Predicting Adverse Outcomes Using Machine Learning of COPD Patients in Hong Kong
Recruiting · NCT05825014 · Observational (researchers observe without assigning treatment) · Lead sponsor: Chinese University of Hong Kong
View the official record on ClinicalTrials.gov →Interventions studied
No intervention
What this trial is about
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
Who can take part
Age range
40 Years and older
Sex
All (male and female)
Healthy volunteers
No - a diagnosis or condition is required
Phase
Not specified
Study type
Observational (researchers observe without assigning treatment)
Inclusion criteria
- ≥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.
Exclusion criteria
- Admission diagnosis due to causes other than COPD
Where it is running
1 location listed.
- The Chinese University of Hong Kong - Hong Kong, New Territories, Hong Kong