ArticleBMJ open2023
Early diagnostic BioMARKers in exacerbations of chronic obstructive pulmonary disease: protocol of the exploratory, prospective, longitudinal, single-centre, observational MARKED study.
Article in BMJ open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05315674 (Early Diagnostic BioMARKers in Exacerbations of COPD), which is not on this map. Cited by 6 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Early Diagnostic BioMARKers in Exacerbations of COPD: the MARKED Study
Who cites it
6 citing papers in PubMed, 2 syntheses or guidelines pooled it, 4 citations in OpenAlex.
- Differentiating the start of an exacerbation from day-to-day variation in people with COPD: a systematic review.European respiratory review : an official journal of the European Respiratory Society · 2026Pooled it
- Predictors of Acute Exacerbations in COPD: A Systematic Review.International journal of chronic obstructive pulmonary disease · 2026Pooled it
- Stage-Dependent Reorganization of Inflammatory Biomarker Networks During Acute Exacerbations of COPD.Life (Basel, Switzerland) · 2026Article
- Bidirectional Clinical Interactions among Exacerbations and Comorbidities in COPD: A Narrative Review.Seminars in respiratory and critical care medicine · 2026Review
- Multimodal machine learning predicts type 2 respiratory failure in COPD exacerbations: a multicenter XGBoost model with clinical nomogram.Frontiers in medicine · 2026Article
- Development and validation of the machine learning model for acute exacerbation of chronic obstructive pulmonary disease prediction based on inflammatory biomarkers.Frontiers in medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 4 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionAcute 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.
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