Machine Learning Algorithms Can Be Reliably Leveraged to Identify Patients at High Risk of Prolonged Postoperative Opioid Use Following Orthopedic Surgery: A Systematic Review.

ABSTRACT: Background: Machine learning (ML) has emerged as a method to determine patient-specific risk for prolonged postoperative opioid use after orthopedic procedures. Purpose: We sought to analyze the efficacy and validity of ML algorithms in identifying patients who are at high risk for prolonged opioid use following orthopedic procedures. Methods: PubMed, EMBASE, and Web of Science Core Collection databases were queried for articles published prior to August 2021 for articles applying ML to predict prolonged postoperative opioid use following orthopedic surgeries. Features pertaining to patient demographics, surgical procedures, and ML algorithm performance were analyzed. Results: Ten studies met inclusion criteria: 4 spine, 3 knee, and 3 hip. Studies reported posto

SUBMITTER: Krivicich LM 

PROVIDER: S-EPMC11520020 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature