Allergic Bronchopulmonary Aspergillosis (ABPA)

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.

This multicenter bidirectional cohort study aims to investigate the clinical characteristics and prognostic outcomes of patients with allergic bronchopulmonary aspergillosis (ABPA) stratified by fungal microbiological status in sputum or bronchoalveolar lavage fluid (BALF). Patients diagnosed with ABPA according to the ISHAM 2024 criteria will be classified into fungal-positive and fungal-negative groups based on results from fungal culture, galactomannan (GM) testing, and metagenomic next-generation sequencing (mNGS) of sputum or BALF samples. The study will compare baseline clinical features, laboratory findings, radiological characteristics, treatment response, and long-term outcomes, including acute exacerbation rate and time to first exacerbation, between the two groups. All participants will be followed for up to 12 months with serial clinical and laboratory assessments.

To develop and externally validate a machine learning model for predicting the 1-year risk of relapse in patients with stable ABPA, and to further evaluate its value in risk stratification and clinical decision-making.

To evaluate the efficacy and safety of biological agents in patients with allergic bronchopulmonary aspergillosis.

The goal of this study is to learn how the body's immune system affects disease control in people with different airway inflammatory diseases.We want to understand: 1.Whether specific immune cell patterns in the blood are linked to how severe the disease is or how well it is controlled. Participants will: 1. Answer questions about their health and symptoms. 2. Give blood samples 3. Have lung function tests and other standard check-ups. 4. share sleep study results. We will compare people with airway diseases to healthy volunteers to see how their immune systems differ.

To evaluate the efficacy and safety of bronchoscopic airway clearance and amphotericin B spraying in the treatment of allergic bronchopulmonary aspergillosis

This study aims to reveal the immune and inflammatory regulatory molecular mechanisms related to the progression of allergic bronchopulmonary aspergillosis (ABPA), and to build a precision typing based on inflammation and identify the biomarkers of typing, and to explore the clinical outcomes of different inflammatory phenotypes.