Exploring Novel Biomarkers for Emphysema Detection

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)

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%)

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%)

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%)

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%)

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%)

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%)

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%)

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%)

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 (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 (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%)