Predictors of formation of functional disorders of gastrointestinal tract after norovirus infection

The purpose of the work is the development of an informationally significant mathematical and statistical model for predicting the development of functional disorders of the gastrointestinal tract in children after a norovirus infection.55 children with norovirus infection aged 1 to 7 years (mean ag...

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Bibliographic Details
Main Authors: K. D. Ermolenko, N. V. Gonchar, Yu. V. Lobzin, S. G. Grigoriev
Format: Article
Language:Russian
Published: Journal Infectology 2017-06-01
Series:Журнал инфектологии
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Online Access:https://journal.niidi.ru/jofin/article/view/599
Description
Summary:The purpose of the work is the development of an informationally significant mathematical and statistical model for predicting the development of functional disorders of the gastrointestinal tract in children after a norovirus infection.55 children with norovirus infection aged 1 to 7 years (mean age 2,8±0,2 years, boys – 25, girls – 30) in a hospital and within 12 months after acute infection were observed due to development of a mathematical discriminant model of the prognosis for the formation of functional disorders of the gastrointestinal tract (FDGIT) with the purpose of their subsequent prevention. Statistically significant differences in the prognosis «probability of development of FDGIT» and «the lack of probability of FDGIT» were found on the following grounds: duration of preservation of norovirus infection symptoms (p=0,056), detection of opportunistic microorganisms in the intestinal microbiota in titles not less than 5 lg CFU/g (p=0,02), detection of bacterial overgrowth syndrome in the small intestine (p=0,001). These signs can be considered as a determinant of the probability of development of FDGIT. This model based on the available definition of informative clinical and laboratory signs characterizing the severity of the disease and the state of the intestinal microbiota of patients with norovirus infection. The information capacity of a statistically significant model (p<0.01) is 86,8%.
ISSN:2072-6732