%0 Journal Article
%A Wang, Xuejie
%A Villa, Carmen
%A Dobarganes, Yadira
%A Olveira, Casilda
%A Giron, Rosa
%A Garcia-Clemente, Marta
%A Maiz, Luis
%A Sibila, Oriol
%A Golpe, Rafael
%A Menendez, Rosario
%A Rodriguez-Lopez, Juan
%A Prados, Concepcion
%A Angel Martinez-Garcia, Miguel
%A Luis Rodriguez, Juan
%A de la Rosa, David
%A Duran, Xavier
%A Garcia-Ojalvo, Jordi
%A Barreiro, Esther
%T Systemic Inflammatory Biomarkers Define Specific Clusters in Patients with Bronchiectasis: A Large-Cohort Study
%D 2022
%U http://hdl.handle.net/10668/20814
%X 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 (FEV1, 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.
%K non-cystic fibrosis bronchiectasis
%K blood neutrophil
%K eosinophil
%K lymphocyte counts
%K C reactive protein
%K hemoglobin
%K hierarchical clustering
%K phenotypic clusters
%K multivariate analyses
%K clinical outcomes
%K disease severity scores
%K Cystic fibrosis bronchiectasis
%K Guidelines
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