The physiology of intraoperative error: using electrokardiograms to understand operator performance during robot-assisted surgery simulations

Abstract

BACKGROUND: No platform for objective, synchronous and on-line evaluation of both intraoperative error and surgeon physiology yet exists. Electrokardiogram (EKG) metrics have been associated with cognitive and affective features that are known to impact surgical performance but have not yet been analyzed in conjunction with real-time error signals using objective, real-time methods. METHODS: EKGs and operating console point-of-views (POVs) for fifteen general surgery residents and five non-medically trained participants were captured during three simulated robotic-assisted surgery (RAS) procedures. Time and frequency-domain EKG statistics were extracted from recorded EKGs. Intraoperative errors were detected from operating console POV videos. EKG statistics were synchronized with intraoperative error signals. RESULTS: Relative to personalized baselines, IBI, SDNN and RMSSD decreased 0.15% (S.E. 3.603e-04; P = 3.25e-05), 3.08% (S.E. 1.603e-03; P < 2e-16) and 1.19% (S.E. 2.631e-03; P = 5.66e-06), respectively, during error. Relative LF RMS power decreased 1.44% (S.E. 2.337e-03; P = 8.38e-10), and relative HF RMS power increased 5.51% (S.E. 1.945e-03; P < 2e-16). CONCLUSIONS: Use of a novel, on-line biometric and operating room data capture and analysis platform enabled detection of distinct operator physiological changes during intraoperative errors. Monitoring operator EKG metrics during surgery may help improve patient outcomes through real-time assessments of intraoperative surgical proficiency and perceived difficulty as well as inform personalized surgical skills development.