COPD ExacerbationNCT05825014
Predicting Adverse Outcomes Using Machine Learning of COPD Patients in Hong Kong
An observational study of COPD Exacerbation, run by Chinese University of Hong Kong at 1 site in Hong Kong.
Trial 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 Read more about this study
- Who is it for?
- Ages 40 years and older
- Conditions the study is for
- COPD Exacerbation
- How many people
- 100,000 participants
- Where
- 1 siteHong Kong
- Study dates
- 29 Aug 2023to 30 Apr 2028
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 40 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients are discharged from 2016 -2022 from hospital for acute exacerbation of COPD
Who may be able to join
- ≥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.
Who cannot join
- Admission diagnosis due to causes other than COPD
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Hong Kong1 site, 1 recruiting
- Recruiting
The Chinese University of Hong Kong
Hong Kong, New Territories
David S Hui, MD, 35053133, dschui@cuhk.edu.hk
fanny WS Ko, MD, 35053133, fannyko@cuhk.edu.hk
About this study
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:
- Frequent readmission
- Composite outcome (Early + Frequent readmissions)
- Mortality
- Longstayers
Read the detailed description
Chronic obstructive pulmonary disease (COPD) is a common, preventable, and treatable disease that is characterised by persistent respiratory symptoms and airflow limitation that is due to airway and/or alveolar abnormalities usually caused by significant exposure to noxious particles or gases and influenced by host factors including abnormal lung development. It was estimated 3.2 million people died from COPD worldwide in 2015 and there was an increase of 11.6% compared with 1990. COPD is the third leading cause of death globally in 2019.
In Hong Kong (HK), the prevalence rates of COPD in the elderly population aged ≥60years were 25.9% and 12.4% based on the spirometric definition of forced expiratory volume in 1 s (FEV1)/forced vital capacity (FVC) ratio <70% and the lower limit of normal of the FEV1/FVC respectively.4 From our recent study on COPD hospital admissions, there are a total of 67,628 COPD admissions Jan 2017 Week 1 to Jan 2020 Week 3 (before the COVID pandemic) and 11,065 admissions from Jan 2020 Week 4 to Dec 2020 Week 4 (during the COVID pandemic). 5 The burden of COPD hospitalizations is significant and it is important to understand the driver of these admissions for developing suitable strategies to solve the problem and improve the health outcomes of patients suffering from COPD.
Early readmission and frequent admissions resulting from COPD are commonly studied hospital outcomes because of the high financial burden to both individual and state and the high usage of public healthcare resources. With the advent of Artificial Intelligence (AI) and Machine Learning (ML), there has been considerable interest on its application to medicine. Recent metaanalysis showed compatibility of these models in predicting COPD outcomes.7 However, few studies have managed to show that AI/ML are superior to traditional statistical modeling methods, AI/ML are interpretable and can be clinically correlated, and AI/ML can have direct clinical application.
This study aims to develop predictive models for patients with a diagnosis of COPD at discharge of an index admission on these outcomes:
Primary outcome: Early admission
Secondary outcomes:
- Frequent readmission
- Composite outcome (Early + Frequent readmissions)
- Mortality
- Longstayers
The viability and purported superiority of Machine Learning (ML) models as alternatives to traditional statistical learning methods will be assessed. Apart from that top predictors of each outcome of interest would be identified for suggestions of possible interventions that will improve outcomes (i.e. reduce early admission, frequent admission and mortality rates). Clinical scores for deployment in clinical setting will also be developed.
- Conditions
- COPD Exacerbation
- Being tested
- No intervention
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
No interventionOther
No intervention
What is being measured
The primary outcome is the main question the study is designed to answer.
Early Readmission
Patients were readmitted to hospital with the primary diagnosis of AECOPD* within 30 days since the discharge date of the index admission
Measured: 30 days
Secondary outcomes (3)
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
Measured: 365 days
1-Year Mortality
- Patients who died within 365 days from the discharge date of the index admission
Measured: 365 days
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
Measured: 365 days
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 Apr 2023
- Study start29 Aug 2023
- Primary completion30 Apr 2027 (estimated)
- Study completion30 Apr 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for COPD ExacerbationStudies of the same condition, open and under way.
- More trials run by Chinese University of Hong KongThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Ecologic or community
- Time perspective
- Retrospective
- Enrolment
- 100,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Chinese University of Hong Kong
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Fanny W.S. Ko, Chinese University of Hong Kong
- Investigators
- David Hui, MD, Study director, Chinese University of Hong Kong
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05825014
- Sponsor's protocol ID
- CRE Ref_ No_ 2022_679
- Status last verified
- Mar 2026
- First submitted
- 11 Apr 2023
- Last updated
- 18 Mar 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided: With appropriate study proposal for collaboration, we can consider sharing data (with no personal identifiers).
