Systemic Inflammatory Biomarkers Define Specific Clusters in Patients with Bronchiectasis : A Large-Cohort Study
Wang, Xuejie (Universitat Autònoma de Barcelona. Departament de Medicina)
Villa, Carmen (Hospital Fuensanta, Madrid)
Dobarganes, Yadira (Hospital Fuensanta, Madrid)
Olveira, Casilda (Universidad de Málaga)
Girón, Rosa (Hospital Universitario de la Princesa (Madrid))
García Clemente, Marta (Hospital Universitario Central de Asturias)
Máiz, Luis (Hospital Universitario Ramón y Cajal (Madrid))
Sibila, Oriol (Centro de Investigación Biomédica en Red de Enfermedades Respiratorias)
Golpe, Rafael (Hospital Universitario Lucus Augusti (Lugo))
Menéndez, Rosario (Hospital Universitari i Politècnic La Fe (València))
Rodríguez-López, Juan (Hospital Universitario San Agustín (Avilés))
Prados, Concepción (Hospital Universitario La Paz (Madrid))
Martinez-García, Miguel Angel (Hospital Universitari i Politècnic La Fe (València))
Rodriguez Hermosa, Juan Luis (Universidad Complutense de Madrid)
de la Rosa Carrillo, David (Institut d'Investigació Biomèdica Sant Pau)
Duran-Jordà, Xavier (Institut Hospital del Mar d'Investigacions Mèdiques)
Garcia-Ojalvo, Jordi (Universitat Pompeu Fabra)
Barreiro, Esther (Universitat Pompeu Fabra)

Date:	2022
Abstract:	Differential phenotypic characteristics using data mining approaches were defined in a large cohort of patients from the Spanish Online Bronchiectasis Registry (RIBRON). Three differential phenotypic clusters (hierarchical clustering, scikit-learn library for Python, and agglomerative methods) according to systemic biomarkers: neutrophil, eosinophil, and lymphocyte counts, C reactive protein, and hemoglobin were obtained in a patient large-cohort (n = 1092). Clusters #1-3 were named as mild, moderate, and severe on the basis of disease severity scores. Patients in cluster #3 were significantly more severe (FEV, age, colonization, extension, dyspnea (FACED), exacerbation (EFACED), and bronchiectasis severity index (BSI) scores) than patients in clusters #1 and #2. Exacerbation and hospitalization numbers, Charlson index, and blood inflammatory markers were significantly greater in cluster #3 than in clusters #1 and #2. Chronic colonization by Pseudomonas aeruginosa and COPD prevalence were higher in cluster # 3 than in cluster #1. Airflow limitation and diffusion capacity were reduced in cluster #3 compared to clusters #1 and #2. Multivariate ordinal logistic regression analysis further confirmed these results. Similar results were obtained after excluding COPD patients. Clustering analysis offers a powerful tool to better characterize patients with bronchiectasis. These results have clinical implications in the management of the complexity and heterogeneity of bronchiectasis patients.
Grants:	Instituto de Salud Carlos III FIS-21/00215 Ministerio de Ciencia e Innovación CEX2018-000792-M
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	Non-cystic fibrosis bronchiectasis ; Blood neutrophil ; Eosinophil ; Lymphocyte counts ; C reactive protein ; Hemoglobin ; Hierarchical clustering ; Phenotypic clusters ; Multivariate analyses ; Clinical outcomes ; Disease severity scores
Published in:	Biomedicines, Vol. 10 (january 2022) , ISSN 2227-9059

DOI: 10.3390/biomedicines10020225
PMID: 35203435

18 p, 3.8 MB

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Research literature > UAB research groups literature > Research Centres and Groups (research output) > Health sciences and biosciences > Institut de Recerca Sant Pau
Articles > Research articles
Articles > Published articles