Wang, XuejieVilla, CarmenDobarganes, YadiraOlveira, CasildaGirón, RosaGarcía Clemente, MartaMáiz, LuisSibila, OriolGolpe, RafaelMenéndez, RosarioRodríguez López, Juan PedroPrados, ConcepciónMartinez García, Miguel AngelRodriguez Hermosa, Juan LuisRosa, David de laDuran, XavierGarcia Ojalvo, JordiBarreiro, Esther2023-06-222023-06-222022-01-172227-905910.3390/biomedicines10020225https://hdl.handle.net/20.500.14352/71609Differential 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.engAtribución 3.0 EspañaSystemic Inflammatory Biomarkers Define Specific Clusters in Patients with Bronchiectasis: A Large-Cohort Studyjournal articlehttps://doi.org/10.3390/biomedicines10020225https://www.mdpi.com/2227-9059/10/2/225/htmopen accessnon-cystic fibrosis bronchiectasisblood neutrophileosinophillymphocyte countsC reactive proteinhemoglobinhierarchical clusteringphenotypic clustersmultivariate analysesclinical outcomesdisease severity scoresHematologíaNeumología3205.04 Hematología3205.08 Enfermedades Pulmonares