Mekov Evgeni V, Yanev Nikolay A, Kurtelova Nedelina, Mihalova Teodora, Tsakova Adelina, Yamakova Yordanka, Petkov Rosen E
Department of Pulmonary Diseases, Medical University-Sofia, Sofia, BGR.
Central Clinical Laboratory, Medical University-Sofia, Alexandrovska University Hospital, Sofia, BGR.
Cureus. 2025 Apr 22;17(4):e82811. doi: 10.7759/cureus.82811. eCollection 2025 Apr.
Introduction Chronic obstructive pulmonary disease (COPD) is a heterogeneous condition with varied clinical presentations and prognoses. Identifying patient phenotypes is essential for developing personalized treatment strategies. Principal component analysis (PCA) is a statistical method that can be employed to uncover clinical clusters and gain insight into the relationships among different disease characteristics. This study aims to analyze COPD patient phenotypes using PCA and to identify the key clinical features influencing their distribution. Materials and methods This was a prospective, observational outpatient study involving 96 patients diagnosed with COPD. Data collected included demographic, clinical, spirometric, echocardiographic, laboratory, and functional parameters. PCA was applied to reduce data dimensionality and to identify the principal components underlying phenotype structure. Results The first two principal components accounted for 62% of the total variance, underscoring the clinical heterogeneity of COPD. Visualization of the PCA revealed four distinct clusters that align with recognized COPD phenotypes: chronic bronchitis, emphysema, COPD with asthmatic features (previously referred to as asthma-COPD overlap), and the non-exacerbator type. Each cluster was associated with specific clinical characteristics. Conclusions PCA enabled the identification of four distinct clinical clusters among COPD patients: bronchitis, emphysema, COPD with asthmatic features, and non-exacerbator. This approach helps clarify the relationship between clinical characteristics and supports a more personalized approach to treatment.
引言 慢性阻塞性肺疾病（COPD）是一种具有多种临床表现和预后的异质性疾病。识别患者表型对于制定个性化治疗策略至关重要。主成分分析（PCA）是一种统计方法，可用于揭示临床聚类并深入了解不同疾病特征之间的关系。本研究旨在使用PCA分析COPD患者表型，并确定影响其分布的关键临床特征。材料和方法 这是一项前瞻性观察性门诊研究，纳入了96例诊断为COPD的患者。收集的数据包括人口统计学、临床、肺功能、超声心动图、实验室和功能参数。应用PCA降低数据维度，并确定表型结构的主要成分。结果 前两个主成分占总方差的62%，突出了COPD的临床异质性。PCA可视化显示了四个不同的聚类，与公认的COPD表型一致：慢性支气管炎、肺气肿、具有哮喘特征的COPD（以前称为哮喘-COPD重叠）和非加重型。每个聚类都与特定的临床特征相关。结论 PCA能够识别COPD患者中的四个不同临床聚类：支气管炎、肺气肿、具有哮喘特征的COPD和非加重型。这种方法有助于阐明临床特征之间的关系，并支持更个性化的治疗方法。
