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 A study using artificial intelligence to classify patient pain archetypes and identify risk for severe pain after total knee arthroplasty (TKA) has earned a Best of Meeting award at the 50th Annual Meeting of the American Society of Regional Anesthesia and Pain Medicine (ASRA).

“There is a need to better understand patients’ individual pain trajectories, and one of the most exciting approaches is to leverage artificial intelligence,” said Alexandra Sideris, PhD, director of the Pain Prevention Research Center at Hospital for Special Surgery (HSS) and one of the study authors.

“With our huge patient database at HSS, machine learning can analyze factors such as age, gender, BMI, and presurgical pain levels to predict which patients are at greater risk of severe pain after surgery.”

Armed with this information, the care team can tailor personalized pain management plans to meet patients’ needs.

Study Goals

The HSS researchers had several goals:

• Utilize machine learning to identify pain archetypes following total knee replacement
• Determine important features for predicting pain outcomes
• Classify patients at risk of severe pain in the immediate postoperative period

Their retrospective study included 17,200 patients who underwent TKA at HSS between April 1, 2021, and October 31, 2024.

“Using unsupervised machine learning, we identified 2 distinct pain archetypes in patients who underwent total knee replacement, which corresponded to those who experienced severe, difficult-to-control pain after surgery and those whose pain was relatively well controlled,” said Justin Chew, MD, PhD, a clinical fellow at HSS.

“We then utilized supervised machine learning to determine the most significant predictive factors for severe pain. In our study, risk factors included younger age, greater physical/mental impairment, higher BMI, and preoperative opioid or gabapentinoid use.”

Future Research

Dr. Sideris said that ongoing and future studies at HSS will continue to leverage AI with the goal of improving patient outcomes.

Although the award-winning study focused on the immediate postoperative period, additional studies will follow patients’ pain trajectory and recovery over longer periods of time to determine which strategies physicians can employ before surgery, intraoperatively, and in the immediate postoperative period to manage pain in high-risk patients.

Source

Chew J, Wang J, Sideris A, et al. Classification and stratification of patient pain archetypes following total knee arthroplasty: a machine learning approach. Presented at the 50th Annual Meeting of the American Society of Regional Anesthesia and Pain Medicine, held May 1-3, 2025, in Orlando, Florida.