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

<h4>Background</h4>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.<h4>Methods</h4>From June to July 2020, 71 patients adm...

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Autores principales:	Bohao Wang, Zhiquan He, Zhijie Yi, Chun Yuan, Wenshuai Suo, Shujun Pei, Yi Li, Hongxia Ma, Haifeng Wang, Bianli Xu, Wanshen Guo, Xueyong Huang
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4ee4f2d410b04a95b4aca16bf546e49c
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spelling	oai:doaj.org-article:4ee4f2d410b04a95b4aca16bf546e49c2021-12-02T20:08:50ZApplication of a decision tree model in the early identification of severe patients with severe fever with thrombocytopenia syndrome.1932-620310.1371/journal.pone.0255033https://doaj.org/article/4ee4f2d410b04a95b4aca16bf546e49c2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0255033https://doaj.org/toc/1932-6203<h4>Background</h4>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.<h4>Methods</h4>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.<h4>Results</h4>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<0.001), limb tremor (p<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<0.001), LDH (p<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.<h4>Conclusion</h4>Decision trees can be applied to predict the severity of SFTS.Bohao WangZhiquan HeZhijie YiChun YuanWenshuai SuoShujun PeiYi LiHongxia MaHaifeng WangBianli XuWanshen GuoXueyong HuangPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0255033 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Bohao Wang Zhiquan He Zhijie Yi Chun Yuan Wenshuai Suo Shujun Pei Yi Li Hongxia Ma Haifeng Wang Bianli Xu Wanshen Guo Xueyong Huang Application of a decision tree model in the early identification of severe patients with severe fever with thrombocytopenia syndrome.
description	<h4>Background</h4>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.<h4>Methods</h4>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.<h4>Results</h4>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<0.001), limb tremor (p<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<0.001), LDH (p<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.<h4>Conclusion</h4>Decision trees can be applied to predict the severity of SFTS.
format	article
author	Bohao Wang Zhiquan He Zhijie Yi Chun Yuan Wenshuai Suo Shujun Pei Yi Li Hongxia Ma Haifeng Wang Bianli Xu Wanshen Guo Xueyong Huang
author_facet	Bohao Wang Zhiquan He Zhijie Yi Chun Yuan Wenshuai Suo Shujun Pei Yi Li Hongxia Ma Haifeng Wang Bianli Xu Wanshen Guo Xueyong Huang
author_sort	Bohao Wang
title	Application of a decision tree model in the early identification of severe patients with severe fever with thrombocytopenia syndrome.
title_short	Application of a decision tree model in the early identification of severe patients with severe fever with thrombocytopenia syndrome.
title_full	Application of a decision tree model in the early identification of severe patients with severe fever with thrombocytopenia syndrome.
title_fullStr	Application of a decision tree model in the early identification of severe patients with severe fever with thrombocytopenia syndrome.
title_full_unstemmed	Application of a decision tree model in the early identification of severe patients with severe fever with thrombocytopenia syndrome.
title_sort	application of a decision tree model in the early identification of severe patients with severe fever with thrombocytopenia syndrome.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/4ee4f2d410b04a95b4aca16bf546e49c
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Application of a decision tree model in the early identification of severe patients with severe fever with thrombocytopenia syndrome.

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