaze data was collected via Tobii Pro Glasses 2. Completion time across the 18 repetitions was modeled using Pegels’ discrete exponential learning curve model (Pegels, 1969). Heart rate variability (HRV) and gaze metrics were analyzed for the first repetition and the last repetitions of each day using repeated-measures ANOVA. They were further compared, through a one sample t-test, with data of emergency medicine program residents from our prior study conducted under the same protocol.
The behavioral learning curve over eighteen training trials showed a dynamic pattern in which completion time dropped rapidly during the first day, reached a level comparable to that of the emergency medicine residents by the sixth repetition, and shifted to a more gradual rate of improvement thereafter. A modest increase in completion time was observed at the onset of the second and third days, suggesting a potential forgetting effect between days. The exponential learning curve model, implemented within a nonlinear mixed-effects framework with random intercepts, provided a strong fit to the pooled trial-level data (R2 = 0.83, RMSE = 6.35). It estimated the learning rate parameter alpha at 0.68 (95% CI [0.64, 0.72]), with performance approaching an asymptotic completion time at 39.4 seconds (95% CI [38.0, 40.8]). Physiological measures revealed significant reductions in heart rate and the ratio of low-frequency to high-frequency power of the IBI (p < 0.05), suggesting a shift toward parasympathetic predominance, reflecting stress adaptation as well as improved autonomic regulation. The ratio of saccade to fixation count decreased significantly, indicating enhanced fixation stability and reduced scanning behavior. No significant differences were observed in pupil diameter or in the root mean square of successive differences (RMSSD).
These findings highlight the dynamic pattern of technical surgical skill acquisition rate, associated with the cognitive changes reflected by physiological activities. Post-hoc analyses showed that behavioral improvement was fastest during the initial repetitions, with a significant reduction on the first day (p < 0.05), whereas subsequent gains were not significant (p > 0.05). By contrast, stress adaptation and attentional regulation, although emerging from the outset, required a longer timeframe, showing significant adaptation only by the second or the third day. These insights can inform the design of evidence-based training programs for emergency procedures, addressing the limitations of traditional behavioral metrics in capturing psychological and cognitive factors, and providing a more comprehensive perspective that ultimately enhances clinician preparedness and patient safety.
Reference
Pegels, C. C. (1969). On startup or learning curves: An expanded view. AIIE Transactions, 1(3), 216-222.
Event Type
Oral Presentations
TimeTuesday, March 241:30pm - 1:52pm EDT
LocationMorgan
Simulation and Education