Exploring Novel Biomarkers for Emphysema Detection: the ENBED Study
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 200
- 试验地点
- 2
- 主要终点
- percentage of participants having moderate to severe emphysema on a chest CT (defined as > 25%)
研究概览
简要总结
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
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Case Only
- 时间视角
- Cross Sectional
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •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
排除标准
- •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.
结局指标
主要结局
percentage of participants having moderate to severe emphysema on a chest CT (defined as > 25%)
时间窗: 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
时间窗: 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%)
phase-2 slope from capnography (slp2)
时间窗: 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)
时间窗: 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%)
harmonics-to-noise-ratio from sustained vowel
时间窗: 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
时间窗: 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
时间窗: 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)
时间窗: 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%)
次要结局
- serum sRAGE(baseline)
- ratio of residual volume to total lung capacity (RV/TLC) on body plethysmography(baseline)
- forced expiratory volume in one second(baseline)
- diffusion capacity of the lungs for carbon monoxide(baseline)