Predicting Adverse Outcomes Using Machine Learning of COPD Patients in Hong Kong
ConditionCOPD Exacerbation
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age40+
SponsorChinese University of Hong Kong
About this trial
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 criteria
This trial does not accept healthy volunteersQualifiers
≥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
Disqualifiers
Admission diagnosis due to causes other than COPD
Trial population
Patients are discharged from 2016 -2022 from hospital for acute exacerbation of COPD
Trial design
Design model
Ecologic or community
Time perspective
Retrospective
Treatments tested in this trial
No intervention
Other interventionNo intervention
Treatment groups
No treatment groups listed
Trial outcomes
Primary outcomes
1
Early Readmission
Patients were readmitted to hospital with the primary diagnosis of AECOPD\* within 30 days since the discharge date of the index admission
Time frame
30 daysSecondary outcomes
1
Frequent Admitters
\- Patients with 3 or more admissions (Index Admission + 2 or more admissions) within 365 days from the admission date of the index admission
Time frame
365 days2
1-Year Mortality
\- Patients who died within 365 days from the discharge date of the index admission
Time frame
365 days3
Longstayers
\- Patients who had admissions(s) with a cumulative length of stay of \> 21 days within 1 year after the discharge date of the index admission
Time frame
365 daysSponsors and contacts
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Source ClinicalTrials.gov