Exploring Novel Biomarkers for Emphysema Detection

NCT05825261 | Recruiting

• 1 other identifier: NL83173.068.22/METC22-071
• Study Type: observational
• Target: 200
• Locations: 1 country
• Sites: 2

Brief 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). 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

Conditions

Copd; Emphysema

Keywords

vocal biomarkers; speech; emphysema; capnometry; chest CT

Outcome Measures

Primary Outcomes (8)

• 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)

  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%)

  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%)

  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%)

  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%)

  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%)

  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%)

  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%)

  baseline

Secondary Outcomes (4)

• serum sRAGE

  baseline

• ratio of residual volume to total lung capacity (RV/TLC) on body plethysmography

  baseline

• diffusion capacity of the lungs for carbon monoxide

  baseline

• forced expiratory volume in one second

  baseline

Study Arms (1)

COPD and/or emphysema

COPD is defined according to COPD Gold 2023 guidelines. Emphysema defined acording to Fleischner criteria (2024)

Other: voice sampling; Other: capnometry

Interventions

voice sampling OTHER

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

COPD and/or emphysema

capnometry OTHER

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

COPD and/or emphysema

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

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.

You may qualify if:

• 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
• able to understand, read and write Dutch language

You may not qualify if:

• 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)
• 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.

Sponsors & Collaborators

• Maastricht University: lead
• Roche Pharma AG: collaborator

Study Sites (2)

Dept of Respiratory Medicine, Maastricht University Medical Centre

Maastricht, Limburg, 6202 AZ, Netherlands

RECRUITING

Laurentius Ziekenhuis

Roermond, Limburg, 6043 CV, Netherlands

NOT YET RECRUITING

Biospecimen

Retention: SAMPLES WITHOUT DNA

blood for sRAGE

MeSH Terms

Conditions

Pulmonary Disease, Chronic Obstructive; Emphysema; Speech

Interventions

Blood Gas Monitoring, Transcutaneous

Condition Hierarchy (Ancestors)

Lung Diseases, Obstructive; Lung Diseases; Respiratory Tract Diseases; Chronic Disease; Disease Attributes; Pathologic Processes; Pathological Conditions, Signs and Symptoms; Verbal Behavior; Communication; Behavior

Intervention Hierarchy (Ancestors)

Oximetry; Blood Gas Analysis; Blood Chemical Analysis; Clinical Chemistry Tests; Clinical Laboratory Techniques; Diagnostic Techniques and Procedures; Diagnosis; Heart Function Tests; Diagnostic Techniques, Cardiovascular; Respiratory Function Tests; Diagnostic Techniques, Respiratory System; Investigative Techniques

Study Officials

• Sami Simons, MD PhD

  Maastricht University

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Sami Simons, MD PhD

CONTACT

+31 043 3876543; sami.simons@mumc.nl

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: CROSS SECTIONAL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: March 9, 2023
• First Posted: April 24, 2023
• Study Start: September 7, 2023
• Primary Completion: December 1, 2025
• Study Completion: May 1, 2026
• Last Updated: June 13, 2025

Record last verified: 2025-05

Locations

NL (2)