Background: Extensive knowledge of allergic multimorbidities is required to improve the management of allergic diseases with the industrialization of China. However, the demography and allergen distribution patterns of allergic multimorbidities in China remain unclear, despite the increasing prevalence of allergies. Methods: This was a real-world, cross-sectional study of 1273 outpatients diagnosed with one or more allergic diseases in Guangzhou, the most populated city of southern China, with leading industrial and commercial centers, between April 2021 and March 2022. Seven allergic diseases (allergic rhinitis (AR), asthma (AS)/cough variant asthma (CVA), atopic dermatitis (AD)/eczema, food allergy (FA), allergic conjunctivitis (AC), drug allergy (DA), and anaphylaxis) were assessed. Positive rates of sensitization to different allergens were measured using an allergen detection system of the UniCAP (Pharmacia Diagnostics, Sweden) instrument platform to compare the groups of allergic multimorbidities against a single entity. Results: There were 659 (51.8%) males and 614 (48.2%) females aged from 4 months to 74 years included in the analysis. The study participants who were diagnosed with allergic diseases had an average of 1.6 diagnoses. Overall, 46.5% (592 of 1273) of the patients had more than one allergic condition, and allergic rhinitis was the most common type of multimorbidity. Women were more likely to suffer from an allergic disease alone, whereas allergic multimorbidities were more likely to be diagnosed in men (p = 0.005). In addition, allergic multimorbidities were common in all age groups, with an incidence ranging from 37.1% to 57.4%, in which children and adolescents were more frequently diagnosed with allergic multimorbidities than adults (18–60 years old) (all p < 0.05). Allergic multimorbidity was observed throughout the year. A difference in the positive rate of allergens sensitization and total immunoglobulin E (tIgE) levels between different allergic multimorbidities was observed. Conclusions: Allergic multimorbidities were very commonly found in nearly half of all patients with allergies. The proportion of allergic multimorbidities varied with the type of disease, sex, age, and allergen distribution pattern. These findings may help clinicians to develop “One health” strategies for the clinical management of allergic diseases.
BackgroundBacterial infections have long been a significant cause of child mortality worldwide; however, models for predicting the risk of death in children with bacterial infections that combine predictive ability and interpretability are scarce. Here we try to explore a new method to complete the task. MethodsWe use Paediatric Intensive Care Database (PIC) to carry out the research. Data from hospitalized children with positive bacterial culture results was extracted and categorized into three groups: the positive culture group, the gram-positive group, and the gram-negative group, which were divided into 80% training and 20% test sets. Then we extracted the demographic information, vital signs, and laboratory data within 24 hours of admission of the datasets. We use the XGBoost algorithm to select the features and rank their importance,and the Logistic Regression (LR) algorithm for model development based on various numbers of feature. All the models were evaluated by Receiver Operating Characteristic curve (ROC), area under the ROC curve (ROC-AUC), Precision-Recall curve (PR), the area under the PR curve (PR-AUC), and compared with Paediatric Mortality Risk Score III (PRISM III), the paediatric logistic organ dysfunction score-2 (PELOD-2), and the paediatric multiple organ dysfunction scores (P-MODS).ResultIn total, 3695 children with bacterial infection were included, with an average age of 20.17 ± 36.74 months, average paediatric intensive care unit stay of 18.51 ± 28.84 days, and overall mortality rate of 8.39%. The following predictors appeared in the 64 most important predictors of the three datasets: average white blood cell count, maximum value of white blood cell technology, average value of anion gap, minimum value of anion gap, maximum value of type B natriuretic peptide, and maximum value of thrombocytocrit. Finally, we established a LR model for bacterial infected children with 4 features and a LR model for gram-negative bacterial infected children with 10 features, which achieve a 0.7244 and a 0.7848 ROC-AUC score respectively. The ROC-AUC scores of the two models were better than PRISM III、PELOD-2 and P-MODS. ConclusionThis study developed models to predict the risk of death in children with bacterial infections. The final models use fewer features and achieve better mortality prediction performance than traditional scoring models, and the models are easier for paediatricians to understand.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.