Wang, XuejieVilla, CarmenDobarganes, YadiraOlveira, CasildaGirón, RosaGarcía Clemente, MartaMáiz, LuisSibila, OriolGolpe, RafaelMenéndez, RosarioRodríguez López, Juan PedroPrados, ConcepciónMartinez García, Miguel AngelRodríguez 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 Españahttps://creativecommons.org/licenses/by/3.0/es/journal 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