ML-Based ABPA Recurrence Prediction and Clinical Utility

Plain-language summary

This multicenter bidirectional cohort study aims to develop and externally validate a machine learning model for predicting the risk of acute exacerbation within 1 year in patients with allergic bronchopulmonary aspergillosis (ABPA) during the stable phase, and further to evaluate the model's practical value in risk stratification and clinical decision-making. All patients diagnosed with ABPA according to the ISHAM 2024 criteria will be assigned to either the acute exacerbation group or the non-exacerbation group based on whether they experience an acute exacerbation within 1 year. Enrolled participants will be randomly divided into a training set and an internal validation set. During the feature selection phase, univariate analysis, collinearity diagnostics, feature importance ranking derived from nine machine learning algorithms, and expert consensus are comprehensively applied, ultimately leading to the development of 12 independent machine learning models. Model performance is assessed using the receiver operating characteristic (ROC) curve and its area under the curve (AUC), sensitivity, specificity, F1-score, calibration curve, and decision curve analysis. In addition, external validation further enhances the credibility of the model. To improve clinical interpretability, the SHAP method is employed to quantify the contribution of each feature, and an interactive nomogram is constructed to facilitate clinical application. All participants will be followed up for 12 months, during which regular clinical and laboratory evaluations will be performed.

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