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Development and internal validation of a multidimensional nomogram integrating PIV, LDH, and FeNO for predicting poor
Zhijian Zhan1, Tianfu Xu1, Saiping Huang1
1Department of Paediatrics, The Affiliated Hospital of Putian University, Putian City, Fujian, China.
Insights
A new model integrating inflammatory, metabolic, and airway biomarkers accurately identifies uncontrolled asthma in children. This multidimensional approach aids in personalized risk assessment and management for pediatric asthma patients.
Area of Science:
- Pediatric Pulmonology
- Biomarker Discovery
- Asthma Management
Background:
- Poor asthma control is prevalent in school-aged children, despite current treatment guidelines.
- Existing assessment tools for childhood asthma control have limitations.
- Objective, multidimensional biomarkers are needed for accurate asthma assessment.
Purpose of the Study:
- To develop and validate a risk stratification model for identifying uncontrolled asthma in children.
- The model integrates systemic inflammatory, metabolic, and airway-specific indicators.
- To improve personalized management strategies for pediatric asthma.
Main Methods:
- Retrospective study of 232 children (aged 6-14) with bronchial asthma.
- Utilized Childhood Asthma Control Test (C-ACT) for control assessment.
- Employed LASSO regression and multivariate logistic regression for variable selection and predictor identification.
Main Results:
- Identified six independent predictors: PIV, LDH, FeNO, Vitamin D, asthma duration, and FEV1% predicted.
- The integrated model showed superior predictive performance (AUC=0.886) compared to individual biomarkers.
- The nomogram demonstrated good calibration and clinical utility.
Conclusions:
- A multidimensional model combining PIV, LDH, FeNO, Vitamin D, asthma duration, and lung function accurately stratifies risk for poor asthma control in children.
- This model offers a clinically applicable tool for localized risk assessment.
- Facilitates personalized management approaches in pediatric asthma.
Background:
Poor asthma control remains common in school-aged children despite guideline-based treatment. Traditional assessment tools have limitations, highlighting the need for objective and multidimensional biomarkers. This study aimed to develop and internally validate a risk stratification model integrating systemic inflammatory, metabolic, and airway-specific indicators for identifying uncontrolled asthma.
Methods:
In this retrospective study, 232 children with bronchial asthma (aged 6-14 years) were enrolled. Asthma control was assessed using the Childhood Asthma Control Test (C-ACT). Clinical data, laboratory biomarkers, and pulmonary function parameters were collected. Least absolute shrinkage and selection operator (LASSO) regression was used for variable selection, followed by multivariate logistic regression to identify independent predictors. A nomogram was constructed, and internal model performance was evaluated using receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis (DCA).
Results:
Six variables were identified as independent predictors of poor asthma control: PIV, LDH, FeNO, Vitamin D, asthma duration, and FEV1% predicted. PIV (OR=1.008), LDH (OR=1.043), FeNO (OR=1.056), and asthma duration (OR=1.251) were risk factors, whereas Vitamin D (OR=0.891) and FEV1% predicted (OR=0.953) were protective. The combined model demonstrated superior predictive performance (AUC = 0.886, 95% CI: 0.835-0.937) compared with individual biomarkers and the baseline model. The nomogram showed good calibration and provided favorable clinical net benefit in DCA.
Conclusions:
A multidimensional model integrating PIV, LDH, FeNO, Vitamin D, asthma duration, and lung function provides accurate and clinically applicable risk stratification of poor asthma control in school-aged children. This approach may facilitate localized risk assessment and support personalized management in pediatric asthma.
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