New Statistical Model Aims to Improve Intraoperative Pain Management

Anesthesiologists face a critical challenge: balancing effective pain control during surgery with minimizing the risk of post-operative side effects from pain medications. A new study by researchers at MIT and Massachusetts General Hospital (MGH) has developed a set of statistical models to objectively measure pain perception—or nociception—during surgery, potentially transforming how anesthesiologists manage pain in real time. Published in Proceedings of the National Academy of Sciences, the study presents a promising advance toward real-time, personalized pain management during surgery.

The research team, led by Sandya Subramanian, PhD, analyzed data from over 18,000 minutes of surgery at MGH, including 49,878 nociceptive events, like incisions and cautery, in 101 abdominal surgeries. These models integrate data from five physiological sensors—measuring factors like heart rate and skin conductance—to track the body’s response to pain-inducing stimuli. By also incorporating drug administration details, the models can account for how pain medications affect nociception. Subramanian and her team developed multiple model versions, with a “random forest” model, which included drug information, performing the best. Unlike previous attempts that relied heavily on single metrics like heart rate variability, this multisensor approach enables a more comprehensive and accurate measure of nociception.

The study's findings are significant, as they represent the first models validated using actual surgical data, offering anesthesiologists a potentially reliable and objective tool to guide pain management. Currently, anesthesiologists rely on intuition and experience to decide on pain drug dosing. The new models aim to reduce guesswork by providing objective data on the body’s pain response, allowing anesthesiologists to tailor dosing more precisely. Accurate dosing could lower the incidence of side effects, such as nausea or delirium, while still ensuring patients receive adequate pain relief.

Looking ahead, the team’s goal is to make this technology practical for real-time use in the operating room. Future development could allow the models to inform “closed-loop” systems that automatically adjust drug dosages under the anesthesiologist's supervision, further enhancing precision and safety in pain management. Additionally, this approach holds promise for broader applications, including intensive care settings where precise pain management remains a challenge.

This study marks an essential step toward more personalized, objective approaches to managing surgical pain, potentially improving patient outcomes and setting a new standard for intraoperative care. The next phase of the research will focus on refining the model’s real-time capabilities, with the hope that it will soon be a vital tool for anesthesiologists and intensivists alike.