R. Dias2,7, M. Zenati5,7, H. Conboy6, J. Gabany5, D. Arney3,4, J. Goldman3,4,7, L. Osterweil6, G. Avrunin6, L. Clarke6, S. Yule1,2,7 1Brigham And Women’s Hospital,Department Of Surgery,Boston, MA, USA 2Brigham And Women’s Hospital,STRATUS Center For Medical Simulation,Boston, MA, USA 3Massachusetts General Hospital,Department Of Anesthesia,Boston, MA, USA 4Massachusetts General Hospital,MD PnP Program,Boston, MA, USA 5VA Boston Healthcare System,Division Of Cardiac Surgery,West Roxbury, MA, USA 6University Of Massachusetts,Amherst, MA, USA 7Harvard Medical School,Boston, MA, USA
Introduction:
Surgeons constantly deal with a high-demand operative environment that requires simultaneously processing a large amount of information. In certain situations, high demands imposed by surgical tasks may exceed surgeons’ cognitive resources, leading to a state of cognitive overload. This state may impact negatively on performance, increasing the risk of patient harm. The aim of this study was to investigate the concurrent validity of heart rate variability (HRV) analysis as a real-time and objective measure of surgeons’ cognitive load during cardiothoracic surgery. We also aimed to develop a behavioral framework that embeds surgeons’ physiological data into surgical process modeling for 14 unique high-level stages of cardiothoracic surgery.
Methods:
A heart rate sensor chest strap was used by a cardiac surgeon during 16 consecutive cardiothoracic procedures. Inter-beat intervals (R-R intervals) were captured via a validated smartphone app using a Bluetooth connection, and HRV parameters were calculated using spectral analysis. At the end of each procedure, a modified version of the SURG-TLX questionnaire, a validated tool assessing self-perceived cognitive load, was completed by the surgeon. Using audio-video recordings from real-life cardiac surgeries, the HRV parameters were embedded into the surgical workflow, enabling synchronized visualization of video, audio and cognitive load metrics during specific contexts and stages of cardiothoracic surgery.
Results:
The HRV parameters presenting statistically significant correlation with SURG-TLX score were standard deviation of normal to normal R-R intervals (SDNN) (r = -0.61, p < 0.001), HRV triangulation index (r = -0.69, p < 0.001), maximum low frequency (LF)/ high frequency (HF) ratio (r = 0.55, p < 0.027), and LF/HF ratio episodes > 2.0 (r = 0.80, p < 0.001). A total of 14 unique stages of coronary artery bypass graft (CABG) were identified and we built a behavioral analysis system incorporating video and physiological data (Figure 1).
Conclusion:
A statistically significant association between HRV parameters and SURG-TLX was found, validating HRV analysis as an objective method of measuring surgeons’ cognitive load. We also developed a framework that enables the synchronization of physiological-based cognitive metrics into the surgical process analysis. This behavioral framework can be used to monitor surgeons’ cognition in real-time, enhancing the understanding of how specific mental states can impact surgical performance and patient safety. Once this relationship is established, approaches seeking to mitigate the deleterious effects of cognitive overload can be developed.
