⚡ Quick Summary
A recent study developed a diagnostic prediction model for childhood allergic asthma using specific IgE tests for airborne allergens. The model demonstrated a strong predictive performance with an AUC of 0.853 in the training set and 0.838 in the validation set, providing a valuable tool for early asthma diagnosis in children. 🌟
🔍 Key Details
- 📊 Dataset: 4,338 pediatric cases from Beijing Children’s Hospital
- 🧩 Features used: Age, gender, cough and wheezing symptoms, sIgE concentrations of 15 airborne allergens
- ⚙️ Methodology: LASSO regression and multivariate logistic regression
- 🏆 Performance: AUC of 0.853 (training set), 0.838 (validation set)
🔑 Key Takeaways
- 👶 Age distribution: Majority of cases were aged 3 to <6 years (36.49%) and 6 to <12 years (46.98%).
- 👦 Gender: Males represented 65.17% of the cases.
- 💨 Wheezing symptoms: 58.53% of children exhibited wheezing.
- 🔍 Significant predictors: Cough and wheezing symptoms, sIgE levels for specific allergens (d1, e5, m3, w6).
- 📈 Model applicability: Best predictive applicability observed at a probability threshold of 8%-92%.
- 🧪 Clinical relevance: The model can aid in the early identification and management of childhood asthma.
- 🤖 AI integration: Highlights the potential for AI in analyzing big data for asthma management.
📚 Background
Childhood asthma is a prevalent condition that can significantly impact a child’s quality of life. Traditional diagnostic methods often rely on clinical symptoms and history, which can be subjective. The integration of specific IgE testing for airborne allergens offers a promising avenue for enhancing diagnostic accuracy and early intervention strategies.
🗒️ Study
Conducted at the Allergy Department of Beijing Children’s Hospital from January to December 2023, this case-control study analyzed data from 4,338 children who underwent specific IgE testing. The researchers aimed to construct a robust diagnostic prediction model by examining various clinical and laboratory parameters.
📈 Results
The study identified six key predictor variables through LASSO regression, with cough and wheezing symptoms showing the highest odds ratio (OR=24.37). The model’s performance was validated with an AUC of 0.853 for the training set and 0.838 for the validation set, indicating excellent discrimination ability. The calibration curves further confirmed the model’s reliability, making it a valuable tool for clinicians.
🌍 Impact and Implications
This diagnostic prediction model represents a significant advancement in the field of pediatric asthma management. By combining clinical data with specific IgE testing, healthcare providers can achieve more accurate diagnoses, leading to timely interventions. The potential integration of artificial intelligence in analyzing such data could further enhance the management and prevention of childhood asthma, ultimately improving patient outcomes. 🌈
🔮 Conclusion
The construction of this childhood asthma diagnostic prediction model underscores the importance of utilizing both clinical and laboratory data for improved diagnostic accuracy. With its strong predictive performance, this model can serve as a practical tool for clinicians, paving the way for better asthma management strategies in children. Continued research and development in this area are essential for harnessing the full potential of AI in healthcare. 🚀
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[Construction of a diagnostic prediction model for childhood allergic asthma based on the detection results of specific IgE for airborne allergens].
Abstract
Objective: To construct a diagnostic prediction model for childhood asthma and conduct a preliminary evaluation based on the test results of specific IgE (sIgE) for airborne allergens and in combination with clinical data. Methods: This study is a case-control study. A total of 4 338 cases that completed the sIgE test for airborne allergens in the Allergy Department of Beijing Children’s Hospital Affiliated to Capital Medical University from January to December 2023 were selected as the research subjects. They were divided into the asthma group and the non-asthma group based on the diagnostic information. Age, gender, cough and wheezing symptoms, and the classification results of sIgE concentrations of 15 airborne allergens were collected as the predictor variables of the asthma diagnostic prediction model. Differential analysis and LASSO regression were employed for the screening of predictor variables. The multivariate logistic regression method was applied to construct the nomogram prediction model. The data set was randomly split at a ratio of 7∶3 into a training set (3 036 cases) for constructing the prediction model and a validation set (1 302 cases) for testing the predictive efficacy of the model. The area under the receiver operating characteristic (ROC) curve (AUC), the Hosmer-Lemeshow calibration curve were utilized to assess the discrimination and goodness of fit of the model, and the clinical decision curve (DCA) was adopted to evaluate the clinical application value of the model. Results: Among 4 338 pediatric cases, children aged 0 to <3 years accounted for 10.17% (441 cases), those aged 3 to <6 years accounted for 36.49% (1 583 cases), those aged 6 to <12 years accounted for 46.98% (2 038 cases), and those aged 12 to 18 years accounted for 6.36% (276 cases). Males constituted 65.17% (2 827 cases), and females 34.83% (1 511 cases). The proportion of children without wheezing symptoms was 41.47% (1 799 cases), while those with wheezing symptoms was 58.53% (2 539 cases). The asthma group accounted for 41.77% (1 812 cases), and the non-asthma group for 58.23% (2 526 cases). Statistically significant differences were observed between the asthma group and the non-asthma group in 18 predictive variables including age, gender, wheezing symptoms, d1, d2, e1, e5, g2, g6, m6, t11, t3, t6, w1, w22, w6, wx5, and m3 (P<0.05). LASSO regression analysis identified six predictor variables: age (calculated in months), cough and wheezing symptoms, and sIgE of four airborne allergens, namely, Dermatophagoides pteronyssinus (d1), Canis familiaris dander (e5), Aspergillus fumigatus (m3), and Artemisia vulgaris pollen (w6).Multifactorial regression analysis revealed that the contribution degrees of the above-mentioned predictor variables to the asthma diagnosis prediction model were ranked as follows: cough and wheezing symptoms (OR=24.37, P<0.001), m3 (OR=1.34, P<0.001), d1 (OR=1.22, P<0.001), e5 (OR=1.12, P=0.028), w6 (OR=1.11, P<0.001), and age (OR=1.01, P<0.001).The AUCs of the nomogram prediction model for the training set and the validation set were 0.853 (95%CI: 0.840-0.866) and 0.838 (95%CI: 0.817-0.860), respectively. The Hosmer-Lemeshow calibration curve indicated a good fit (P=0.215 for the training set; P=0.352 for the validation set). The DCA of the validation set demonstrated that when the probability threshold for predicting the occurrence of childhood asthma was 8%-92%, the model had the best applicability. Conclusion: By combining age, cough and wheezing symptoms, and sIgE of the four airborne allergens (d1, e5, m3, and w6) selected from 15 airborne allergens, a childhood asthma diagnosis prediction model with good predictive performance and clinical practicability was constructed. It can serve as a simple and convenient tool for accurately identifying asthma and provides a practical basis for the application of artificial intelligence big data analysis models in the prevention, treatment, and management of childhood asthma.
Author: [‘Yue CY’, ‘Xiang L’, ‘Hou XL’, ‘Huang HJ’]
Journal: Zhonghua Yu Fang Yi Xue Za Zhi
Citation: Yue CY, et al. [Construction of a diagnostic prediction model for childhood allergic asthma based on the detection results of specific IgE for airborne allergens]. [Construction of a diagnostic prediction model for childhood allergic asthma based on the detection results of specific IgE for airborne allergens]. 2025; 59:658-666. doi: 10.3760/cma.j.cn112150-20250210-00098