TY - JOUR
T1 - Early diagnostic BioMARKers in exacerbations of chronic obstructive pulmonary disease: protocol of the exploratory, prospective, longitudinal, single-centre, observational MARKED study
AU - Waeijen-Smit, Kiki
AU - DiGiandomenico, Antonio
AU - Bonnell, Jessica
AU - Ostridge, Kristoffer
AU - Gehrmann, Ulf
AU - Sellman, Bret R.
AU - Kenny, Tara
AU - van Kuijk, Sander
AU - Peerlings, Daphne
AU - Spruit, Martijn A.
AU - Simons, Sami O.
AU - Houben-Wilke, Sarah
AU - Franssen, Frits M. E.
PY - 2023/3/1
Y1 - 2023/3/1
N2 - Introduction Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) play a pivotal role in the burden and progressive course of chronic obstructive pulmonary disease (COPD). As such, disease management is predominantly based on the prevention of these episodes of acute worsening of respiratory symptoms. However, to date, personalised prediction and early and accurate diagnosis of AECOPD remain unsuccessful. Therefore, the current study was designed to explore which frequently measured biomarkers can predict an AECOPD and/or respiratory infection in patients with COPD. Moreover, the study aims to increase our understanding of the heterogeneity of AECOPD as well as the role of microbial composition and hostmicrobiome interactions to elucidate new disease biology in COPD.Methods and analysis The 'Early diagnostic BioMARKers in Exacerbations of COPD' study is an exploratory, prospective, longitudinal, single-centre, observational study with 8-week follow-up enrolling up to 150 patients with COPD admitted to inpatient pulmonary rehabilitation at Ciro (Horn, the Netherlands). Respiratory symptoms, vitals, spirometry and nasopharyngeal, venous blood, spontaneous sputum and stool samples will be frequently collected for exploratory biomarker analysis, longitudinal characterisation of AECOPD (ie, clinical, functional and microbial) and to identify host-microbiome interactions. Genomic sequencing will be performed to identify mutations associated with increased risk of AECOPD and microbial infections. Predictors of time-to-first AECOPD will be modelled using Cox proportional hazards' regression. Multiomic analyses will provide a novel integration tool to generate predictive models and testable hypotheses about disease causation and predictors of disease progression.Ethics and dissemination This protocol was approved by the Medical Research Ethics Committees United (MEC-U), Nieuwegein, the Netherlands (NL71364.100.19).rial registration number NCT05315674
AB - Introduction Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) play a pivotal role in the burden and progressive course of chronic obstructive pulmonary disease (COPD). As such, disease management is predominantly based on the prevention of these episodes of acute worsening of respiratory symptoms. However, to date, personalised prediction and early and accurate diagnosis of AECOPD remain unsuccessful. Therefore, the current study was designed to explore which frequently measured biomarkers can predict an AECOPD and/or respiratory infection in patients with COPD. Moreover, the study aims to increase our understanding of the heterogeneity of AECOPD as well as the role of microbial composition and hostmicrobiome interactions to elucidate new disease biology in COPD.Methods and analysis The 'Early diagnostic BioMARKers in Exacerbations of COPD' study is an exploratory, prospective, longitudinal, single-centre, observational study with 8-week follow-up enrolling up to 150 patients with COPD admitted to inpatient pulmonary rehabilitation at Ciro (Horn, the Netherlands). Respiratory symptoms, vitals, spirometry and nasopharyngeal, venous blood, spontaneous sputum and stool samples will be frequently collected for exploratory biomarker analysis, longitudinal characterisation of AECOPD (ie, clinical, functional and microbial) and to identify host-microbiome interactions. Genomic sequencing will be performed to identify mutations associated with increased risk of AECOPD and microbial infections. Predictors of time-to-first AECOPD will be modelled using Cox proportional hazards' regression. Multiomic analyses will provide a novel integration tool to generate predictive models and testable hypotheses about disease causation and predictors of disease progression.Ethics and dissemination This protocol was approved by the Medical Research Ethics Committees United (MEC-U), Nieuwegein, the Netherlands (NL71364.100.19).rial registration number NCT05315674
KW - Chronic airways disease
KW - Microbiology
KW - BACTERIOLOGY
KW - Molecular diagnostics
KW - Rehabilitation medicine
KW - Thoracic medicine
KW - COPD EXACERBATIONS
KW - SPUTUM MICROBIOME
KW - RELEVANCE
KW - FREQUENCY
KW - HEALTH
KW - IMPACT
U2 - 10.1136/bmjopen-2022-068787
DO - 10.1136/bmjopen-2022-068787
M3 - Article
C2 - 36868599
SN - 2044-6055
VL - 13
JO - BMJ Open
JF - BMJ Open
IS - 3
M1 - e068787
ER -