Multi-Omics Inflammatory Phenotype for ABPA Recurrence Risk Prediction
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.
| Condition(s) | Allergic Bronchopulmonary Aspergillosis (ABPA), Machine Learning, Multi-omics, Multicenter Study, Relapse |
|---|---|
| Status | Recruiting |
| Study type | Observational |
| 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. |
| Who can participate | 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. |
| Ages | 18 Years to 80 Years |
| Sex | All |
| Lead sponsor | Qianfoshan Hospital |
| Locations | Jinan, Shandong, China |
| Start date | 2021-01-01 |
| NCT ID | NCT07611838 |
| Official listing | https://clinicaltrials.gov/study/NCT07611838 |