22b17ac7b89fe6c7a03401c22059dd5d pmc_35203435.pdf 6eaf5563995c32361c8e2fc086e7c7695b0ea4a9 pmc_35203435.pdf 5f6cec0b2673571c0c0f1becfc364285cb590a810f7f72161cf69efa18603790 pmc_35203435.pdf Title: Systemic Inflammatory Biomarkers Define Specific Clusters in Patients with Bronchiectasis: A Large-Cohort Study Subject: 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. Keywords: non-cystic fibrosis bronchiectasis; blood neutrophil; eosinophil; lymphocyte counts; C reactive protein; hemoglobin; hierarchical clustering; phenotypic clusters; multivariate analyses; clinical outcomes; disease severity scores Author: Xuejie Wang, Carmen Villa, Yadira Dobarganes, Casilda Olveira, Rosa Girón, Marta García-Clemente, Luis Máiz, Oriol Sibila, Rafael Golpe, Rosario Menéndez, Juan Rodríguez-López, Concepción Prados, Miguel Angel Martinez-García, Juan Luis Rodriguez, David de la Rosa, Xavier Duran, Jordi Garcia-Ojalvo and Esther Barreiro Creator: LaTeX with hyperref Producer: pdfTeX-1.40.21 CreationDate: Tue Jan 25 01:40:55 2022 CET ModDate: Tue Jan 25 01:44:57 2022 CET Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 18 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 3945451 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- 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TrueType Identity-H yes yes yes 237 0 PMCLKC+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 243 0 PMCLIP+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 255 0 PMCLKA+PalatinoLinotype TrueType WinAnsi yes yes no 258 0 PMCLKB+PalatinoLinotype,Italic CID TrueType Identity-H yes yes yes 261 0 PMCLKC+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 267 0 PMCLIP+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 283 0 PMCLKA+PalatinoLinotype TrueType WinAnsi yes yes no 286 0 PMCLKC+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 289 0 PMCLIP+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 304 0 PMCLKA+PalatinoLinotype TrueType WinAnsi yes yes no 307 0 PMCLKC+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 310 0 PMCLIP+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 322 0 PMCLKA+PalatinoLinotype TrueType WinAnsi yes yes no 325 0 PMCLKC+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 328 0 Jhove (Rel. 1.22.1, 2019-04-17) Date: 2022-03-15 02:32:51 CET 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edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@371a67ec Item: Title: Statistical Analysis Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@5ed828d Item: Title: Results Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@50d0686 Children: Item: Title: General Clinical Characteristics of the Three Patient Clusters Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@7a3d45bd Item: Title: Systemic Inflammatory and Nutritional Parameters in the Three Cluster Patients Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@1e7c7811 Item: Title: Multivariate Analysis Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@77ec78b9 Item: Title: Discussion Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@1a3869f4 Item: Title: Conclusions Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@a38d7a3 Item: Title: References Destination: edu.harvard.hul.ois.jhove.module.pdf.PdfSimpleObject@77f99a05 Info: Title: Systemic Inflammatory Biomarkers Define Specific Clusters in Patients with Bronchiectasis: A Large-Cohort Study Author: Xuejie Wang, Carmen Villa, Yadira Dobarganes, Casilda Olveira, Rosa Girón, Marta García-Clemente, Luis Máiz, Oriol Sibila, Rafael Golpe, Rosario Menéndez, Juan Rodríguez-López, Concepción Prados, Miguel Angel Martinez-García, Juan Luis Rodriguez, David de la Rosa, Xavier Duran, Jordi Garcia-Ojalvo and Esther Barreiro Subject: 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. 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