Using machine learning to predict outcomes for COPD patients in Hong Kong

Is this trial a fit for you?

A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.

What this trial studies

This observational study aims to develop predictive models for patients diagnosed with chronic obstructive pulmonary disease (COPD) at the time of discharge from hospital admissions in Hong Kong. By utilizing machine learning techniques, the study will focus on predicting early admissions, frequent readmissions, mortality, and long hospital stays among COPD patients. The research will analyze data from COPD admissions between 2016 and 2022, leveraging spirometry results to validate diagnoses. The ultimate goal is to identify key factors that contribute to hospital readmissions and improve health outcomes for COPD patients.

Why it matters

Who can join: the full rules

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's running

• The Chinese University of Hong Kong
  Hong Kong, New Territories, Hong Kong Recruiting

Study contacts

• Study coordinator: Fanny Ko, MD
• Email: fannyko@cuhk.edu.hk
• Phone: 35053133

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How to take part

1. Review the eligibility criteria above with your treating physician.
2. Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
3. Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.