RT Journal Article
T1 Systemic Inflammatory Biomarkers Define Specific Clusters in Patients with Bronchiectasis: A Large-Cohort Study
A1 Wang, Xuejie
A1 Villa, Carmen
A1 Dobarganes, Yadira
A1 Olveira, Casilda
A1 Girón, Rosa
A1 García Clemente, Marta
A1 Máiz, Luis
A1 Sibila, Oriol
A1 Golpe, Rafael
A1 Menéndez, Rosario
A1 Rodríguez López, Juan Pedro
A1 Prados, Concepción
A1 Martinez García, Miguel Angel
A1 Rodríguez Hermosa, Juan Luis
A1 Rosa, David de la
A1 Duran, Xavier
A1 Garcia Ojalvo, Jordi
A1 Barreiro, Esther
AB 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.
PB MPDI
SN 2227-9059
YR 2022
FD 2022-01-17
LK https://hdl.handle.net/20.500.14352/71609
UL https://hdl.handle.net/20.500.14352/71609
LA eng
NO Instituto de Salud Carlos III (ISCIII)/FEDER
NO Ministerio de Ciencia e Innovación (MICINN)
NO Unidad de Excelencia María de Maeztu
DS Docta Complutense
RD 20 abr 2025