exploring novel biomarkers for emphysema detection

Conditions: Copd; Emphysema

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

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.

Eligibility criteria:
inclusion criteria: * 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 exclusion criteria: * acute exacerbation of copd within 8 weeks of start of the study * comorbidities affecting speech or breathing coordination (neuromuscular disease, cva\ 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.

Keywords: vocal biomarkers speech emphysema capnometry chest ct