An anesthesia provider must critically manage patients in the operating room and respond to any physiologic aberration in an environment that contains a large amount of sensory information. Relevant information must be appropriately assimilated and irrelevant information filtered.1 Attention to the pulse oximeter, a vital piece of monitoring equipment, needs to be particularly acute during induction and emergence, the period when attention is heavily directed toward the patient and when errors are most likely to occur.2,3
Dividing attention and triaging clinical events and tasks are imperative to the anesthesiologist, and these challenges are amplified under circumstances in which the physician’s attentional resources are at or beyond capacity.4 The consequences of divided attention have been the subject of a significant amount of psychological, neuroscience, and human factors research, including applied studies asking how such abilities might be improved.4–10 Indeed, training in which subjects make use of combined visual and auditory (i.e., multisensory) cues has been shown to be able to improve sensory performance and perception.10–12 The ability to integrate information across multiple senses leads to improvements in detection,13,14 accuracy,15,16 and response times.17,18
We assessed whether perceptual training would improve the ability of resident anesthesiologists to detect frequency changes used to report oxygen saturation.
This study was approved by the Vanderbilt University Medical Center IRB, and written consent was obtained from all participants. Anesthesia residents were recruited by electronic mail. Forty electronic mail messages were sent to residents in the Department of Anesthesiology who were postgraduate years 1 to 4 and scheduled on a clinical rotation that was amenable to study participation. Fifteen residents replied and participated in the study. All participants had normal hearing and normal or correct-to-normal vision. Before multisensory perceptual training, all 15 resident anesthesiologists completed a session measuring their sensitivity to changes in pulse oximetry frequency.4 This session tested sensitivity to changes in simulated pulse oximeter frequency over 3 levels of attentional load, as well as in 2 levels of noise (in quiet and with typical operating room background noise (Fig. 1, A–C)). The order of the 6 conditions was randomly permuted but uniformly applied to each participant. Participants then underwent multisensory perceptual training regimens where visual rings and auditory beeps were presented with varying stimulus onset asynchronies (SOAs) (Fig. 2A), after which they completed a posttraining pulse oximetry session identical to that on experiment day 1. The details of the paradigm and training are contained in Figure 1.4
Response time and accuracy (dependent variables) were separately examined using a linear mixed-effects model with fixed effects for attentional load (low, medium, or high), background noise (noise or no noise), and training (before or after). All possible interactions were considered. A random intercept for each participant was used to account for the correlation among repeated measurements and to reflect the random sampling nature of participant enrollment.
A likelihood ratio (chunk) test was used to assess the interaction and overall effects of each factor. Contrasts for the effect of training in each combination of attentional load and noise level were summarized using a point estimate and Wald-type 95% confidence interval (CI) test. Quantile-quantile (QQ) plots were used to assess residual normality. Levene test was used to assess homogeneity of variance among experimental stratum, with a significance threshold of P < 0.01. The symbols
, and tk represent the partial η2, χ2, and Student t statistic with k degrees of freedom.
Residual diagnostics were evaluated in the pulse oximetry accuracy and response time data. Levene test resulted in P values 0.71 and 0.044 for response time and accuracy, respectively. The QQ plot for response time residuals was unremarkable. The QQ plot for accuracy indicated that residuals were sufficiently heavy tailed at the lower end to warrant additional scrutiny. We elected to use a conservative significance threshold of 0.01 for P values associated with the accuracy end point. A conventional threshold of 0.05 was used for tests associated with response time. CIs that had failed to include an appropriate null value were also considered statistically significant.
We conducted a bootstrap-based simulation study to assess the post hoc power and type-I error associated with the likelihood ratio test for an overall training effect, by resampling the observed sample data at the participant level. To estimate type-I error, the null hypothesis was imposed on the sample data by subtracting the estimated main and interaction effects due to training. The estimated type-I error was within 1% point of the nominal value for both response time (estimated type-I error: 0.048) and accuracy (estimated type-I error: 0.015). Given the magnitude of the effect of training in these data, the power to detect such an effect under the current design and test was >0.99 for response time, and >0.76 for accuracy.
Environmental noise significantly impaired residents’ accuracy and response time to detect changes in tones designed to simulate decreasing oxygen saturation by pulse oximetry. Attentional load also significantly impaired response time. Multisensory training significantly improved both accuracy and response time (Table 1). A significant interaction was observed between attentional load and training for response time (P = 0.0058,
= 14.52). No other 2- or 4-way interactions were significant.
