CopdENBEDNCT05825261
Exploring Novel Biomarkers for Emphysema Detection
An observational study of Copd and Emphysema, run by Maastricht University at 2 sites in Netherlands.
Trial summary
The goal of this clinical trial is to evaluate whether voice or capnometry, alone or in combination with other (non invasive) biomarkers can be used to detect emphysema on chest CT-scan in people with chronic obstructive pulmonary disease (COPD). Read more about this study
- Who is it for?
- Ages 18 years and older
- What is being tested?
- voice sampling and capnometry
- How many people
- 200 participants
- Where
- 2 sitesNetherlands
- Study dates
- 7 Sep 2023to May 2026
- 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 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: The source population (primary dataset) consists of COPD patients in whom a chest CT was performed within 12 months before inclusion into the study.
Who may be able to join
- Adults aged over 18 years
- current respiratory smptoms (any dyspnea, cough or sputum)
- spirometry confirmed diagnosis of a non-fully reversible airflow obstruction, defined as a post bronchodilator Forced Expiratory Volume at one second/Forced Vital Capacity (FEV1/FVC ratio) < 0.7 and/or emphysemateus abnormalities on CT imaging.
- presence of risk factors or causes associated with COPD
- chest CT scan performed in the past 12 months prior to inclusion to the study
- able to understand, read and write Dutch language
Who cannot join
- acute exacerbation of COPD within 8 weeks of start of the study
- comorbidities affecting speech or breathing coordination (neuromuscular disease, CVA< BMI > 40)
- comorbidities affecting speech characteristics of dyspnea (severe heart failure, interstitial lung disease)
- comorbidities affecting respiratory system including but not exclusive to asthma or cystic fibrosis
- comorbidities that significantly interfere with interpretation of speech (audio signals), such as Parkinson's disease, bulbar palsy, or vocal cord paralysis.
- Medical history of lobectomy or endoscopic lung volume reduction (ELVR)
Show all 9Show fewer
- inability to carry out a capnography recording.
- investigator's uncertainty about the willingness or ability of the patients to comply with the protocol requirements.
- participation in another study involving investigational products. Participation in observational studies is allowed.
Trial locations and contacts
2 sites in 1 country. Contact the study team or a site near you to ask about taking part.
Netherlands2 sites, 2 recruiting
- Recruiting
Dept of Respiratory Medicine, Maastricht University Medical Centre
Maastricht, Limburg, 6202 AZ
Sami Simons, MD PhD, +31433876543, sami.simons@mumc.nl
- Not yet recruiting
Laurentius Ziekenhuis
Roermond, Limburg, 6043 CV
Ragnar Lunde, MD, +31-(0)475 382 222, r.lunde@lzr.nl
+31-(0)475 382 222
About this study
Official title: Exploring Novel Biomarkers for Emphysema Detection: the ENBED Study
The goal of this clinical trial is to evaluate whether voice or capnometry, alone or in combination with other (non invasive) biomarkers can be used to detect emphysema on chest CT-scan in people with chronic obstructive pulmonary disease (COPD). The main question it aims to answer is:
- Can a machine-learning based algorithm be developed that can classify the extent of emphysema on chest CT scan from patients with COPD, based on voice and/or capnometry.
Participants will:
- perform different voice-related tasks
- perform capnometry twice (before/after exercise)
- perform a light exercise task between tasks ( 5-sit-to-stand test)
- undergo one venipuncture
Read the detailed description
This is a cross sectional, single center study. At the clinic, patients with COPD will be invited to perform several voice related tasks (paced reading, sustained vowels, cough, quiet breathing) and will be instructed to perform capnometry measurements. These measurements will be performed before and after a light exercise task (5-STS: 5-sit-to-stand test).
Clinical characterisation of patients including pulmonary function tests (spirometry, body plethysmography, diffusion capacity) and CT scans have been performed in all patients as a part of routine workup in the COPD care pathway. Emphysema will be quantified as low attenuation areas with a density below -950 Hounsfield units (HU) using Syngovia (Siemens, Erlangen, Germany).
