Statistical models aim to improve surgical patient pain management

Editor's Note

Researchers at MIT and Massachusetts General Hospital (MGH) have developed statistical models to improve how anesthesiologists manage unconscious pain, or nociception, during surgery, according to a September 23 report in News Medical. Derived from over 18,000 minutes of surgery data across 101 abdominal procedures, the models aim to optimize drug dosing, reducing post-operative pain and side effects like nausea and delirium.

According to the article, the study used data from five physiological sensors, including heart rate and skin conductance, to analyze 49,878 instances of nociceptive stimuli (such as an incision) alongside the timing and dosage of pain-control drugs. The resulting set of models accurately predicted the body’s response to nociception during surgery, researchers say.

Published in The Proceedings of the National Academy of Sciences, the findings could provide anesthesiologists with real-time, objective data to complement their intuition and experience, researchers say, enhancing patient care by minimizing drug side effects and improving pain management.  

As reported in News Medical, the study aimed to advance earlier methods by tracking multiple physiological indicators, including both cardiovascular and electrodermal activity, and accounting for the effects of administered drugs. The next step is to make these models practical for everyday use in surgical settings, researchers say.