Development and Validation of a Machine Learning Algorithm to Predict the Risk of Blood Transfusion after Total Hip Replacement in Patients with Femoral Neck Fractures: A Multicenter Retrospective Cohort Study.

Journal: Orthopaedic surgery
PMID: 38951965

Abstract

OBJECTIVE: Total hip arthroplasty (THA) remains the primary treatment option for femoral neck fractures in elderly patients. This study aims to explore the risk factors associated with allogeneic blood transfusion after surgery and to develop a dynamic prediction model to predict post-operative blood transfusion requirements. This will provide more accurate guidance for perioperative humoral management and rational allocation of medical resources.

Authors

  • Jieyang Zhu
    Department of Orthopedics, Affiliated Hospital of Jiaxing University, Jiaxing, China.
  • Chenxi Xu
    General Practice Department, Tongxiang Wutong Street Community Health Service Center, Jiaxing, China.
  • Yi Jiang
    Institute of Genomic Medicine, Wenzhou Medical University, Wenzhou 325035, China.
  • Jinyu Zhu
    Department of Orthopedics, Affiliated Hospital of Jiaxing University, Jiaxing, China.
  • Mengyun Tu
    Department of Clinical Laboratory, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, China.
  • Xiaobing Yan
    Key Laboratory of Brain-like Neuromorphic Devices and Systems of Hebei Province, Key Laboratory of Optoelectronic Information Materials of Hebei Province, Hebei University, Baoding, Hebei, 071002, China.
  • Zeren Shen
    Department of Mechanical & Industrial Engineering, University of Toronto, Toronto, Canada.
  • Zhenqi Lou
    Department of Orthopedics, Affiliated Hospital of Jiaxing University, Jiaxing, China.

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