The primary outcome will fit a simple machine learning classification model (e.g. using logistic regression, support vector machines, random forests and/or decision tree) to classify logistic regression model for the outcome of emphysema (>25% vs ≤ 25%) from speech features and capnometry. with explanatory variables of speech features. Similar classification methods with incremental models using capnography features will be explored. Prior to carrying out the above analyses, data has to be pre-processed, including merging data, quality control, handling of missing data and feature extraction.
- Being tested
- voice sampling, capnometry
- Keywords
- vocal biomarkers, speech, emphysema, capnometry, chest CT
- Medical subject headings
- Pulmonary Disease, Chronic Obstructive, Emphysema, Speech
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
voice samplingOther
Patients with COPD will perform several voice-related tasks and capnometry at rest. Thereafter a 5-STS will follow and the voice-related task/capnometry will be repeated
capnometryOther
Patients with COPD will perform several voice-related tasks and capnometry at rest. Thereafter a 5-STS will follow and the voice-related task/capnometry will be repeated
| Study group | Role | What they receive |
|---|---|---|
| COPD and/or emphysema | COPD is defined according to COPD Gold 2023 guidelines. Emphysema defined acording to Fleischner criteria (2024) Receives: Other: voice sampling; Other: capnometry |
What is being measured
The primary outcome is the main question the study is designed to answer.
percentage of participants having moderate to severe emphysema on a chest CT (defined as > 25%)
A baseline chest CT scan from each participant will be analysed using a lung parenchyma analysis software with automated 3-D quantification of emphysema. Emphysema will be defined as low attenuation areas with a density below -950 Hounsfield units. Patients will be either classified as having low emphysema (less or equal to 25% of emphysema on chest CT scan) or moderate to high emphysema (more than 25% of emphysema on chest CT scan)
Measured: baseline
number of (non-linguistic) inhalations per syllable from sustained vowel
Participants will perform a sustained vowel (saying 'a') at rest and after light exercise from which several measurements can be obtained: Syllables per breath group, speaking rate, articulation rate, mean frequency, mean intensity, pitch variability, mean center of gravity, inhalations, non-linguistic inhalations, ratio voice/silence intervals. Based on previous research (Merkus J, 2020) HNR, shimer, vowel duration en number of (non-linguistic) inhalations per syllable were putative vocal biomarkers in COPD.
First key determinant therefore is the number of (non-linguistic) inhalations per syllable during sustained vowel of each participant. This will be used as input for machine learning classification models (e.g. using logistic regression, support vector machines, random forests and/or decision tree) to classify emphysema on CT scan (>25% vs ≤ 25%)
Measured: baseline
harmonics-to-noise-ratio from sustained vowel
Participants will perform a sustained vowel (saying 'a') at rest and after light exercise from which several measurements can be obtained: Syllables per breath group, speaking rate, articulation rate, mean frequency, mean intensity, pitch variability, mean center of gravity, inhalations, non-linguistic inhalations, ratio voice/silence intervals. Based on previous research (Merkus J, 2020) HNR, shimer, vowel duration en number of (non-linguistic) inhalations per syllable were putative vocal biomarkers in COPD.
Second key determinant therefore is the harmonics-to-noise ratio during sustained vowel. This will be used as input for machine learning classification models (e.g. using logistic regression, support vector machines, random forests and/or decision tree) to classify emphysema on CT scan (>25% vs ≤ 25%)
Measured: baseline
vowel duration from sustained vowel
Participants will perform a sustained vowel (saying 'a') at rest and after light exercise from which several measurements can be obtained: Syllables per breath group, speaking rate, articulation rate, mean frequency, mean intensity, pitch variability, mean center of gravity, inhalations, non-linguistic inhalations, ratio voice/silence intervals. Based on previous research (Merkus J, 2020) HNR, shimer, vowel duration en number of (non-linguistic) inhalations per syllable were putative vocal biomarkers in COPD.
