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

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

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

Conditions2

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