Figures 3 and 4 illustrate the effects of training for each combination of attentional load and noise on accuracy and response time, respectively. In quiet, there were significant improvements in response time for the high-attentional load condition (95% CI, 9–71 milliseconds, P = 0.011, t166 = −2.58) but not for the medium (95% CI, −6 to 58 milliseconds, P = 0.11, t166 = −1.62) or low (95% CI, −32 to 30 milliseconds, P = 0.95, t166 = 0.06) attentional load conditions. The corresponding effects on accuracy were not significant for the high (95% CI, 0%–10%, P = 0.048, t166 = 1.99), medium (95% CI, −2% to 8%, P = 0.30, t166 = 1.04), or low (95% CI, −5% to 5%, P = 0.91, t166 = −0.11) attentional load conditions. In noise, there was a significant improvement in accuracy and response time for the high-attentional load condition (95% CI, 4%–14%, P = 0.0004, t166 = 3.60 and 95% CI, 40–103 milliseconds, P < 0.0001, t166 = −4.52, respectively) but not for the medium (95% CI, −3% to 7%, P = 0.45, t166 = 0.75 and 95% CI, 0–62 milliseconds, P = 0.052, t166 = −1.96) or low load condition (95% CI, −5% to 4%, P = 0.83, t166 = −0.21, and 95% CI, −36 to 27 milliseconds, P = 0.76, t166 = 0.31).
To ensure increases in pulse oximetry performance were not due to decreased attention to the visual tasks after training, an identical mixed-effects regression analysis was conducted on visual task accuracies and response times with training. The only significant overall effect on accuracy or response time was due to attentional load (P < 0.0001,
= 116.99, and P < 0.0001,
= 212.45, respectively). Relative to low-attentional load, high load was associated with lower accuracy (95% CI, 25%–40%) and increased response time (95% CI, 239–318 milliseconds). Importantly, there was no evidence for an effect of training on either accuracy or response time (P = 0.35,
= 4.40, and P = 0.33,
= 4.59), ensuring that the training effects seen with auditory pulse oximetry performance were not the result of residents learning to attend to the auditory stimulus at the expense of the primary visual task.
These results show that multisensory training successfully improved individuals’ multisensory temporal acuity (Fig. 2). The mean temporal binding window was significantly reduced from 350 milliseconds pretraining to 208 milliseconds posttraining (P = 0.0044, t14 = 3.39, Cohen’s d = 1.03). A reduction was observed in 14 of 15 residents (P = 0.0010), where only half are expected under the null hypothesis (i.e., training is ineffective).
In the current study, we attempted to improve performance on a fundamental operating room task, monitoring changes in pulse oximetry, by training resident anesthesiologists to better integrate visual and auditory cues. This multisensory perceptual training of anesthesia residents resulted in statistically significant increases in their ability to detect changes in arterial oxygen saturation via auditory pitch changes and increased the speed at which they could do so in an environment most similar to that seen in the operating room, one that was highly attentionally demanding and noisy. Adverse outcomes are typically the result of a number of minor, compounding events, such as failure to detect a change in arterial oxygen saturation.19 Lack of monitor vigilance and inattention are 2 of the most commonly cited factors associated with critical incidents in anesthesia.20,21 While a lab setting has the benefit of isolating independent variables, follow-up studies are needed to test how these perceptual improvements transfer to an operating room setting or in a simulator.22–24 While not tested in the current study, previous work has shown preservation of training effects over the course of months.7,9,10 In this study, we have improved anesthesiologists’ performance through a perceptual training paradigm that is novel to anesthesiology, but that has been used successfully in basic science research settings in the field of sensory perception.7,8,10 We believe that our findings are generalizable to anesthesia training programs nationwide. While future work should seek to extend these results to more realistic clinical settings, these findings are a first step in a line of research that has the capacity to save lives and minimize errors in the operating room by using effective and time-efficient multisensory training to improve an anesthesia provider’s subtle unisensory pulse oximetry pitch perception in a dynamic and attentionally demanding perioperative environment.
Name: Joseph J. Schlesinger, MD.
Contribution: This author helped design and conduct the study, analyze the data, and write the manuscript.
Attestation: Joseph J. Schlesinger has seen the original study data, reviewed the analysis of the data, approved the final manuscript, and is the author responsible for archiving the study files.
Name: Ryan A. Stevenson, PhD.
Contribution: This author helped design and conduct the study, analyze the data, and write the manuscript.
Attestation: Ryan Stevenson has seen the original study data, reviewed the analysis of the data, and approved the final manuscript.
Name: Matthew S. Shotwell, PhD.
Contribution: This author helped analyze the data, and write the manuscript.
Attestation: Matthew Shotwell has seen the original study data, reviewed the analysis of the data, and approved the final manuscript.
Name: Mark T. Wallace, PhD.
Contribution: This author helped design the study, analyze the data, and write the manuscript.
Attestation: Mark Wallace has seen the original study data, reviewed the analysis of the data, and approved the final manuscript.
This manuscript was handled by: Maxime Cannesson, MD, PhD.
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© 2014 International Anesthesia Research Society
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