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Prediction of Postoperative Pain and Side Effects of Patient-Controlled Analgesia in Pediatric Orthopedic Patients Using Machine Learning: A Retrospective Study

Year: 2025

Type: article

Abstract: Background/Objectives: Appropriate postoperative management, especially in pediatric patients, can be challenging for anesthesiologists. This retrospective study used machine learning to investigate the effects and complications of patient-controlled analgesia (PCA) in children undergoing orthopedic surgery. Methods: The medical records of children who underwent orthopedic surgery in a single tertiary hospital and received intravenous and epidural PCA were analyzed. Predictive models were developed using machine learning, and various demographic, anesthetic, and surgical factors were investigated to predict postoperative pain and complications associated with PCA. Results: Data from 1968 children were analyzed. Extreme gradient boosting effectively predicted moderate postoperative pain for the 6–24-h (area under curve (AUC): 0.85, accuracy (ACC): 0.79) and 24–48-h (AUC: 0.89, ACC: 0.87) periods after surgery. The factors that predicted moderate postoperative pain included the pain score immediately before the measurement period, the total amount of opioid infused, and age. For predicting side effects during the 6–24-h period after surgery, a least absolute shrinkage and selection operator model (AUC: 0.75, ACC: 0.64) was selected, while a random forest model (AUC: 0.91, ACC: 0.87) was chosen for the 24–48-h period post-surgery. The factors that predicted complications included the occurrence of side effects immediately before the measurement period, the total amount of opioid infused before the measurement period, and age. Conclusions: This retrospective study introduces machine-learning-based models and factors aimed at forecasting moderate postoperative pain and complications of PCA in children undergoing orthopedic surgery. This research has the potential to enhance postoperative pain management strategies for children.

Source: Journal of Clinical Medicine

DOI: 10.3390/jcm14051459

Authors: Young-Eun Joe, Nayoung Ha, Woojoo Lee, Hyo-Jin Byon

Institutions: Asan Medical Center, University of Ulsan, Seoul National University, Yonsei University, Yonsei University Health System

Language: English

Topic: Anesthesia and Pain Management

Subfield: Surgery

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,137