Predicting Postoperative Blood Transfusion in Elderly Patients Undergoing Total Hip and Knee Arthroplasty Using Machine Learning Models.

Journal: Risk management and healthcare policy
Published Date: May 21, 2025

Abstract

PURPOSE: With the aging population, the demand for total hip arthroplasty (THA) and total knee arthroplasty (TKA) has risen significantly. Elderly patients, especially those over 70 years, face a higher risk of perioperative bleeding and transfusion, increasing morbidity and mortality. Accurate transfusion risk prediction is vital for optimizing perioperative blood management. Traditional models often fail to capture complex factor interactions, whereas machine learning enhances predictive accuracy. This study aimed to develop predictive models for postoperative transfusion in elderly patients undergoing THA or TKA, identify key risk factors, and create an online prediction tool.

Authors

  • Dan Liang
    First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, People's Republic of China (D.L.); Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, People's Republic of China (D.L., Y.L., D.C., A.C., J.D., X.W.).
  • Yiming Pang
    Key Laboratory of Bone and Soft Tissue Injury Repair, The Second Hospital of Shanxi Medical University, Taiyuan, Shanxi, 030001, People's Republic of China.
  • Jingrui Huang
    Key Laboratory of Bone and Soft Tissue Injury Repair, The Second Hospital of Shanxi Medical University, Taiyuan, Shanxi, 030001, People's Republic of China.
  • Xianda Che
    Key Laboratory of Bone and Soft Tissue Injury Repair, The Second Hospital of Shanxi Medical University, Taiyuan, Shanxi, 030001, People's Republic of China.
  • Raorao Zhou
    Key Laboratory of Bone and Soft Tissue Injury Repair, The Second Hospital of Shanxi Medical University, Taiyuan, Shanxi, 030001, People's Republic of China.
  • Xueting Ding
    Animal Laboratory Center, Shanxi Medical University, Taiyuan, Shanxi, 030001, People's Republic of China.
  • Chunfang Wang
    Animal Laboratory Center, Shanxi Medical University, Taiyuan, Shanxi, 030001, People's Republic of China.
  • Litao Zhao
    School of Engineering Medicine, Beihang University, Beijing, 100191, China.
  • Yichen Han
    School of Sino-British Digital Media Art, Lu Xun Academy of Fine Arts, Shenyang, Liaoning, 110004, People's Republic of China.
  • Xueqin Rong
    Department of Pain Medicine, Sanya Central Hospital, Sanya, Hainan, 572000, People's Republic of China.
  • Pengcui Li
    Department of Orthopedics, The Second Hospital of Shanxi Medical University, Taiyuan,, Shanxi, 030001, China. lpc1977@163.com.

Keywords

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