Autonomic Activity and Surgical Flow Disruptions in Healthcare Providers during Cardiac Surgery

Cardiac surgery represents a complex sociotechnical environment relying on a combination of technical and non-technical team-based expertise. Surgical flow disruptions (SFDs) may be influenced by a variety of sources, including social, environmental, and emotional factors affecting healthcare providers (HCPs). Many of these factors can be readily observed, except for emotional factors (i.e. distress), which represents an underappreciated yet critical source of SFDs. The aim of this study was to demonstrate the sensitivity of autonomic activity metrics to detect an SFD during cardiac surgery. We integrated heart rate variability (HRV) analysis with observation-based annotations to allow data triangulation. Following a critical medication administration error by the anesthesiologist in-training, data sources were consulted to identify events precipitating this near-miss event. Using pyphysio, an open-source physiological signal processing package, we analyzed the attending anesthesiologists' HRV, specifically the low frequency (LF) power, high frequency (HF) power, LF/HF ratio, standard deviation of normal-to-normal (SDNN), and root mean square of the successive differences (RMSSD) as indicators of ANS activity. A heightened SNS response in the attending anesthesiologists' physiological arousal was observed as elevations in LF power and LF/HF ratio, as well as depressions in HF power, SDNN, and RMSSD prior to the near-miss event. The attending anesthesiologist subjectively confirmed a state of high distress induced by task-irrelevant environmental factors during this time. Qualitative analysis of audio/video recordings objectively revealed that the autonomic nervous system (ANS) activation detected was temporally associated with an argument over operating room management. This study confirms that it is possible to recognize detrimental psychophysiological influences in cardiac surgery procedures via advanced HRV analysis. To our knowledge, ours is the first such case demonstrating ANS activity coinciding with strong self-reported emotion during live surgery using HRV. Despite extensive experience in the cardiac OR, transient but intense emotional changes may have the potential to disrupt attention processes in even the most experienced HCP. A primary implication of this work is the possibility to detect real-time ANS activity, which could enable personalized interventions to proactively mitigate downstream adverse events. Additional studies on our large database of surgical cases are underway and new studies are actively being planned to confirm this preliminary observation.

[1]  Sandra D Monteiro,et al.  Predictable chaos: a review of the effects of emotions on attention, memory and decision making , 2014, Advances in Health Sciences Education.

[2]  Ute Fischer,et al.  The Role of Affect in Naturalistic Decision Making , 2010 .

[3]  P. Pronovost,et al.  Effectiveness and efficiency of root cause analysis in medicine. , 2008, JAMA.

[4]  D. Bai,et al.  Stress and Heart Rate Variability: A Meta-Analysis and Review of the Literature , 2018, Psychiatry investigation.

[5]  M. Zenati,et al.  Systematic review of measurement tools to assess surgeons' intraoperative cognitive workload , 2018, The British journal of surgery.

[6]  U. Rajendra Acharya,et al.  Heart rate variability: a review , 2006, Medical and Biological Engineering and Computing.

[7]  Mohammad Maifi Hasan Khan,et al.  The Effects of Risk and Role on Users' Anticipated Emotions in Safety-Critical Systems , 2018, HCI.

[8]  S. H. Parker,et al.  Measurement of physiological responses to acute stress in multiple occupations: A systematic review and implications for front line healthcare providers. , 2019, Translational behavioral medicine.

[9]  I. Benseñor,et al.  Reference values for short-term resting-state heart rate variability in healthy adults: Results from the Brazilian Longitudinal Study of Adult Health-ELSA-Brasil study. , 2018, Psychophysiology.

[10]  S. Segerstrom,et al.  Heart Rate Variability Reflects Self-Regulatory Strength, Effort, and Fatigue , 2007, Psychological science.

[11]  Daniel McDuff,et al.  Remote measurement of cognitive stress via heart rate variability , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[12]  Eduardo Salas,et al.  Cardiac Measures of Cognitive Workload: A Meta-Analysis , 2019, Hum. Factors.

[13]  Rossana Castaldo,et al.  Acute mental stress assessment via short term HRV analysis in healthy adults: A systematic review with meta-analysis , 2015, Biomed. Signal Process. Control..

[14]  Andrius Dzedzickis,et al.  Human Emotion Recognition: Review of Sensors and Methods , 2020, Sensors.

[15]  F. Shaffer,et al.  An Overview of Heart Rate Variability Metrics and Norms , 2017, Front. Public Health.

[16]  F. Shaffer,et al.  Heart Rate Variability: New Perspectives on Physiological Mechanisms, Assessment of Self-regulatory Capacity, and Health risk , 2015, Global advances in health and medicine.

[17]  Cesare Furlanello,et al.  pyphysio: A physiological signal processing library for data science approaches in physiology , 2019, SoftwareX.

[18]  David Giles,et al.  Validity of the Polar V800 heart rate monitor to measure RR intervals at rest , 2015, European Journal of Applied Physiology.

[19]  Douglas A Wiegmann,et al.  Disruptions in surgical flow and their relationship to surgical errors: an exploratory investigation. , 2007, Surgery.

[20]  F. Shaffer,et al.  A healthy heart is not a metronome: an integrative review of the heart's anatomy and heart rate variability , 2014, Front. Psychol..

[21]  Marco A. Zenati,et al.  Cognitive Engineering to Improve Patient Safety and Outcomes in Cardiothoracic Surgery. , 2020, Seminars in thoracic and cardiovascular surgery.

[22]  A. Malliani,et al.  Heart rate variability. Standards of measurement, physiological interpretation, and clinical use , 1996 .