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AI predicts patients at highest risk for severe pain, increased opioid use post-surgery

Artificial intelligence (AI) used in machine learning models can predict which patients are at highest risk for severe pain after surgery, and help determine who would most benefit from personalized pain management plans that use non-opioid alternatives, suggests new research being presented at the ANESTHESIOLOGY 2020 annual meeting.

Some patients experience more after and need higher doses of opioids for longer periods of time, which increases their risk for abuse disorder. By knowing which patients are at higher risk for severe post-surgical , physician anesthesiologists can create an anesthesia plan using non-opioid alternatives—such as nerve blocks, epidurals and other medications—to more effectively address pain and reduce the need for opioids.

Currently physicians use time-consuming questionnaires to identify patients at higher risk for severe post-surgical pain, asking about their history of anxiety, sleep quality and depression. In this study, researchers sought a faster, more effective method using machine learning, where a system learns and evolves based on data it is provided. They created three machine learning models that analyzed patients’ electronic medical records, which identified that younger age, higher body mass index, female gender, pre-existing pain and prior opioid use were the most predictive factors of post-surgical pain.

“We plan to integrate the models with our to provide a prediction of post-surgical pain for each patient,” said Mieke A. Soens, M.D., lead author of the study and an anesthesiologist at Brigham and Women’s Hospital and anesthesiology instructor at Harvard Medical School, Boston. “If the patient is determined to be at high risk for severe post-surgical pain, the physician anesthesiologist can then adjust the patient’s anesthesia plan to maximize non-opioid pain management strategies that would reduce the need for opioids after surgery.”

In the two-part study, researchers looked at data from 5,944 patients who

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You should not take this test if you are pregnant or think you may be pregnant. Radiation can be harmful to an unborn baby.

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