Third key determinant therefore is the vowel duration (in seconds) during sustained vowel. This will be used as input for machine learning classification models (e.g. using logistic regression, support vector machines, random forests and/or decision tree) to classify emphysema on CT scan (>25% vs ≤ 25%)
Measured: baseline
shimmer from sustained vowel
Participants will perform a sustained vowel (saying 'a') at rest and after light exercise from which several measurements can be obtained: Syllables per breath group, speaking rate, articulation rate, mean frequency, mean intensity, pitch variability, mean center of gravity, inhalations, non-linguistic inhalations, ratio voice/silence intervals. Based on previous research (Merkus J, 2020) HNR, shimer, vowel duration en number of (non-linguistic) inhalations per syllable were putative vocal biomarkers in COPD.
Fourth key determinant therefore is shimmer (in Hz) during sustained vowel. This will be used as input for machine learning classification models (e.g. using logistic regression, support vector machines, random forests and/or decision tree) to classify emphysema on CT scan (>25% vs ≤ 25%)
Measured: baseline
end-tidal CO2 from capnography (ETCO2)
Participation perform a quiet breathing (tidal volume) at rest and after light exercise to measure CO2 during exhalation (capnogram) from which several parameters can be measured, of which end-tidal CO2 (etCO2), phase 2 slope and phase 3 slope are most distinctive for COPD phenotyping (Pereira 2016).
First key determinant from capnography is therefore end-tidal CO2 (in mm Hg). This will be used as input for machine learning classification models (e.g. using logistic regression, support vector machines, random forests and/or decision tree) to classify emphysema on CT scan (>25% vs ≤ 25%)
Measured: baseline
phase-2 slope from capnography (slp2)
Participation perform a quiet breathing (tidal volume) at rest and after light exercise to measure CO2 during exhalation (capnogram) from which several (more than 80) parameters can be measured, of which end-tidal CO2 (etCO2), phase 2 slope and phase 3 slope are most distinctive for COPD phenotyping (Pereira 2016).
Second key determinant from capnography is therefore phase-2 slope (in mm Hg/L). This will be used as input for machine learning classification models (e.g. using logistic regression, support vector machines, random forests and/or decision tree) to classify emphysema on CT scan (>25% vs ≤ 25%)
Measured: baseline
phase-2 slope from capnography (slp3)
Participation perform a quiet breathing (tidal volume) at rest and after light exercise to measure CO2 during exhalation (capnogram) from which several parameters can be measured, of which end-tidal CO2, phase 2 slope and phase 3 slope are most distinctive for COPD phenotyping (Pereira 2016).
Third key determinant from capnography is therefore phase 3 slope (in mm Hg/L). This will be used as input for machine learning classification models (e.g. using logistic regression, support vector machines, random forests and/or decision tree) to classify emphysema on CT scan (>25% vs ≤ 25%)
Measured: baseline
Secondary outcomes (4)
serum sRAGE
Serum soluble receptor for advanced glycation end-products (sRAGE) from peripheral blood will be determined in each participant. Serum sRAGE is considered a blood biomarker for emphysema (Klont 2022). Serum sRAGE levels (in ng/mL) from each participant will be used as input variable for machine learning classification models (e.g. using logistic regression, support vector machines, random forests and/or decision tree) to classify emphysema on CT scan (>25% vs ≤ 25%)
Measured: baseline
ratio of residual volume to total lung capacity (RV/TLC) on body plethysmography
Emphysema can be measured using body plethysmography. Several variables can be measured with body plethysmography: total lung capacity (TLC), inspiratory capacity (IC), functional residual capacity (FRC), residual volume (RV), ratio of IC/TLC, ratio FRC/TLC and ratio RV/TLC. The ratio of RV/TLC might be the most sensitive measure for airtrapping as the first sign of emphysema and is therefore chosen as the key outcome measure of body plethysmograpy. RV/TLC ratio (expressed as Z-score) from each participant will be used as input variable for machine learning classification models (e.g. using logistic regression, support vector machines, random forests and/or decision tree) to classify emphysema on CT scan (>25% vs ≤ 25%)
Measured: baseline
diffusion capacity of the lungs for carbon monoxide
Diffusion capacity of the lungs for carbon monoxide (DLCO) is a measure of the lungs ability to transfer gas from air to the blood stream and a decrease in DLCO is associated with the extent of emphysema in chest CT scans. DLCO (expressed a Z-score) in each participant will be measured and used as input variable for machine learning classification models (e.g. using logistic regression, support vector machines, random forests and/or decision tree) to classify emphysema on CT scan (>25% vs ≤ 25%)
Measured: baseline
forced expiratory volume in one second
Forced expiratory volume in one second (FEV1) is a measure of severity of the underlying COPD. postbronchodilator FEV1 (expressed a Z-score) in each participant will be measured via spirometry. according to ERS/ATS guidelines. FEV1 (Z-score) will be used as input variable for machine learning classification models (e.g. using logistic regression, support vector machines, random forests and/or decision tree) to classify emphysema on CT scan (>25% vs ≤ 25%)
Measured: baseline
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 start7 Sep 2023
- Primary completionDec 2025 (estimated)
- Study completionMay 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for CopdStudies of the same condition, open and under way.
