RecruitingCOPD Exacerbation
Predicting Adverse Outcomes Using Machine Learning of COPD Patients in Hong Kong
Eligible age
40+ yrs
Accepts
All genders
Locations
0 states
Healthy volunteers
No
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About this study
This study aims to develop predictive models for patients with a diagnosis of COPD at discharge of an index admission on these outcomes using machine learning: Primary outcome: Early admission Secondary outcomes: 1. Frequent readmission 2. Composite outcome (Early + Frequent readmissions) 3. Mortality 4. Longstayers
Sponsor: Chinese University of Hong Kong
You may qualify if…
- ✓ ≥40 years
- ✓ Patients are discharged from 2016 -2022
- ✓ Discharge Diagnosis: Using the Discharge Diagnosis ICD Codes found in the Primary Diagnosis to determine if a patient has COPD
- ✓ Validated against Spirometry results (for patient with a spirometry reading):
- ✓ Spirometry reading taken from anytime point before. Patient should have Post FEV1/FVC ratio of \< 0.7 in any one of the spirometry readings. If Post FEV1/FVC is not available, we will check if patients have a Pre FEV1/FVC value, and will also include patients with Pre FEV1/FVC ratio of \< 0.7 in any one of the spirometry readings.
You may not qualify if…
- ✕ Admission diagnosis due to causes other than COPD
Source: ClinicalTrials.gov · NCT05825014 · last updated 2026-03-18