Development and validation of machine learning-based prediction model for central venous access device-related thrombosis in children.

Journal: Thrombosis research
PMID: 39889316

Abstract

BACKGROUND: Identifying independent risk factors and implementing high-quality assessment tools for early detection of patients at high risk of central venous access device (CVAD)-related thrombosis (CRT) plays a critical role in delivering timely preventive interventions and reducing the incidence of CRT. Approaches for identifying the risk of CRT in children have not been well-researched.

Authors

  • Maoling Fu
    Department of Nursing, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Road, Wuhan, Hubei 430030, China; School of Nursing, Tongji Medical College, Huazhong University of Science and Technology, 13 Aviation Road, Wuhan, Hubei 430030, China.
  • Xinyu Li
    School of Pharmacy, Binzhou Medical University, Yantai, China.
  • Zhuo Wang
    Sichuan Center for Disease Control and Prevention, Chengdu 610500, China.
  • Qiaoyue Yang
    Department of Nursing, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Road, Wuhan, Hubei 430030, China; School of Nursing, Tongji Medical College, Huazhong University of Science and Technology, 13 Aviation Road, Wuhan, Hubei 430030, China.
  • Genzhen Yu
    Department of Nursing, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Road, Wuhan, Hubei 430030, China. Electronic address: 691007@tjh.tjmu.edu.cn.

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