Three machine learning algorithms have identified patients likely to suffer extreme pain following surgery with about 80% accuracy each. The predictive assistance may help physicians appropriately prescribe alternate pain-management plans over dangerous and addictive opioids.
The research was presented at the 2020 annual meeting of the American Society of Anesthesiologists, held virtually Oct. 2 to 5.
Lead study author Mieke Soens, MD, of Harvard told attendees his team plans to integrate the models with the EHR at Brigham and Women’s Hospital to “provide a prediction of post-surgical pain for each patient.”
To build their models, Soens and colleagues reviewed data from almost 6,000 postsurgical patients spanning a variety of procedure categories. They found some 22% of these patients had been given high doses of morphine milligram equivalent in the first 24 hours after their operation.
Next they consulted pain-care experts and searched the literature to come up with 163 factors potentially predictive of severe postsurgical pain.
Armed with these insights, the team constructed three models—logistical regression, random forest and artificial neural networks—capable of poring through the patients’ medical records and pruning the 163 factors to only the most strongly predictive.
Comparing the models’ predictions with actual opioid use in the same patients, Soens and colleagues found all three had around 80% accuracy at pinpointing which patients suffered the greatest pain and needed the higher doses of opioids.
“Electronic medical records are a valuable and underused source of patient data and can be employed effectively to enhance patients’ lives,” Soens says in prepared remarks issued after the presentation. “Selectively identifying patients who typically need high doses of opioids after surgery is important to help reduce opioid misuse.”