Multimodal Physiological Indices During Surgery Under Anesthesia

Monitoring nociception, the flow of information associated with harmful
stimuli through the nervous system even during unconsciousness, is critical
for proper anesthesia care during surgery. Currently, this is done by tracking
heart rate and blood pressure by eye. Monitoring objectively a patient's
nociceptive state remains a challenge, causing drugs to often be over- or
under-dosed intraoperatively. Inefficient management of surgical nociception
may lead to more complex post-operative pain management and side effects such
as post-operative cognitive dysfunction, particularly in elderly patients. We
collected a comprehensive and multi-sensor prospective observational dataset
focused on surgical nociception (101 surgeries, 18,582 minutes, 49,878
nociceptive stimuli), including annotations of all nociceptive stimuli
occurring during surgery and medications administered. Using this dataset, we
developed indices of autonomic nervous system activity based on
physiologically and statistically rigorous point process representations of
cardiac action potentials and sweat gland activity. Next, we constructed
highly interpretable supervised and unsupervised models with appropriate
inductive biases that quantify surgical nociception throughout surgery. Our
models track nociceptive stimuli more accurately than existing nociception
monitors.