Machine learning-based prediction model for arteriovenous fistula thrombosis risk: a retrospective cohort study from 2017 to 2024.

Journal: BMC nephrology
Published Date: Jul 1, 2025

Abstract

BACKGROUND: Thrombosis of arteriovenous fistulas represents a prevalent complication among patients undergoing hemodialysis, characterized by a notably high incidence rate. Presently, there is an absence of robust assessment tools capable of predicting thrombosis occurrence. This study seeks to develop an interpretable machine learning model to forecast the risk of arteriovenous fistula thrombosis.

Authors

  • Peng Shu
    The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, No.26, Shengli Street, Jiang'an District, Wuhan, Hubei, China. 312855784@qq.com.
  • Ling Huang
    School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, 510640, China.
  • Xia Wang
    Department of Neurology, The Sixth People's Hospital of Huizhou City, Huizhou, China.
  • Zhuping Wen
    The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, No.26, Shengli Street, Jiang'an District, Wuhan, Hubei Province, China.
  • Yiqi Luo
    The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, No.26, Shengli Street, Jiang'an District, Wuhan, Hubei Province, China.
  • Fang Xu
    CAS Key Laboratory of Urban Pollutant Conversion, Department of Chemistry, University of Science & Technology of China, Hefei 230026, China; School of Medical Engineering, Hefei University of Technology, Hefei 230009, China.

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