Criteria for early etiological diagnosis of serous meningitis in children
Keywords:children, serous meningitis, enteroviruses, herpesviruses, clinic, laboratory diagnostics, diagnostic algorithms
To improve early etiological diagnosis of serous meningitis in children, the differential diagnostic algorithms have been developed.
Methods. 220 children with serous meningitis(SM) aged 1 to 17 years were divided into 3 groups depending on the etiological factor: 1) enterovirus SM (n=130); 2) herpesvirus SM (n=14); 3) enterovirus SM combined with persistent herpes virus infection (n=76). In the alternative groups of patients (in all combinations), the differential diagnostic significance of clinical and laboratory parameters has been evaluated by means of Wald–Genkin heterogeneous sequential procedure. All studied traits were divided into gradations and then differential diagnostic coefficients (DC) and diagnostic information (I) were obtained separately for the clinical indicators, CSF indicators and full blood count. The indicators were distributed in the descending order of their self-descriptiveness that, in future, taking into account the most significant of them, allowed to form a generalized diagnostic algorithms in each comparison group.
Results. Differential algorithmic diagnosis is obtained by means of algebraic summation of DC until the diagnostic threshold is reached. For 95 percent confidence level, the threshold sum is DK≥13,0. Etiologic diagnosis of SM involves the use of all three algorithms, that is, differentiation is conducted between SM herpesvirus, enterovirus etiology and SM enterovirus etiology in combination with persistent herpes virus infection. When reaching the diagnostic threshold in favor of certain etiology, the appropriate diagnosis is made. In case the diagnostic threshold for two etiological factors is reached, the final diagnosis is formulated on the basis of the differential diagnosis data.
Conclusions. The developed algorithms of early etiological diagnosis of SM in children are highly reliable (≥95%) and minimize the number of indicators required for diagnosis with a given level of reliability
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Copyright (c) 2015 Лариса Анатольевна Ходак, Виктория Игоревна Браилко, Татьяна Ивановна Навет, Ольга Васильевна Книженко, Наталья Ивановна Скрипченко
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