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 Giron, Rosa
A1 Garcia-Clemente, Marta
A1 Maiz, Luis
A1 Sibila, Oriol
A1 Golpe, Rafael
A1 Menendez, Rosario
A1 Rodriguez-Lopez, Juan
A1 Prados, Concepcion
A1 Angel Martinez-Garcia, Miguel
A1 Luis Rodriguez, Juan
A1 de la Rosa, David
A1 Duran, Xavier
A1 Garcia-Ojalvo, Jordi
A1 Barreiro, Esther
K1 non-cystic fibrosis bronchiectasis
K1 blood neutrophil
K1 eosinophil
K1 lymphocyte counts
K1 C reactive protein
K1 hemoglobin
K1 hierarchical clustering
K1 phenotypic clusters
K1 multivariate analyses
K1 clinical outcomes
K1 disease severity scores
K1 Cystic fibrosis bronchiectasis
K1 Guidelines
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 Mdpi
YR 2022
FD 2022-02-01
LK http://hdl.handle.net/10668/20814
UL http://hdl.handle.net/10668/20814
LA en
DS RISalud
RD Apr 19, 2025