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Obstructive Lung Diseases: COPD Phenotypes |

Exploratory Study of the Clinical Phenotype of Airways Disease by Different Cluster Analysis Methods FREE TO VIEW

Pu Ning, MA; Yanfei Guo, PhD
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Beijing Hospital, Beijing, China


Copyright 2016, American College of Chest Physicians. All Rights Reserved.


Chest. 2016;149(4_S):A395. doi:10.1016/j.chest.2016.02.410
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SESSION TITLE: COPD Phenotypes

SESSION TYPE: Original Investigation Poster

PRESENTED ON: Saturday, April 16, 2016 at 11:45 AM - 12:45 PM

PURPOSE: To explore the clinical phenotype of airways disease by hierarchical cluster analysis and two-step cluster analysis.

METHODS: A population sample of adults who had wheeze within the last 12 months underwent detailed investigation, including a clinical questionnaire, pulmonary function tests, blood tests (total serum IgE levels and blood eosinophil level) and a peak flow diary. Cluster analysis was carried out on the data set with the subjects defined as described using the following nine variables: 1) pre-bronchodilator FEV1/FVC ratio expressed as a percentage; 2) pre-bronchodilator FEV1 expressed as a percentage of the predicted value; 3) post-bronchodilator change in FEV1, expressed as percentage from baseline; 4) residual capacity expressed as a percentage of the predicted value; 5) diffusing capacity of the lung for carbon monoxide/alveolar volume adjusted for haemoglobin level and expressed as a percentage of the predicted value; 6) the serum IgE level; 7) the variability of PEF; 8) respiratory symptoms; 9) cumulative tobacco cigarette consumption.

RESULTS: Subjects with a complete dataset (n=121) were included in the cluster analysis. Four clusters were identified with the following characteristics by hierarchical cluster analysis: cluster 1: chronic bronchitis in smokers whose pulmonary function is normal; cluster 2: chronic bronchitis or mild chronic obstructive pulmonary disease patients with mild airflow limitation; cluster 3:chronic obstructive pulmonary disease patients with heavy smoking, poor quality of life and severe airflow limitation; cluster 4: atopic patients with mild airflow limitation, serum IgE increased significantly and showing the features of asthma. Moreover, four clusters also identified by two-step cluster analysis as follows: cluster 1: chronic obstructive pulmonary disease patients with moderate to severe airflow limitation; cluster 2: asthma-chronic obstructive pulmonary disease overlap syndrome patients with heavy smoking, airflow limitation and increased airways reversibility; cluster 3: patients who had a small amount of smoking and normal pulmonary function with wheezing but no chronic cough; cluster 4: chronic bronchitis patients with normal pulmonary function but had chronic cough.

CONCLUSIONS: By different cluster analyisis, we can identify distinct clinical phenotypes of airways disease.

CLINICAL IMPLICATIONS: Our study identified distinct clinical phenotypes of airways disease by different cluster analysis. And these phenotype may provide guidance for clinical treatment.

DISCLOSURE: The following authors have nothing to disclose: Pu Ning, Yanfei Guo

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