Exploring Novel Biomarkers for Emphysema Detection (ENBED)

Maastricht University

Status

Enrolling

Conditions

Emphysema

Copd

Treatments

Other: capnometry

Other: voice sampling

Study type

Observational

Funder types

Other

Industry

Identifiers

NCT05825261

NL83173.068.22/METC22-071

Details and patient eligibility

About

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

Full 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.

Enrollment

200 estimated patients

Sex

All

Ages

18+ years old

Volunteers

No 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.