Study at a glance
From the registry record, last updated by the sponsor on 18 Mar 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Predicting Adverse Outcomes Using Machine Learning of COPD Patients in Hong Kong
- ClinicalTrials.gov ID
- NCT05825014
- Acronym / sponsor ID
- CRE Ref_ No_ 2022_679
- Conditions
- COPD Exacerbation
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 100,000 participants (estimated)
- Lead sponsor
- Chinese University of Hong KongUniversity, hospital or other
- Study start
- 29 Aug 2023 (actual)
- Primary completion
- 30 Apr 2027 (estimated)
- Study completion
- 30 Apr 2028 (estimated)
- First posted
- 24 Apr 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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:
- Frequent readmission
- Composite outcome (Early + Frequent readmissions)
- Mortality
- Longstayers
Detailed description
Chronic obstructive pulmonary disease (COPD) is a common, preventable, and treatable disease that is characterised by persistent respiratory symptoms and airflow limitation that is due to airway and/or alveolar abnormalities usually caused by significant exposure to noxious particles or gases and influenced by host factors including abnormal lung development. It was estimated 3.2 million people died from COPD worldwide in 2015 and there was an increase of 11.6% compared with 1990. COPD is the third leading cause of death globally in 2019.
In Hong Kong (HK), the prevalence rates of COPD in the elderly population aged ≥60years were 25.9% and 12.4% based on the spirometric definition of forced expiratory volume in 1 s (FEV1)/forced vital capacity (FVC) ratio <70% and the lower limit of normal of the FEV1/FVC respectively.4 From our recent study on COPD hospital admissions, there are a total of 67,628 COPD admissions Jan 2017 Week 1 to Jan 2020 Week 3 (before the COVID pandemic) and 11,065 admissions from Jan 2020 Week 4 to Dec 2020 Week 4 (during the COVID pandemic). 5 The burden of COPD hospitalizations is significant and it is important to understand the driver of these admissions for developing suitable strategies to solve the problem and improve the health outcomes of patients suffering from COPD.
Early readmission and frequent admissions resulting from COPD are commonly studied hospital outcomes because of the high financial burden to both individual and state and the high usage of public healthcare resources. With the advent of Artificial Intelligence (AI) and Machine Learning (ML), there has been considerable interest on its application to medicine. Recent metaanalysis showed compatibility of these models in predicting COPD outcomes.7 However, few studies have managed to show that AI/ML are superior to traditional statistical modeling methods, AI/ML are interpretable and can be clinically correlated, and AI/ML can have direct clinical application.
This study aims to develop predictive models for patients with a diagnosis of COPD at discharge of an index admission on these outcomes:
Primary outcome: Early admission
Secondary outcomes:
- Frequent readmission
- Composite outcome (Early + Frequent readmissions)
- Mortality
- Longstayers
The viability and purported superiority of Machine Learning (ML) models as alternatives to traditional statistical learning methods will be assessed. Apart from that top predictors of each outcome of interest would be identified for suggestions of possible interventions that will improve outcomes (i.e. reduce early admission, frequent admission and mortality rates). Clinical scores for deployment in clinical setting will also be developed.
Study design
- Observational model
- Ecologic or community
- Time perspective
- Retrospective
Who may qualify
- Ages
- 40 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients are discharged from 2016 -2022 from hospital for acute exacerbation of COPD
Key inclusion criteria (5 listed)
- ≥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.
Key exclusion criteria (1 listed)
- Admission diagnosis due to causes other than COPD
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| No interventionNo intervention | Other | – |
Primary outcome
Early Readmission
Time frame: 30 days
Patients were readmitted to hospital with the primary diagnosis of AECOPD* within 30 days since the discharge date of the index admission
3 secondary outcomes
Frequent Admitters
Time frame: 365 days
1-Year Mortality
Time frame: 365 days
Longstayers
Time frame: 365 days
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 18 Mar 2026.
- Individual participant dataSharing planned: Undecided. With appropriate study proposal for collaboration, we can consider sharing data (with no personal identifiers).
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
David Hui, MD study director
Chinese University of Hong Kong
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of COPD Exacerbation, run by Chinese University of Hong Kong at 1 site in Hong Kong.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 18 Mar 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Hong Kong. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Fanny Ko, MD (35053133, fannyko@cuhk.edu.hk), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Early Readmission (time frame: 30 days). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 18 Mar 2026; AzTrials last refreshed its copy on 30 Oct 2026.