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Cohort-based development and validation of a nomogram for predicting cough variant asthma progression to typical
Kaiwen Zheng1, Qin Wang2, Yuling Zhao2
1School of Clinical Medicine, Shandong Second Medical University, Weifang, Shandong, China.
Insights
Predicting childhood cough variant asthma (CVA) progression to typical asthma (TA) is challenging. A new nomogram identifies key risk factors, offering a visual tool for early risk stratification in pediatric patients.
Area of Science:
- Pediatric Pulmonology
- Asthma Research
- Clinical Prediction Modeling
Background:
- Cough variant asthma (CVA) often precedes typical asthma (TA) in children, but predicting this progression is clinically difficult.
- Early identification of children at high risk for CVA to TA progression is crucial for timely intervention.
Purpose of the Study:
- To identify independent risk factors for the progression of pediatric CVA to TA.
- To develop and validate a visualized nomogram for early risk stratification of this disease progression.
Main Methods:
- A prospective cohort of 289 children with CVA was followed for 12 months.
- Risk factors were screened using logistic regression and LASSO regression.
- A nomogram was constructed and validated using ROC curves, calibration, and decision curve analysis.
Main Results:
- 14.5% of children progressed from CVA to TA during follow-up.
- Ten independent predictors were identified, including personal/family history of allergy/asthma, treatment compliance, pet ownership, FeNO, CaNO, X5%pred, R5%pred, FEF25%pred, and MMEF%pred.
- The nomogram demonstrated good predictive performance with AUCs of 0.863 (training) and 0.838 (validation).
Conclusions:
- A novel nomogram integrating ten risk factors provides a preliminary, visually intuitive tool for stratifying the risk of CVA to TA progression in children.
- This model aids in early identification and management of pediatric asthma.
- Further validation in diverse populations is warranted.
Abstract:
Cough variant asthma (CVA) is a common precursor of typical asthma (TA) in pediatric populations, whereas prediction of the progression from childhood CVA to TA remains difficult in clinical practice. This study aimed to screen independent risk factors for the progression of pediatric CVA to TA and construct a visualized nomogram prediction model for early risk stratification of this disease progression.
Methods:
A prospective cohort of 289 children diagnosed with CVA between June 2023 and June 2024 was followed for 12 months until June 2025. Patients were randomly divided into a training set (n = 203) and a validation set (n = 86) in a 7:3 ratio. Predictors were screened by univariate logistic regression and LASSO regression. A nomogram was constructed using the rms package in R and evaluated by ROC curves, 1000-iteration Bootstrap calibration, decision curve analysis (DCA), and Hosmer-Lemeshow tests.
Results:
During the 12-month follow-up, 42 children (14.5%) progressed to TA. Ten independent predictors were finally selected by LASSO regression and incorporated into the nomogram: personal history of allergy (OR = 3.62, 95% CI:1.78-7.36), family history of asthma (OR = 3.18, 95% CI:1.37-7.39), poor treatment compliance (OR = 2.94, 95% CI:1.32-6.55), history of pet keeping (OR = 2.41, 95% CI: 1.11-5.24), FeNO (OR = 1.09, 95% CI:1.05-1.14), CaNO (OR = 1.16, 95% CI: 1.06-1.27), X5%pred (OR = 1.04, 95% CI:1.02-1.07), R5%pred (OR = 1.03, 95% CI:1.01-1.06), FEF25%pred (OR = 0.96, 95% CI:0.94-0.98), and MMEF%pred (OR = 0.97, 95% CI: 0.95-0.99). The nomogram showed good discrimination with AUCs of 0.863 (95% CI: 0.782-0.944) in the training set and 0.838 (95% CI:0.742-0.933) in the validation set. Hosmer-Lemeshow tests indicated good fit (training:χ2=6.7277, p = 0.5663; validation:χ2=7.7376, p = 0.4595). DCA demonstrated favorable clinical net benefit across a wide range of threshold probabilities.
Conclusions:
This novel nomogram integrating ten key risk factors provides a preliminary and visually intuitive tool for risk stratification of CVA progression to TA in children.
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