RecruitingNCT05825014

Predicting Adverse Outcomes Using Machine Learning of COPD Patients in Hong Kong

Sponsor

Chinese University of Hong Kong

Enrollment

100,000 participants

Start Date

Aug 29, 2023

Study Type

OBSERVATIONAL

Conditions

COPD Exacerbation

Summary

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

Eligibility

Min Age: 40 Years

Inclusion Criteria5

≥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.