PLoS ONE (Jan 2021)

Application of a decision tree model in the early identification of severe patients with severe fever with thrombocytopenia syndrome.

  • Bohao Wang,
  • Zhiquan He,
  • Zhijie Yi,
  • Chun Yuan,
  • Wenshuai Suo,
  • Shujun Pei,
  • Yi Li,
  • Hongxia Ma,
  • Haifeng Wang,
  • Bianli Xu,
  • Wanshen Guo,
  • Xueyong Huang

DOI
https://doi.org/10.1371/journal.pone.0255033
Journal volume & issue
Vol. 16, no. 7
p. e0255033

Abstract

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BackgroundSevere fever with thrombocytopenia syndrome (SFTS) is a serious infectious disease with a fatality of up to 30%. To identify the severity of SFTS precisely and quickly is important in clinical practice.MethodsFrom June to July 2020, 71 patients admitted to the Infectious Department of Joint Logistics Support Force No. 990 Hospital were enrolled in this study. The most frequently observed symptoms and laboratory parameters on admission were collected by investigating patients' electronic records. Decision trees were built to identify the severity of SFTS. Accuracy and Youden's index were calculated to evaluate the identification capacity of the models.ResultsClinical characteristics, including body temperature (p = 0.011), the size of the lymphadenectasis (p = 0.021), and cough (p = 0.017), and neurologic symptoms, including lassitude (pConclusionDecision trees can be applied to predict the severity of SFTS.