Machine learning-based model for acute asthma exacerbation detection using routine blood parameters.

BACKGROUND

Acute asthma exacerbations (AAEs) are a leading cause of asthma-related morbidity and mortality, especially in resource-limited settings where pulmonary function tests are unavailable or when patients are unable to cooperate with testing. This study aimed to develop and validate a diagnostic model for AAE using routine blood parameters through machine learning techniques.

METHODS

We developed a machine learning-based diagnostic model using routine blood test parameters. Data from 23,013 asthma patients treated at the First Affiliated Hospital of Guangzhou Medical University were analyzed. Significant variables were identified through logistic regression, and 12 machine learning algorithms were used to construct diagnostic models, which were evaluated using Receiver Operating Characteristic (ROC) analysis, calibration, and Decision Curve Analysis (DCA).

RESULTS

The Generalized Linear Model Boosting combined with Random Forest (glmBoost + RF) algorithm using 14 variables achieved comparable performance (Area Under the Curve [AUC] = 0.981) to the more complex Least Absolute Shrinkage and Selection Operator combined with Random Forest (Lasso + RF) algorithm using 25 variables (AUC = 0.985). Both models demonstrated excellent calibration and consistent performance across different demographic subgroups. DCA confirmed superior clinical utility compared to conventional strategies.

CONCLUSIONS

This machine learning model provides an efficient and practical tool for detecting AAE using routine blood parameters, offering potential value in clinical practice, especially in resource-limited settings.

CLINICAL TRIAL NUMBER

Not applicable.