Credit score: Pixabay/CC0 Public Area
A research utilizing synthetic intelligence to categorise affected person ache archetypes and determine danger for extreme ache after knee alternative has earned a Better of Assembly award on the fiftieth Annual Assembly of the American Society of Regional Anesthesia and Ache Medication (ASRA). The dignity, which acknowledges excellence in scientific analysis, is awarded to a few of the highest 10 highest-scoring abstracts chosen by the ASRA Analysis Committee.
“It is an honor to have one of the top professional organizations in the field of regional anesthesia and pain medicine highlight the collaborative work of our department’s Pain Prevention Research Center,” stated Alexandra Sideris, Ph.D., director of the Ache Prevention Analysis Middle at HSS. “The award reflects our dedication to innovations in patient care and underscores the greater scientific community’s acknowledgment of our efforts.”
A couple of million individuals bear knee alternative surgical procedure every year in the USA, and people numbers proceed to rise, Dr. Sideris notes.
“There is a need to better understand patients’ individual pain trajectories, and one of the most exciting approaches is to leverage artificial intelligence. 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,” she stated.
Armed with this info, the care workforce can tailor personalised ache administration plans to fulfill sufferers’ wants.
The HSS researchers had a number of targets: make the most of machine studying to determine ache archetypes following complete knee alternative; decide vital options for predicting ache outcomes; and classify sufferers vulnerable to extreme ache within the speedy postoperative interval. The retrospective research included 17,200 sufferers who had complete knee replacements at HSS from April 1, 2021, to October 31, 2024.
“Using unsupervised machine learning, we identified two 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,” defined Justin Chew, MD, Ph.D., a medical fellow at HSS who offered the research on the ASRA assembly on Could 1.
“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.”
Dr. Sideris notes that ongoing and future research at HSS will proceed to leverage AI with the purpose of enhancing affected person outcomes. Whereas the research targeted on the speedy postoperative interval, she stated further research will observe sufferers’ ache trajectory and restoration over longer durations of time to find out which methods docs can make use of earlier than surgical procedure, intraoperatively and within the speedy postoperative interval to handle ache in high-risk sufferers.
Extra info:
Classification and stratification of affected person ache archetypes following complete knee arthroplasty: a machine studying method (2025)
Supplied by
Hospital for Particular Surgical procedure
Quotation:
AI efficiently identifies danger components linked to extra extreme ache after knee alternative (2025, Could 2)
retrieved 2 Could 2025
from https://medicalxpress.com/information/2025-05-ai-successfully-factors-linked-severe.html
This doc is topic to copyright. Aside from any truthful dealing for the aim of personal research or analysis, no
half could also be reproduced with out the written permission. The content material is supplied for info functions solely.
