ENBED: Exploring Novel Biomarkers for Emphysema Detection

Sponsor

Maastricht University (Other)

Overall Status

Not yet recruiting

CT.gov ID

NCT05825261

Collaborator

Roche Pharma AG (Industry)

200

Enrollment

Location

17.1

Anticipated Duration (Months)

11.7

Patients Per Site Per Month

Study Details

Study Description

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

Condition or Disease	Intervention/Treatment	Phase
Copd Emphysema	Other: voice sampling Other: capnometry

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

Study Type:

Observational

Anticipated Enrollment :

200 participants

Observational Model:

Case-Only

Time Perspective:

Cross-Sectional

Official Title:

Exploring Novel Biomarkers for Emphysema Detection: the ENBED Study

Anticipated Study Start Date :

Apr 1, 2023

Anticipated Primary Completion Date :

Apr 1, 2024

Anticipated Study Completion Date :

Sep 1, 2024

Arms and Interventions

Arm	Intervention/Treatment
COPD COPD is defined according to COPD Gold 2023 guidelines.	Other: voice sampling 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 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

Arm

Intervention/Treatment

COPD

COPD is defined according to COPD Gold 2023 guidelines.

Other: voice sampling

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

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

Outcome Measures

Primary Outcome Measures

percentage of participants having moderate to severe emphysema on a chest CT (defined as > 25%) [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 [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 [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 [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 [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) [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) [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) [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%)

Secondary Outcome Measures

serum sRAGE [baseline]
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%)
ratio of residual volume to total lung capacity (RV/TLC) on body plethysmography [baseline]
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%)
diffusion capacity of the lungs for carbon monoxide [baseline]
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%)
forced expiratory volume in one second [baseline]
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%)

Eligibility Criteria

Criteria

Ages Eligible for Study:

18 Years and Older

Sexes Eligible for Study:

All

Accepts Healthy Volunteers:

Inclusion Criteria:

COPD diagnosis based on COPD Gold 2023 guideline, including
current respiratory symptoms (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
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

Exclusion Criteria:

acute exacerbation of COPD within 8 weeks of start of the study
comorbidities affecting speech or breathing coordination (neuromuscular disease, CVA)
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.
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.