Allergic Bronchopulmonary Aspergillosis (ABPA), Machine Learning, Multicenter Study, Multi-omics, Relapse
Conditions
Keywords
Allergic Bronchopulmonary Aspergillosis, Relapse, Machine Learning, Multicenter Study, Multi-omics
Brief summary
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.
Detailed description
This project aims to develop an inflammatory phenotype-based risk prediction model for recurrence of allergic bronchopulmonary aspergillosis (ABPA) to enable stratified patient management. The study integrates multidimensional data sources, including radiomics, mycobiomics, inflammatory biomarkers, pulmonary function parameters, and routine clinical records. Deep machine learning algorithms are employed to extract and select key features from these multi-omics and clinical datasets, define inflammatory phenotypes, and subsequently construct a recurrence risk prediction model. Based on the risk stratification derived from the model, low-risk individuals will receive regular follow-up, whereas high-risk individuals will undergo intensified intervention and management. This approach is expected to optimize individualized treatment strategies for ABPA patients, reduce recurrence rates, and improve clinical outcomes.
Interventions
None listed
Sponsors
Qianfoshan Hospital
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 80 Years
Healthy volunteers
No
Inclusion criteria
* Female and Male patients aged 18-80 years * diagnosis of Allergic Bronchopulmonary Aspergillosis ABPA accroding to the 2024 ISHAM Working Group Diagnostic Criteria
Exclusion criteria
* Patients with malignant tumors or severe organ dysfunction (e.g., cardiac, cerebral, renal, etc.) * Patients with severe comorbidities, including active pulmonary tuberculosis, lung cancer, chronic heart failure (NYHA class Ⅳ), chronic kidney disease (CKD stage 5), decompensated cirrhosis, etc. * Patients with immunosuppressive status, such as HIV infection, long-term use of oral corticosteroids or immunosuppressive agents. * Pregnant or lactating women. * Patients with missing key data or incomplete medical records.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Recurrent disease occurs in patients during the remission period. | 1 year | Observe whether disease recurrence occurs in ABPA patients who have reached stable phase after treatment. Stable Phase： 1. Symptomatic improvement by at least 50% (on a Likert or visual analog scale) after eight weeks; and, 2. Major radiological improvement (\>50% reduction in radiologic opacities) or decline in serum total IgE by at least 20% after eight weeks of treatment. Exacerbation/Recurrence: In a patient with diagnosed ABPA 1. Sustained (\>14 days) clinical worsening, or 2. Radiological worsening, and 3. Increase in serum total IgE by ≥50% from the last recorded IgE value during clinical stability, along with 4. Exclusion of other causes of worsening. |
Countries
China
Contacts
CONTACTQian Qi
STUDY_DIRECTORQian Qi
Shandong First Medical University
Outcome results
None listed