Wang, XuejieVilla, CarmenDobarganes, YadiraOlveira, CasildaGirón, RosaGarcía-Clemente, MartaMáiz, LuisSibila, OriolGolpe, RafaelMenéndez, RosarioRodríguez-López, JuanPrados, ConcepciónMartínez-García, Miguel ÁngelRodriguez, Juan LuisRosa, David de laDuran Jordà, Xavier, 1974-García Ojalvo, JordiBarreiro Portela, Esther2022-03-072022-03-072022Wang X, Villa C, Dobarganes Y, Olveira C, Girón R, García-Clemente M, Máiz L, Sibila O, Golpe R, Menéndez R, Rodríguez-López J, Prados C, Martinez-García MA, Rodriguez JL, de la Rosa D, Duran X, Garcia-Ojalvo J, Barreiro E. Systemic inflammatory biomarkers define specific clusters in patients with bronchiectasis: A large-cohort study. Biomedicines. 2022 Jan 21;10(2):225. DOI: 10.3390/biomedicines100202252227-9059http://hdl.handle.net/10230/52634Differential 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.application/pdfeng© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).info:eu-repo/semantics/articlehttp://dx.doi.org/10.3390/biomedicines10020225C reactive proteinBlood neutrophilClinical outcomesDisease severity scoresEosinophilHemoglobinHierarchical clusteringLymphocyte countsMultivariate analysesNon-cystic fibrosis bronchiectasisPhenotypic clustersinfo:eu-repo/semantics/openAccess