Prediction of outcomes following intravenous thrombolysis in patients with acute ischemic stroke using serum UCH-L1, S100β, and NSE: a multicenter prospective cohort study employing machine learning methods.

Journal: Therapeutic advances in neurological disorders
Published Date:

Abstract

BACKGROUND: Acute ischemic stroke (AIS) is a leading cause of mortality and disability worldwide. Intravenous thrombolysis (IVT) improves recovery, but predicting outcomes remains challenging. Machine learning (ML) and biomarkers like ubiquitin carboxyl-terminal hydrolase L1 (UCH-L1), S100 calcium-binding protein β (S100β), and neuron-specific enolase (NSE) may enhance prognostic accuracy.

Authors

  • Ming-Ya Luo
    Stroke Center, Department of Neurology, The First Hospital of Jilin University, Changchun, China.
  • Yang Qu
    Shanghai Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Shanghai, China.
  • Peng Zhang
    Key Laboratory of Macromolecular Science of Shaanxi Province, School of Chemistry & Chemical Engineering, Shaanxi Normal University, Xi'an, Shaanxi 710062, China.
  • Reziya Abuduxukuer
    Stroke Center, Department of Neurology, The First Hospital of Jilin University, Changchun, China.
  • Li-Juan Wang
    College of Chemistry, Chemical Engineering and Materials Science, Collaborative Innovation Center of Functionalized Probes for Chemical Imaging in Universities of Shandong, Key Laboratory of Molecular and Nano Probes, Ministry of Education, Shandong Provincial Key Laboratory of Clean Production of Fine Chemicals, Shandong Normal University Jinan 250014 China cyzhang@sdnu.edu.cn.
  • Li-Chong Yang
    Department of Neurology, Jilin Neuropsychiatric Hospital, Siping, China.
  • Zhi-Guo Li
    Stroke Center, Department of Neurology, Siping Central People's Hospital, Siping, China.
  • Xiao-Dong Liu
    Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.
  • Ce Han
    Department of Radiation and Medical Oncology, The 1st Affiliated Hospital of Wenzhou Medical University, No.2 Fuxue Lane, Wenzhou, 325000, People's Republic of China.
  • Dan Li
    State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center, Chengdu, Sichuan 610041, PR China.
  • Wei-Jia Wang
  • Dian-Ping Lv
    Department of Neurology, Songyuan Hospital of Integrated Traditional Chinese and Western Medicine, Songyuan, China.
  • Ming Liu
    School of Land Engineering, Chang'an University, Xi'an 710064, China; Xi'an Key Laboratory of Territorial Spatial Information, School of Land Engineering, Chang'an University, Xi'an 710064, China. Electronic address: mingliu@chd.edu.cn.
  • Jian Gao
  • Jing Xu
    First Department of Infectious Diseases, The First Affiliated Hospital of China Medical University, Shenyang, China.
  • Yongfei Jiang
    Department of Neurology, Changchun People's Hospital, Changchun, China.
  • Hai-Nan Chen
    Department of Neurology, Dongliao First People's Hospital, Liaoyuan, China.
  • Fu-Jin Li
    Department of Neurology, Jilin People's Hospital, Jilin, China.
  • Li-Ming Sun
    Department of Neurology, Jilin City Hospital of Chemical Industry, Jilin, China.
  • Qi-Dong Sun
    Department of Neurology, Jilin City Hospital of Chemical Industry, Jilin, China.
  • Yingbin Qi
    Department of Neurology, Jilin Province People's Hospital, Changchun, China.
  • Si-Yin Sun
    Stroke Center, Department of Neurology, Jilin Central General Hospital, Jilin, China.
  • Yu Zhang
    College of Marine Electrical Engineering, Dalian Maritime University, Dalian, China.
  • Zhen-Ni Guo
    Stroke Center, Department of Neurology, The First Hospital of Jilin University, No. 1, Xinmin Street, Changchun 130021, China.
  • Yi Yang
    Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, Chengdu, Sichuan, China.

Keywords

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