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We sought to investigate differential phenotypic characteristics according to neutrophil counts, using a biostatistics approach in a large-cohort study from the Spanish Online Bronchiectasis Registry (RIBRON). The 1034 patients who met the inclusion criteria were clustered into two groups on the basis of their blood neutrophil levels. Using the Mann–Whitney U test to explore potential differences according to FACED and EFACED scores between the two groups, a neutrophil count of 4990 cells/µL yielded the most balanced cluster sizes: (1) above-threshold (n = 337) and (2) below-threshold (n = 697) groups. Patients above the threshold showed significantly worse lung function parameters and nutritional status, while systemic inflammation levels were higher than in the below-threshold patients. In the latter group, the proportions of patients with mild disease were greater, while a more severe disease was present in the above-threshold patients. According to the blood neutrophil counts using biostatistics analyses, two distinct clinical phenotypes of stable patients with non-CF bronchiectasis were defined. Patients falling into the above-threshold cluster were more severe. Severity was characterized by a significantly impaired lung function parameters and nutritional status, and greater systemic inflammation. Phenotypic profiles of bronchiectasis patients are well defined as a result of the cluster analysis of combined systemic and respiratory variables.

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.

We hypothesized that systemic inflammatory and nutritional parameters may differ between male and female patients with non-CF bronchiectasis. In a large patient cohort from the Spanish Online Bronchiectasis Registry (RIBRON), clinical features, systemic inflammatory and nutritional parameters were analyzed in male and female patients with bronchiectasis. Lung function, disease severity using several scores, nutritional status, systemic inflammatory parameters, and multivariate regression analyses were performed to identify differences between male and female patients in the target variables. The number of female patients included in the registry was greater than male patients and they had a less severe disease as measured by all three indices of disease severity, a lower degree of airway obstruction, worse diffusion capacity and airway trapping, better nutritional parameters, and lower levels of inflammatory biomarkers. Multivariate regression analysis evidenced that strong relationships were found between female gender and the following variables: total numbers of leukocytes and neutrophils, hemoglobin, hematocrit, creatinine, and body mass index (BMI). Multivariate regression analyses evidenced that nutritional parameters and inflammatory biomarkers may be reliable indicators of gender-related differences in patients with non-CF bronchiectasis. These findings deserve further attention in follow-up investigations in which the potential predictive value of those biomarkers should be thoroughly explored.