Wang, XuejieVilla, CarmenDobarganes, YadiraOlveira, CasildaGiron, RosaGarcia-Clemente, MartaMaiz, LuisSibila, OriolGolpe, RafaelMenendez, RosarioRodriguez-Lopez, JuanPrados, ConcepcionAngel Martinez-Garcia, MiguelLuis Rodriguez, Juande la Rosa, DavidDuran, XavierGarcia-Ojalvo, JordiBarreiro, Esther2023-05-032023-05-032022-02-01http://hdl.handle.net/10668/20814Differential 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.enAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/non-cystic fibrosis bronchiectasisblood neutrophileosinophillymphocyte countsC reactive proteinhemoglobinhierarchical clusteringphenotypic clustersmultivariate analysesclinical outcomesdisease severity scoresCystic fibrosis bronchiectasisGuidelinesresearch articleopen access10.3390/biomedicines100202252227-9059https://www.mdpi.com/2227-9059/10/2/225/pdf?version=1643071497920260900002