- 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
- Case only
- Time perspective
- Cross-sectional
- Enrolment
- 200 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Maastricht University
- Sponsor type
- University, hospital or other
- Collaborators
- Roche Pharma AG
- Responsible party
- Sponsor
- Investigators
- Sami Simons, MD PhD, Principal investigator, Maastricht University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05825261
- Sponsor's protocol ID
- NL83173.068.22/METC22-071
- Acronym
- ENBED
- Status last verified
- May 2025
- First submitted
- 9 Mar 2023
- Last updated
- 13 Jun 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 13 Jun 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Exploring Novel Biomarkers for Emphysema Detection: the ENBED Study
- ClinicalTrials.gov ID
- NCT05825261
- Acronym / sponsor ID
- ENBED / NL83173.068.22/METC22-071
- Keywords
- vocal biomarkers, speech, emphysema, capnometry, chest CT
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 200 participants (estimated)
- Lead sponsor
- Maastricht UniversityUniversity, hospital or other
- Collaborators
- Roche Pharma AG
- Study start
- 7 Sep 2023 (actual)
- Primary completion
- Dec 2025 (estimated)
- Study completion
- May 2026 (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.
The goal of this clinical trial is to evaluate whether voice or capnometry, alone or in combination with other (non invasive) biomarkers can be used to detect emphysema on chest CT-scan in people with chronic obstructive pulmonary disease (COPD). The main question it aims to answer is:
- Can a machine-learning based algorithm be developed that can classify the extent of emphysema on chest CT scan from patients with COPD, based on voice and/or capnometry.
Participants will:
- perform different voice-related tasks
- perform capnometry twice (before/after exercise)
- perform a light exercise task between tasks ( 5-sit-to-stand test)
- undergo one venipuncture
Detailed description
This is a cross sectional, single center study. At the clinic, patients with COPD will be invited to perform several voice related tasks (paced reading, sustained vowels, cough, quiet breathing) and will be instructed to perform capnometry measurements. These measurements will be performed before and after a light exercise task (5-STS: 5-sit-to-stand test).
Clinical characterisation of patients including pulmonary function tests (spirometry, body plethysmography, diffusion capacity) and CT scans have been performed in all patients as a part of routine workup in the COPD care pathway. Emphysema will be quantified as low attenuation areas with a density below -950 Hounsfield units (HU) using Syngovia (Siemens, Erlangen, Germany).
The primary outcome will fit a simple machine learning classification model (e.g. using logistic regression, support vector machines, random forests and/or decision tree) to classify logistic regression model for the outcome of emphysema (>25% vs ≤ 25%) from speech features and capnometry. with explanatory variables of speech features. Similar classification methods with incremental models using capnography features will be explored. Prior to carrying out the above analyses, data has to be pre-processed, including merging data, quality control, handling of missing data and feature extraction.
