Symptom phenotyping in people with cystic fibrosis during acute pulmonary exacerbations using machine-learning K-means clustering analysis

利用机器学习K均值聚类分析对囊性纤维化患者急性肺部加重期症状进行表型分析

阅读:3

Abstract

INTRODUCTION: People with cystic fibrosis (PwCF) experience frequent symptoms associated with chronic lung disease. A complication of CF is a pulmonary exacerbation (PEx), which is often preceded by an increase in symptoms and a decline in lung function. A symptom cluster is when two or more symptoms co-occur and are related; symptom clusters have contributed meaningful knowledge in other diseases. The purpose of this study is to discover symptom clustering patterns in PwCF during a PEx to illuminate symptom phenotypes and assess differences in recovery from PExs. METHODS: This study was a secondary, longitudinal analysis (N = 72). Participants at least 10 years of age and being treated with intravenous antibiotics for a CF PEx were enrolled in the United States. Symptoms were collected on treatment days 1-21 using the CF Respiratory Symptom Diary (CFRSD)-Chronic Respiratory Symptom Score (CRISS). K-means clustering was computed on day 1 symptom data to detect clustering patterns. Linear regression and multi-level growth models were performed. RESULTS: Symptoms significantly clustered based on severity: low symptom (LS)-phenotype (n = 42), high symptom (HS)-phenotype (n = 30). HS-phenotype had worse symptoms and CRISS scores (p< 0.01) than LS-phenotype. HS-phenotype was associated with spending 5 more nights in the hospital annually (p< 0.01) than LS-phenotype. HS-phenotype had worse symptoms over 21 days than LS-phenotype (p< 0.0001). CONCLUSION: Symptoms significantly cluster on day 1 of a CF-PEx. PwCF with HS-phenotype spend more nights in the hospital and are less likely to experience the same resolution in symptoms by the end of PEx treatment than LS-phenotype.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。