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Developing a prediction model for persistent airflow limitation in asthmatic children
Shiqiu Xiong1,2, Xinyu Jia1, Wei Chen1
1Department of Allergy, Center for Asthma Prevention and Lung Function Laboratory, Capital Center for Children's Health, Capital Medical University, Beijing, China.
Insights
Predicting persistent airflow limitation (PAL) in children with asthma is possible using clinical data. Machine learning models like random forest and logistic regression accurately identify children at risk for PAL.
Area of Science:
- Pediatric pulmonology
- Clinical informatics
- Biostatistics
Background:
- A subset of children with asthma develops persistent airflow limitation (PAL).
- Identifying children at risk for PAL is crucial for timely intervention.
- Predictive modeling can aid in early risk stratification.
Purpose of the Study:
- To develop and validate predictive models for identifying asthmatic children at risk of developing PAL.
- To compare the performance of classical and machine learning methods in predicting PAL.
Main Methods:
- Utilized demographic and clinical data from 1,671 asthmatic children for training and internal validation.
- Included 401 asthmatic children for temporal validation.
- Developed prediction models using logistic regression (LR), random forest (RF), and extreme gradient boost (XGBoost).
- Evaluated model performance using area under the curve (AUC), accuracy, sensitivity, specificity, calibration curves, Brier scores, and decision curve analysis.
Main Results:
- In internal validation, RF (AUC=0.857), LR (AUC=0.849), and XGBoost (AUC=0.835) showed strong predictive ability.
- Temporal validation confirmed comparable performance across models: RF (AUC=0.853), LR (AUC=0.836), and XGBoost (AUC=0.848).
- All models demonstrated good fitness and clinical utility.
Conclusions:
- Asthma-related persistent airflow limitation in children can be predicted with significant accuracy.
- Routinely collected clinical data are sufficient for developing effective predictive models.
- LR, RF, and XGBoost models offer comparable performance in identifying high-risk pediatric asthma patients.
Background:
A small proportion of asthmatic children will develop persistent airflow limitation (PAL). The purpose of this study was to develop predictive models using classical and machine learning methods to identify asthmatic children at risk of PAL.
Methods:
A total of 1,671 asthmatic children were enrolled between January 1, 2019, and December 31, 2020, to serve as training and internal validation sets. Temporal validation included 401 patients from January 1, 2021, to December 31, 2021. PAL was determined in the third year after enrollment, defined as a fixed forced expiratory volume in 1 second (FEV1)/forced vital capacity (FVC) ratio below 0.75. Predictors included demographic and clinical data. Machine learning algorithms, including random forest (RF) and extreme gradient boost (XGBoost), along with the classical logistic regression (LR) methods, were utilized to develop prediction models. Discrimination ability evaluation was conducted using the area under the curve (AUC), accuracy, sensitivity, and specificity, while fitness estimation utilized calibration curves and Brier scores. Additionally, decision curve analysis was employed for clinical value evaluation.
Results:
In the internal validation, the RF model achieved an AUC of 0.857 (95% CI: 0.791-0.924), followed by LR with an AUC of 0.849 (95% CI: 0.780-0.908) and XGBoost with an AUC of 0.835 (95% CI: 0.761-0.909). In the temporal validation, the three prediction models exhibited similar performance. Specifically, RF attained an AUC of 0.853 (95% CI: 0.771-0.935), LR achieved an AUC of 0.836 (95% CI: 0.742-0.938), and XGBoost reached an AUC of 0.848 (95% CI: 0.757-0.940). The calibration curve and low Brier score indicated good fitness of all prediction models, and decision curve analysis revealed desirable net benefits for all prediction models in both internal and temporal validation.
Conclusions:
PAL in asthmatic children can be predicted with clinically meaningful accuracy using routinely available clinical data, and three prediction models (LR, RF, and XGBoost) demonstrated comparable performance in identifying high-risk patients.
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