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?Predicting Mortality in Intensive Care Unit Patients with Allergic Bronchopulmonary Aspergillosis (ABPA) Using an Interpretable Machine Learning Model: A Retrospective Cohort Study

?Predicting Mortality in Intensive Care Unit Patients with Allergic Bronchopulmonary Aspergillosis (ABPA) Using an Interpretable Machine Learning Model: A Retrospective Cohort Study

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2600118920
Enrollment
Unknown
Registered
2026-02-12
Start date
2025-04-10
Completion date
Unknown
Last updated
2026-02-16

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Allergic bronchopulmonary aspergillosis

Interventions

Training Set:None
Validation Set:None

Sponsors

Yuebei People’s Hospital
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 100 Years

Inclusion criteria

Inclusion criteria: Meeting the 2017 Chinese Society of Respiratory Disease, Asthma Group diagnostic criteria for Allergic Bronchopulmonary Aspergillosis (ABPA): 1. Associated disease: Asthma or other conditions such as bronchiectasis, COPD, or cystic fibrosis; 2. Essential criteria: Aspergillus fumigatus-specific IgE > 0.35 kUA/L or positive immediate skin test to Aspergillus fumigatus; serum total IgE level > 1000 IU/mL; 3. Additional criteria: Peripheral blood eosinophil count > 0.5 × 10?/L; radiographic findings consistent with ABPA (including but not limited to transient opacities such as pulmonary consolidation, nodules, "mucus plug" or "finger-in-glove" signs, or migratory shadows; or persistent changes such as bronchiectasis or pleuropulmonary fibrosis); positive serum Aspergillus-specific IgG antibodies or precipitins. Diagnosis of ABPA requires fulfillment of criterion 1, criterion 2, and at least two of the criteria in group 3. If all other criteria are met, ABPA may still be diagnosed even if serum total IgE is 18 years.

Exclusion criteria

Exclusion criteria: 1. Presence of malignancy or severe organ dysfunction (e.g., cardiac, cerebral, or renal failure). 2. Pregnant or lactating women. 3. Incomplete medical records or insufficient diagnostic data.

Design outcomes

Primary

MeasureTime frame
Hospital mortality rate;Importance of predictor variables in XGBoost model predictions;

Countries

China

Contacts

Public ContactZhang Jing

Yuebei People’s Hospital

zhangjing930730@163.com+86 15702411816

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 19, 2026