Can a multi-omics and AI model predict ABPA relapse within 1 year in adults?
Official title: Multi-Omics Data-Derived Inflammatory Phenotype for ABPA Recurrence Risk Prediction: A Multicenter Study
Run by Qianfoshan Hospital · NCT07611838 · Data updated 2026-11-12
This study tests a machine learning model that uses multi-omics and clinical data to predict the 1-year relapse risk for adults with ABPA who are in a stable state.
Quick facts
| Study type | Observational |
|---|---|
| Ages | 18 Years to 80 Years |
| Sex | All |
| Enrollment | 300 (estimated) |
| Sponsor | Qianfoshan Hospital Academic / other |
| Locations | 1 site (Jinan, Shandong) |
| Status | Recruiting |
| Started | January 1, 2021 |
| Main results expected | December 31, 2028 |
| Study ends | December 31, 2028 |
| Registry record updated | June 1, 2026 |
| Trial ID | NCT07611838 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Adults aged 18–80 with a confirmed ABPA diagnosis by the 2024 ISHAM criteria.
✕Probably not a fit
Patients with incomplete data, severe comorbidities, or immunosuppressed status may not benefit from the model.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
Researchers will develop and externally validate a deep learning model that integrates radiomics, mycobiomics, inflammatory biomarkers, lung function, and routine clinical data to predict ABPA relapse over 12 months. The goal is to define inflammatory phenotypes and create a risk score to guide follow-up intensity and management. The study will use multicenter data to improve generalizability and may help tailor treatment decisions to individual risk profiles. Patients will be stratified into low- and high-risk groups to optimize monitoring and intervention strategies. No interventions are specified in this observational design.
Why it matters
Potential benefit: If successful, the model could improve relapse prediction and support personalized follow-up and management plans for ABPA patients.
How similar studies have gone: Previous work suggests multi-omics and machine learning can uncover informative patterns for relapse risk in complex respiratory diseases, supporting potential value here.
Who can join: the full rules
Show full inclusion / exclusion criteria
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.
Where it's running
-
Department of Respiratory, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, #16766, Jingshi Road, Jinan City, Shandong Province, China, Jinan, Shandong 250014
Jinan, Shandong, China Recruiting
Study contacts
- Study coordinator: Qian Qi
- Email: qiqianqlh@163.com
- Phone: +86 13706380314
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.