Effects of food allergens on asthma exacerbations in schoolchildren with atopic asthma

Food Sensitization Impact on Asthma Attacks in Children According to Age Group

Introduction: The progression of allergy disorders is termed “atopic march.” Having one allergic disorder increases the likelihood of acquiring others. Asthma and food allergies often coexist. There are no thresholds for specific IgE (sIgE) associated with the presence of clinical symptoms. Each allergen shows a particular trend with age. Objective: Our study and analysis aim to identify food sensitization in children with asthma and evaluate its impact on asthma attacks and clinical control. Material and methods: As a part of a bigger study, 56 children (mean age 11.07 years (5.3–17.5), 38 boys, and 18 girls) with bronchial asthma were tested for total IgE and sIgE against food and inhalator allergens. All children performed baseline and post-BD spirometry and were assessed for asthma control. Results: In the studied population of children, sIgE against several food allergens was positive in the same patient. A significant correlation was found between the positive sIgE for milk and soy (p < 0.0001), for milk and egg yolk (p = 0.01), compared to milk and peanuts (p = 0.004), compared to egg yolk and fish (p < 0.0001), compared to egg yolk and casein (p < 0.001), and soy (p < 0.0001). The children who are positive for sIgE antibodies in cats, dogs, Cladosporium, Aspergillus, wormwood from aeroallergens and soy from food allergens have a higher risk of hospitalization for exacerbation of bronchial asthma. (p < 0.05). In the studied population, sensitization to food allergens among asthmatics does not contribute to the number of asthma attacks. Conclusions: Food sensitivity is associated with eczema, while mite sensitization is strongly associated with rhinitis and asthma. Food sensitization is not a risk factor for asthma exacerbation in children older than five years old.

Evaluation of Bayesian classifiers in asthma exacerbation prediction after medication discontinuation

Objective

The achievement of the optimal control of the disease is of cardinal importance in asthma treatment. As the control of the disease is sustained the medication should be gradually reduced and then stopped. Nevertheless, the discontinuation of asthma medication may lead to loss of disease control and eventually to an exacerbation of the disease. The goal of this paper is to examine the performance of Bayesian network classifiers in predicting asthma exacerbation based on several patient’s parameters such as objective measurements and medical history data.

Results

In this study several Bayesian network classifiers are presented and evaluated. It is shown that the proposed semi-naive network classifier with the use of Backward Sequential Elimination and Joining algorithm is able to predict if a patient will have an exacerbation of the disease after his last assessment with 93.84% accuracy and 90.9% sensitivity. In addition, the resulting structure and the conditional probability tables give a clear view of the probabilistic relationships between the used factors. This network may help the clinicians to identify the patients who are at high risk of having an exacerbation after stopping the medication and to confirm which factors are the most important.

Electronic supplementary material

The online version of this article (10.1186/s13104-018-3621-1) contains supplementary material, which is available to authorized users.