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

Severe 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. From June to July 2020, 71 patients admitted to the Infectious Department of Joint Logistics Supp...

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Published in:PloS one 2021-07, Vol.16 (7), p.e0255033-e0255033
Main Authors: Wang, Bohao, He, Zhiquan, Yi, Zhijie, Yuan, Chun, Suo, Wenshuai, Pei, Shujun, Li, Yi, Ma, Hongxia, Wang, Haifeng, Xu, Bianli, Guo, Wanshen, Huang, Xueyong
Format: Article
Language:English
Subjects:
Analysis
Arachnids
Biology and Life Sciences
Body temperature
Computer and Information Sciences
Cough
Data mining
Decision trees
Decision-making
Demographics
Diagnosis
Disease control
Disease prevention
Engineering and Technology
Fatalities
Fever
Health aspects
Hypersomnia
Infectious diseases
Laboratories
Logistics
Mathematical models
Medicine and Health Sciences
Microorganisms
Nausea
Parameters
Pathogens
Patients
Public health
Research and Analysis Methods
Sample size
Signs and symptoms
Sleep disorders
Statistical analysis
Thrombocytopenia
Tremor
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creator Wang, Bohao
He, Zhiquan
Yi, Zhijie
Yuan, Chun
Suo, Wenshuai
Pei, Shujun
Li, Yi
Ma, Hongxia
Wang, Haifeng
Xu, Bianli
Guo, Wanshen
Huang, Xueyong
description Severe 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. From 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. Clinical 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 (p
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To identify the severity of SFTS precisely and quickly is important in clinical practice. From 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. Clinical 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 (p&lt;0.001), limb tremor (p&lt;0.001), hypersomnia (p = 0.009), coma (p = 0.018) and dysphoria (p = 0.008), were significantly different between the mild and severe groups. As for laboratory parameters, PLT (p = 0.006), AST (p&lt;0.001), LDH (p&lt;0.001), and CK (p = 0.003) were significantly different between the mild and severe groups of SFTS patients. A decision tree based on laboratory parameters and one based on demographic and clinical characteristics were built. Comparing with the decision tree based on demographic and clinical characteristics, the decision tree based on laboratory parameters had a stronger prediction capacity because of its higher accuracy and Youden's index. 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To identify the severity of SFTS precisely and quickly is important in clinical practice. From 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. Clinical 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 (p&lt;0.001), limb tremor (p&lt;0.001), hypersomnia (p = 0.009), coma (p = 0.018) and dysphoria (p = 0.008), were significantly different between the mild and severe groups. As for laboratory parameters, PLT (p = 0.006), AST (p&lt;0.001), LDH (p&lt;0.001), and CK (p = 0.003) were significantly different between the mild and severe groups of SFTS patients. A decision tree based on laboratory parameters and one based on demographic and clinical characteristics were built. Comparing with the decision tree based on demographic and clinical characteristics, the decision tree based on laboratory parameters had a stronger prediction capacity because of its higher accuracy and Youden's index. Decision trees can be applied to predict the severity of SFTS.</abstract><cop>San Francisco</cop><pub>Public Library of Science</pub><pmid>34329338</pmid><doi>10.1371/journal.pone.0255033</doi><tpages>e0255033</tpages><orcidid>https://orcid.org/0000-0001-8995-9274</orcidid><oa>free_for_read</oa></addata></record>
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subjects Analysis
Arachnids
Biology and Life Sciences
Body temperature
Computer and Information Sciences
Cough
Data mining
Decision trees
Decision-making
Demographics
Diagnosis
Disease control
Disease prevention
Engineering and Technology
Fatalities
Fever
Health aspects
Hypersomnia
Infectious diseases
Laboratories
Logistics
Mathematical models
Medicine and Health Sciences
Microorganisms
Nausea
Parameters
Pathogens
Patients
Public health
Research and Analysis Methods
Sample size
Signs and symptoms
Sleep disorders
Statistical analysis
Thrombocytopenia
Tremor
title Application of a decision tree model in the early identification of severe patients with severe fever with thrombocytopenia syndrome
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