Study design
- Observational model
- Case only
- Time perspective
- Cross-sectional
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| COPD and/or emphysema | – | Other: voice sampling, Other: capnometryCOPD is defined according to COPD Gold 2023 guidelines. Emphysema defined acording to Fleischner criteria (2024) |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The source population (primary dataset) consists of COPD patients in whom a chest CT was performed within 12 months before inclusion into the study.
Key inclusion criteria (6 listed)
- Adults aged over 18 years
- current respiratory smptoms (any dyspnea, cough or sputum)
- spirometry confirmed diagnosis of a non-fully reversible airflow obstruction, defined as a post bronchodilator Forced Expiratory Volume at one second/Forced Vital Capacity (FEV1/FVC ratio) < 0.7 and/or emphysemateus abnormalities on CT imaging.
- presence of risk factors or causes associated with COPD
- chest CT scan performed in the past 12 months prior to inclusion to the study
Key exclusion criteria (9 listed)
- acute exacerbation of COPD within 8 weeks of start of the study
- comorbidities affecting speech or breathing coordination (neuromuscular disease, CVA< BMI > 40)
- comorbidities affecting speech characteristics of dyspnea (severe heart failure, interstitial lung disease)
- comorbidities affecting respiratory system including but not exclusive to asthma or cystic fibrosis
- comorbidities that significantly interfere with interpretation of speech (audio signals), such as Parkinson's disease, bulbar palsy, or vocal cord paralysis.
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 |
|---|---|---|
| voice samplingPatients with COPD will perform several voice-related tasks and capnometry at rest. Thereafter a 5-STS will follow and the voice-related task/capnometry will be repeated | Other | COPD and/or emphysema |
| capnometryPatients with COPD will perform several voice-related tasks and capnometry at rest. Thereafter a 5-STS will follow and the voice-related task/capnometry will be repeated | Other | COPD and/or emphysema |
Primary outcome
percentage of participants having moderate to severe emphysema on a chest CT (defined as > 25%)
Time frame: baseline
A baseline chest CT scan from each participant will be analysed using a lung parenchyma analysis software with automated 3-D quantification of emphysema. Emphysema will be defined as low attenuation areas with a density below -950 Hounsfield units. Patients will be either classified as having low emphysema (less or equal to 25% of emphysema on chest CT scan) or moderate to high emphysema (more than 25% of emphysema on chest CT scan)
number of (non-linguistic) inhalations per syllable from sustained vowel
Time frame: baseline
Participants will perform a sustained vowel (saying 'a') at rest and after light exercise from which several measurements can be obtained: Syllables per breath group, speaking rate, articulation rate, mean frequency, mean intensity, pitch variability, mean center of gravity, inhalations, non-linguistic inhalations, ratio voice/silence intervals. Based on previous research (Merkus J, 2020) HNR, shimer, vowel duration en number of (non-linguistic) inhalations per syllable were putative vocal biomarkers in COPD. First key determinant therefore is the number of (non-linguistic) inhalations per syllable during sustained vowel of each participant. This will be used as input for machine learning classification models (e.g. using logistic regression, support vector machines, random forests and/or decision tree) to classify emphysema on CT scan (>25% vs ≤ 25%)
harmonics-to-noise-ratio from sustained vowel
Time frame: baseline
Participants will perform a sustained vowel (saying 'a') at rest and after light exercise from which several measurements can be obtained: Syllables per breath group, speaking rate, articulation rate, mean frequency, mean intensity, pitch variability, mean center of gravity, inhalations, non-linguistic inhalations, ratio voice/silence intervals. Based on previous research (Merkus J, 2020) HNR, shimer, vowel duration en number of (non-linguistic) inhalations per syllable were putative vocal biomarkers in COPD. Second key determinant therefore is the harmonics-to-noise ratio during sustained vowel. This will be used as input for machine learning classification models (e.g. using logistic regression, support vector machines, random forests and/or decision tree) to classify emphysema on CT scan (>25% vs ≤ 25%)
vowel duration from sustained vowel
Time frame: baseline
