ML-Based ABPA Recurrence Prediction and Clinical Utility
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- This is an observational study: the protocol does not assign a study treatment.
- Who it may be relevant to
- Registry conditions: ABPA, Allergic Bronchopulmonary Aspergillosis, Allergic Bronchopulmonary Aspergillosis (ABPA), Acute Exacerbation. Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- China
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
Unsure about the terms? Read our patient guide →
Official title
Machine Learning-Based Prediction of Recurrence Risk in Allergic Bronchopulmonary Aspergillosis and Its Clinical Decision-Making Value: A Multicenter Study With External Validation
Overview
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.
Primary outcome measures
- The occurrence of ABPA exacerbation within one year of enrollment. [Time frame: 1 year]
Secondary outcome measures (12)
- Time to first acute exacerbation [Time frame: 1 year]
- Total serum IgE [Time frame: 1 year]
- FEV1 (% predicted) [Time frame: 1 year]
- Changes in chest CT features including scores for bronchiectasis severity [Time frame: 1 year]
- Aspergillus-specific IgE [Time frame: 1 year]
- Aspergillus-specific IgG [Time frame: 1 year]
- forced vital capacity (FVC) [Time frame: 1 year]
- FEV1/FVC ratio [Time frame: 1 year]
- diffusing capacity for carbon monoxide (DLCO) [Time frame: 1 year]
- extent of bronchiectasis of chest CT [Time frame: 1 year]
- mucus plugging on chest CT [Time frame: 1 year]
- high-attenuation (HAM) on chest CT [Time frame: 1 year]
Eligibility criteria
Inclusion criteria
- Aged between 18 and 80 years old.
- Consistent with the diagnostic consensus criteria for ABPA proposed by the ISHAM-ABPA Working Group.
- Patients in stable phase of ABPA: newly diagnosed treatment-naive patients or those with prior ABPA exacerbation who achieved at least 50% improvement in symptoms assessed by Likert scale or visual analogue scale (VAS) following initial therapy, accompanied by marked radiological improvement (≥50% reduction in pulmonary opacities) or a minimum 20% decline in serum total IgE level.
Exclusion criteria
- Concurrent malignant tumors or severe organ dysfunction involving the heart, brain, kidney and other vital organs.
- Complicated with severe underlying diseases, including active pulmonary tuberculosis, lung cancer, chronic heart failure (NYHA class Ⅳ), chronic kidney disease stage 5 (CKD 5), decompensated liver cirrhosis, etc.
- Immunocompromised status, such as human immunodeficiency virus (HIV) infection, long-term oral administration of glucocorticoids or immunosuppressive agents.
- Pregnant or breastfeeding women.
- Patients with missing core clinical data or incomplete medical records.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Cohort
Study locations
China · 1 center
- Department of Respiratory, The First Affiliated Hospital of Shandong First Medical Univers — Jinan
Identifiers
NCT: NCT07714863 · ABPA-006