Changes in serum inflammatory factors after hip arthroplasty and analysis of risk factors for prosthesis loosening

Ming He1, Qingsheng Zhu1, Dayu Yin1

  • 1Department of Arthrology I, Xi'an International Medical Center Hospital No. 777 Xitai Road, Chang'an District, Xi'an 710100, Shaanxi, China.

Insights

Elevated inflammatory cytokines, including IL-1β, IL-6, and TNF-α, are linked to hip prosthesis loosening. A machine learning model incorporating these factors and clinical data can predict loosening risk.

Area of Science:

  • Orthopedic Surgery
  • Biomarkers
  • Machine Learning in Medicine

Background:

  • Prosthesis loosening is a major complication after hip arthroplasty.
  • Identifying reliable predictors for prosthesis loosening is crucial for improving patient outcomes.

Purpose of the Study:

  • To investigate the association between serum levels of interleukin-1 beta (IL-1β), interleukin-6 (IL-6), and tumor necrosis factor-alpha (TNF-α) and hip prosthesis loosening.
  • To develop and validate a predictive model for prosthesis loosening using machine learning.

Main Methods:

  • Retrospective analysis of 501 patients undergoing hip arthroplasty.
  • Serum cytokine levels and clinical data were collected and analyzed.
  • Machine learning algorithms (Lasso regression, decision tree, random forest) and nomogram were used for model development and validation.

Main Results:

  • Serum levels of IL-1β, IL-6, and TNF-α were significantly higher in patients with prosthesis loosening (P<0.0001).
  • Osteoporosis and postoperative infection were identified as risk factors.
  • The predictive model, incorporating IL-1β, IL-6, TNF-α, osteoporosis, and infection, achieved an AUC of 0.763.

Conclusions:

  • Elevated serum IL-1β, IL-6, and TNF-α are associated with hip prosthesis loosening.
  • Osteoporosis, postoperative infection, and inflammatory cytokines are independent risk factors.
  • The developed machine learning model effectively predicts prosthesis loosening risk, aiding in preventative strategies.
Abstract