An observational study in Copd and Emphysema, sponsored by Maastricht University. Recruiting at 2 sites in Netherlands. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2025-06-13.
Sponsored by Maastricht University · Observational
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:
Participants will:
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.
5,520 studies on the registry are indexed under Pulmonary Disease, Chronic Obstructive; 856 are open to participants now.
This study's planned enrollment of 200 is above the median of 158 across 1,497 observational studies indexed under Pulmonary Disease, Chronic Obstructive.
Browse Pulmonary Disease, Chronic Obstructive studies →Maastricht University is the lead sponsor of 97 studies on the registry; 20 are open to participants now.
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The source population (primary dataset) consists of COPD patients in whom a chest CT was performed within 12 months before inclusion into the study.
Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.
Exclusion Criteria:
Underlined terms in the criteria open these too. Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
From a breathing test (spirometry): how much air you can blow out in the first second. Often given as a percentage of what is expected for your age, height and sex.
Chronic obstructive pulmonary disease: long-term lung disease that makes it hard to breathe out, including emphysema and chronic bronchitis.
Weight in kilograms divided by height in metres squared. Example: 70 kg and 1.75 m gives 70 ÷ (1.75 × 1.75) ≈ 22.9.
What counts as overweight or obesity differs between countries (some use lower cut-offs), so go by the range the study states.
A drug or device being studied in a clinical trial.
It may be new, or already approved and being studied for another use, dose or form. Criteria often leave out people who took part in another study of one within a recent period, commonly 30 days or five half-lives.
FEV1
From a breathing test (spirometry): how much air you can blow out in the first second. Often given as a percentage of what is expected for your age, height and sex.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
COPD
Chronic obstructive pulmonary disease: long-term lung disease that makes it hard to breathe out, including emphysema and chronic bronchitis.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
BMI (body mass index)
Weight in kilograms divided by height in metres squared. Example: 70 kg and 1.75 m gives 70 ÷ (1.75 × 1.75) ≈ 22.9.
What counts as overweight or obesity differs between countries (some use lower cut-offs), so go by the range the study states.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Investigational drug or device
A drug or device being studied in a clinical trial.
It may be new, or already approved and being studied for another use, dose or form. Criteria often leave out people who took part in another study of one within a recent period, commonly 30 days or five half-lives.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
COPD is defined according to COPD Gold 2023 guidelines. Emphysema defined acording to Fleischner criteria (2024)
Other: voice sampling · Other: capnometry
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
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
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)
Time frame: 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%)
Time frame: 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%)
Time frame: 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%)
Time frame: 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%)
Time frame: 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%)
Time frame: 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%)
Time frame: 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%)
Time frame: baseline
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%)
Time frame: 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%)
Time frame: 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%)
Time frame: 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%)
Time frame: baseline
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