Predicting periprosthetic joint infection in primary total knee arthroplasty: a machine learning model integrating preoperative and perioperative risk factors.

Journal: BMC musculoskeletal disorders
PMID: 40069724

Abstract

BACKGROUND: Periprosthetic joint infection leads to significant morbidity and mortality after total knee arthroplasty. Preoperative and perioperative risk prediction and assessment tools are lacking in Asia. This study developed the first machine learning model for individualized prediction of periprosthetic joint infection following primary total knee arthroplasty in this demographic.

Authors

  • Yuk Yee Chong
    Department of Orthopedics and Traumatology, The University of Hong Kong, Hong Kong SAR, China.
  • Chun Man Lawrence Lau
    Department of Orthopedics and Traumatology, The University of Hong Kong, Hong Kong SAR, China. laucml@hku.hk.
  • Tianshu Jiang
  • Chunyi Wen
  • Jiang Zhang
    College of Electrical Engineering and Information Technology, Sichuan University, Chengdu, 610065, China.
  • Amy Cheung
    Department of Orthopedics and Traumatology, Queen Mary Hospital, Hong Kong SAR, China.
  • Michelle Hilda Luk
    Department of Orthopedics and Traumatology, Queen Mary Hospital, Hong Kong SAR, China.
  • Ka Chun Thomas Leung
    Department of Orthopedics and Traumatology, Queen Mary Hospital, Hong Kong SAR, China.
  • Man Hong Cheung
    Department of Orthopedics and Traumatology, The University of Hong Kong, Hong Kong SAR, China.
  • Henry Fu
    Department of Orthopedics and Traumatology, The University of Hong Kong, Hong Kong SAR, China.
  • Kwong Yuen Chiu
    Department of Orthopedics and Traumatology, The University of Hong Kong, Hong Kong SAR, China.
  • Ping Keung Chan
    Department of Orthopedics and Traumatology, The University of Hong Kong, Hong Kong SAR, China. cpk464@yahoo.com.hk.

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