Systemic Inflammatory Biomarkers Define Specific Clusters in Patients with Bronchiectasis: A Large-Cohort Study

Abstract

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.

Chronic Obstructive Pulmonary Disease: The Present and Future

“…Frequent exacerbator LABA, LAMA, ICS, roflumilast, macrolides Optimisation of comorbid physical and mental health conditions [27] Chronic bronchitis Roflumilast, mucolytics Use of CFTR modulators [13] Emphysema Lung volume reduction surgery Correction of miR overexpression [8] Type 1 respiratory failure Long-term oxygen therapy Increased vigilance for VTE in acute illness [29] Type 2 respiratory failure Domiciliary NIV Consideration of comorbidities such as OSA/ORRF [21] Eosinophilic COPD Steroids Identification of distinct microbiome in eosinophil-predominant COPD [15] Investigation of immunomodulatory alternatives to steroids [30] Bronchiectasis Targeted antibiotics, chest physiotherapy Identify severity clusters using biomarkers, to stratify follow-up and hospitalisation [33] α-1 antitrypsin deficiency LABA, LAMA, ICS α-1 antitrypsin augmentation therapy [17] Subgroups requiring further study Biomass and pollutant COPD Removal of pollutant exposure Use of predictive machine-learning to target individuals at greatest risk of pollutant-induced emphysema [31] Premalignant COPD Smoking cessation…”

“…This study is an excellent example of supervised machine learning algorithms analysing large datasets to develop predictive models. Similarly, Wang et al performed data mining to identify distinct phenotypic clusters in bronchiectasis patients, demonstrating the use of clustering analysis to identify distinct endotypes in complex heterogeneous disease [ 33 ]. These methods have public health implications for personalised lifestyle interventions and an enhanced diagnosis.…”

Blood Neutrophil Counts Define Specific Clusters of Bronchiectasis Patients: A Hint to Differential Clinical Phenotypes

“…Specific phenotypes have been defined on the basis of the eosinophil counts in a large cohort of patients with bronchiectasis [12]. In another investigation [13], several blood parameters allowed for the identification of three different clinical phenotypes in bronchiectasis patients in another large cohort of patients. In both investigations, disease severity, lung function, and systemic inflammatory and nutritional parameters clearly differed among the clusters of patients obtained from the biostatistical approach used in each type of investigation [12,13].…”