ABSTRACT
OBJECTIVES: To study the risk factors for food sensitization and the influence of food sensitization on quality of life and clinical signs in children with atopic dermatitis (AD). METHODS: A retrospective analysis was performed on the medical data of 241 children with AD, including demographic features, age of onset, severity of AD, quality of life, physical examination results, skin prick test (SPT) results, serum total IgE levels, and eosinophil count. According to the results of SPT, the children were divided into a food sensitization group (n=127) and a non-food sensitization group (n=114). The multivariate logistic regression analysis was used to identify the risk factors for food sensitization in children with AD. RESULTS: The prevalence rate of food sensitization was 52.7% (127/241) in the children with AD. The multivariate logistic regression analysis showed that birth in autumn or winter, age of onset of AD<12 months, severe AD, and total IgE>150 IU/mL were risk factors for food sensitization (P<0.05). Compared with the non-food sensitization group, the food sensitization group had a significantly poorer quality of life (P=0.008) and significantly higher prevalence rates of non-specific hand/foot dermatitis and palmar hyperlinearity (P<0.05). Compared with the single food sensitization group, the multiple food sensitization group had more severe AD and a significantly higher proportion of children with exclusive breastfeeding or total IgE>150 IU/mL (P<0.05). CONCLUSIONS: The AD children born in autumn or winter, or those with early onset (<12 months), severe AD or total IgE>150 IU/mL have a higher risk of food sensitization. The AD children with food sensitization have a poorer quality of life and are more likely to develop non-specific hand/foot dermatitis and palmar hyperlinearity.
Subject(s)
Dermatitis, Atopic , Food Hypersensitivity , Allergens , Child , Cross-Sectional Studies , Humans , Immunoglobulin E , Infant , Quality of Life , Retrospective Studies , Risk FactorsABSTRACT
Purpose: In order to classify different types of health data collected in clinical practice of hernia surgery more effectively and improve the classification performance of support vector machine (SVM). Methods: A prospective randomized study was conducted. Sixty patients undergoing hernia repair under general anesthesia were randomly divided into two groups, PLMA group (n = 30) and ETT group (n = 30), for airway management. Heart rate, systolic blood pressure, diastolic blood pressure, mean arterial pressure, respiratory parameters and the incidence of complications related to ProSeal laryngeal mask airway (PLMA) and endotracheal tube (ETT) were collected in clinical experiments in order to evaluate the operation condition. On the basis of this experiment, at first, expert credibility is introduced to process the index value; secondly, the classification weight of the index is objectively determined by the information entropy output of the index itself; finally, a comprehensive classification model of support vector machine based on key sample set is proposed and its advantages are evaluated. Result: After classifying the experimental data, we found that SVM can accurately judge the effect of surgery by data. In this experiment, PLMA method is better than ETT method in xenon repair operation. Discussion: SVM has great accuracy and practicability in judging the outcome of xenon repair operation. Conclusion: The proposed index classification weight model can deal with the uncertainties caused by uncertain information and give the confidence of the uncertain information. Compared with the traditional SVM method, the proposed method based on SVM and key sample set greatly reduces the number of samples that misjudge the effect of samples, and improves the practicability of SVM method. It is concluded that PLMA is superior to the ETT technique to hernia surgical. The idea of constructing classification model based on key sample set proposed in this paper can also be used for reference in other data mining methods.
