Zdorovʹe Rebenka (Jun 2021)
Prediction of anemia of inflammation development in young children with acute inflammatory bacterial respiratory diseases
Abstract
Background. Randomization of pathogenetic factors that determine the risk of developing anemia of inflammation in young children with acute inflammatory bacterial diseases of the respiratory system, and the creation of a mathematical model for predicting its development were the purposes of the study. Materials and methods. The study groups included 80 children, the average age of the patients was 1.6 ± 0.3 years. The basic group consisted of 40 children with acute inflammatory bacterial respiratory diseases, which, taking into account the hematological picture, was divided into two subgroups: the first subgroup — 26 children with anemia of inflammation, which was determined 4–5 days after the onset of the disease; the second subgroup — 14 children without anemia. The comparison group enrolled 20 children with iron deficiency anemia without inflammatory manifestations. The control group consisted of 20 apparently healthy children. To identify the signs that are most associated with the development of anemia of inflammation, the method of factor analysis was used. The basis of modeling for the selection of factor complexes was the Spearman correlation matrix with the subsequent determination of the factor loading. The analysis of the prognostic significance of individual signs as risk factors for the development of anemia of inflammation in young children with acute inflammatory bacterial respiratory diseases was carried out based on calculating the relative risk (RR) index in 2 x 2 contingency tables with the determination of 95% confidence intervals (95% CI) and Pearson’s χ2 test. The most significant factors included informative signs with an RR value of more than 1.0. To predict the probability of developing anemia of inflammation, the method of binary logistic regression was used. Results. The factorial analysis results demonstrated five factors that have eigenvalues greater than 1.0 and describe 70.5 % of the total dispersion of the variables. Factor 1, the “factor of iron metabolism”, described 21.5 % of the total variance and included 2 variables: the number of red blood cells and the level of hepcidin. Factor 2, the “anemia factor”, described 14.6 % of the total dispersion and included hemoglobin levels. Factor 3, “oxidative stress factor”, described 12.7 % of the total dispersion and included 2 variables: nitrotyrosine content and IL-6 level. Factor 4, the “pro-inflammatory factor”, described 12.2 % of the total dispersion and included data on phospholipase A2 content and the severity of the inflammatory disease. Factor 5, “iron deposition factor”, described 8.9 % of the total dispersion and included ferritin level data. At the next stage, calculating the RR index, we identified five risk factors that have the greatest influence on the development of anemia of inflammation: ferritin content (≥ 73.2 ± 4.6 ng/ml), the presence of gram-negative microflora as a bacterial agent that caused the development of inflammatory diseases, the presence of febrile fever in the patient, repeated episode of inflammatory disease, hepcidin level (≥ 1.9 ± 0.11 ng/ml). Conclusions. Based on the results of the conducted factor analysis, a prognostic model was formed for the development of anemia of inflammation in young children with acute inflammatory bacterial respiratory diseases. According to the results of factor analysis, it was found that the leading contribution to the pathogenesis of the development of anemia of inflammation was made by disorders of iron metabolism against the background of the inflammatory process, including the processes of iron deposition; oxidative stress, and interleukin-6. It is advisable to use certain risk factors and the results of predictive modeling regarded to the group of high risk of developing anemia of inflammation in young children with acute inflammatory bacterial respiratory diseases.
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