Participants will perform a sustained vowel (saying 'a') at rest and after light exercise from which several measurements can be obtained: Syllables per breath group, speaking rate, articulation rate, mean frequency, mean intensity, pitch variability, mean center of gravity, inhalations, non-linguistic inhalations, ratio voice/silence intervals. Based on previous research (Merkus J, 2020) HNR, shimer, vowel duration en number of (non-linguistic) inhalations per syllable were putative vocal biomarkers in COPD. Third key determinant therefore is the vowel duration (in seconds) during sustained vowel. This will be used as input for machine learning classification models (e.g. using logistic regression, support vector machines, random forests and/or decision tree) to classify emphysema on CT scan (>25% vs ≤ 25%)
shimmer from sustained vowel
Time frame: baseline
Participants will perform a sustained vowel (saying 'a') at rest and after light exercise from which several measurements can be obtained: Syllables per breath group, speaking rate, articulation rate, mean frequency, mean intensity, pitch variability, mean center of gravity, inhalations, non-linguistic inhalations, ratio voice/silence intervals. Based on previous research (Merkus J, 2020) HNR, shimer, vowel duration en number of (non-linguistic) inhalations per syllable were putative vocal biomarkers in COPD. Fourth key determinant therefore is shimmer (in Hz) during sustained vowel. This will be used as input for machine learning classification models (e.g. using logistic regression, support vector machines, random forests and/or decision tree) to classify emphysema on CT scan (>25% vs ≤ 25%)
end-tidal CO2 from capnography (ETCO2)
Time frame: baseline
Participation perform a quiet breathing (tidal volume) at rest and after light exercise to measure CO2 during exhalation (capnogram) from which several parameters can be measured, of which end-tidal CO2 (etCO2), phase 2 slope and phase 3 slope are most distinctive for COPD phenotyping (Pereira 2016). First key determinant from capnography is therefore end-tidal CO2 (in mm Hg). This will be used as input for machine learning classification models (e.g. using logistic regression, support vector machines, random forests and/or decision tree) to classify emphysema on CT scan (>25% vs ≤ 25%)
phase-2 slope from capnography (slp2)
Time frame: baseline
Participation perform a quiet breathing (tidal volume) at rest and after light exercise to measure CO2 during exhalation (capnogram) from which several (more than 80) parameters can be measured, of which end-tidal CO2 (etCO2), phase 2 slope and phase 3 slope are most distinctive for COPD phenotyping (Pereira 2016). Second key determinant from capnography is therefore phase-2 slope (in mm Hg/L). This will be used as input for machine learning classification models (e.g. using logistic regression, support vector machines, random forests and/or decision tree) to classify emphysema on CT scan (>25% vs ≤ 25%)
phase-2 slope from capnography (slp3)
Time frame: baseline
Participation perform a quiet breathing (tidal volume) at rest and after light exercise to measure CO2 during exhalation (capnogram) from which several parameters can be measured, of which end-tidal CO2, phase 2 slope and phase 3 slope are most distinctive for COPD phenotyping (Pereira 2016). Third key determinant from capnography is therefore phase 3 slope (in mm Hg/L). This will be used as input for machine learning classification models (e.g. using logistic regression, support vector machines, random forests and/or decision tree) to classify emphysema on CT scan (>25% vs ≤ 25%)
4 secondary outcomes
serum sRAGE
Time frame: baseline
ratio of residual volume to total lung capacity (RV/TLC) on body plethysmography
Time frame: baseline
diffusion capacity of the lungs for carbon monoxide
Time frame: baseline
forced expiratory volume in one second
Time frame: baseline
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 13 Jun 2025.
- Individual participant dataSharing planned: Undecided
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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.
Sami Simons, MD PhD principal investigator
Maastricht University
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Copd and Emphysema, run by Maastricht University at 2 sites in Netherlands.
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 6 inclusion and 9 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 13 Jun 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in Netherlands. 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 Sami Simons, MD PhD (+31 043 3876543, sami.simons@mumc.nl), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
percentage of participants having moderate to severe emphysema on a chest CT (defined as > 25%) (time frame: baseline). The study lists 8 primary outcomes in all. 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 13 Jun 2025; AzTrials last refreshed its copy on 29 Oct 2026.