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Systemic Inflammatory Biomarkers Define Specific Clusters in Patients with Bronchiectasis: A Large-Cohort Study



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Wang, Xuejie and Villa, Carmen and Dobarganes, Yadira and Olveira, Casilda and Girón, Rosa and García Clemente, Marta and Máiz, Luis and Sibila, Oriol and Golpe, Rafael and Menéndez, Rosario and Rodríguez López, Juan Pedro and Prados, Concepción and Martinez García, Miguel Angel and Rodriguez Hermosa, Juan Luis and Rosa, David de la and Duran, Xavier and Garcia Ojalvo, Jordi and Barreiro, Esther (2022) Systemic Inflammatory Biomarkers Define Specific Clusters in Patients with Bronchiectasis: A Large-Cohort Study. Biomedicines, 10 (2). p. 225. ISSN 2227-9059

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Official URL: https://doi.org/10.3390/biomedicines10020225


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.

Item Type:Article
Uncontrolled Keywords:non-cystic fibrosis bronchiectasis; blood neutrophil; eosinophil; lymphocyte counts; C reactive protein; hemoglobin; hierarchical clustering; phenotypic clusters; multivariate analyses; clinical outcomes; disease severity scores
Subjects:Medical sciences > Medicine > Hematology
Medical sciences > Medicine > Pneumology
ID Code:72839
Deposited On:27 Jun 2022 14:31
Last Modified:28 Jun 2022 11:18

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