Machine learning-based predictive model for the development of thrombolysis resistance in patients with acute ischemic stroke.

Journal: BMC neurology
PMID: 39187795

Abstract

BACKGROUND: The objective of this study was to establish a predictive model utilizing machine learning techniques to anticipate the likelihood of thrombolysis resistance (TR) in acute ischaemic stroke (AIS) patients undergoing recombinant tissue plasminogen activator (rt-PA) intravenous thrombolysis, given that nearly half of such patients exhibit poor clinical outcomes.

Authors

  • Xiaorui Wang
    Structural Biophysics Group, School of Optometry and Vision Sciences, Cardiff University, Cardiff, Wales, UK.
  • Song Luo
    Department of Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, 210002, P.R. China.
  • Xue Cui
    Department of Neurology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, 233004, China.
  • Hongdang Qu
    Department of Neurology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, 233004, China.
  • Yujie Zhao
    Department of Neurology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, 233004, China.
  • Qirong Liao
    Department of Neurology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, 233004, China.

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