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Methodology for Sputum Induction and Laboratory Processing

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Published on: December 17, 2017

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

Xuejie Wang^1,2, Carmen Villa³, Yadira Dobarganes³

¹Lung Cancer and Muscle Research Group, Pulmonology Department, Hospital del Mar-IMIM, Parc de Salut Mar, PRBB, C/Dr. Aiguader, 88, 08003 Barcelona, Spain.

|February 25, 2022

Summary

Data mining identified three distinct patient groups in bronchiectasis, with one severe cluster showing worse lung function and higher exacerbation rates. This aids in personalized bronchiectasis management.

Keywords:

C reactive protein blood neutrophil clinical outcomes disease severity scores eosinophil hemoglobin hierarchical clustering lymphocyte counts multivariate analyses non-cystic fibrosis bronchiectasis phenotypic clusters

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Bronchoalveolar Lavage of Murine Lungs to Analyze Inflammatory Cell Infiltration

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Published on: May 4, 2017

Area of Science:

Pulmonology
Biostatistics
Data Science

Background:

Bronchiectasis is a complex lung disease with significant heterogeneity.
Understanding patient phenotypes is crucial for effective management and treatment strategies.

Purpose of the Study:

To identify distinct patient phenotypes in bronchiectasis using data mining techniques.
To correlate these phenotypes with clinical characteristics, biomarkers, and disease severity.

Main Methods:

Utilized hierarchical clustering (scikit-learn) on systemic biomarkers (neutrophils, eosinophils, lymphocytes, CRP, hemoglobin) from 1092 patients.
Assessed disease severity using scores like FACED, EFACED, and Bronchiectasis Severity Index (BSI).
Employed multivariate ordinal logistic regression for confirmation.

Main Results:

Three clusters (mild, moderate, severe) were identified based on biomarkers.
The severe cluster (Cluster #3) exhibited significantly higher disease severity scores (FEV1, age, colonization, extension, dyspnea, exacerbations, BSI).
Cluster #3 also showed increased exacerbations, hospitalizations, Charlson index, inflammatory markers, Pseudomonas aeruginosa colonization, and COPD prevalence, with reduced airflow and diffusion capacity.

Conclusions:

Clustering analysis effectively characterizes bronchiectasis patient heterogeneity.
Identified phenotypes have significant clinical implications for managing complex bronchiectasis cases.
Findings support tailored management strategies based on distinct patient phenotypes.