Early diagnostic BioMARKers in exacerbations of chronic obstructive pulmonary disease: protocol of the exploratory, prospective, longitudinal, single-centre, observational MARKED study. Issue 3 (3rd March 2023) - Waeijen-Smit, Kiki; DiGiandomenico, Antonio; Bonnell, Jessica; Ostridge, Kristoffer; Gehrmann, Ulf; Sellman, Bret R; Kenny, Tara; van Kuijk, Sander; Peerlings, Daphne; Spruit, Martijn A; Simons, Sami O; Houben-Wilke, Sarah; et al.
Abstract : 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). Trial registration number: NCT05315674 .