Chronic Obstructive Pulmonary Disease (COPD) is a highly heterogeneous condition projected to become the third leading cause of death worldwide by 2030. To better characterize this condition, clinicians have classified patients sharing certain symptomatic characteristics, such as symptom intensity and history of exacerbations, into distinct phenotypes. In recent years, the growing use of machine learning algorithms, and cluster analysis in particular, has promised to advance this classification through the integration of additional patient characteristics, including comorbidities, biomarkers, and genomic information. This combination would allow researchers to more reliably identify new COPD phenotypes, as well as better characterize existing ones, with the aim of improving diagnosis and developing novel treatments. Here, we systematically review the last decade of research progress, which uses cluster analysis to identify COPD phenotypes. Collectively, we provide a systematized account of the extant evidence, describe the strengths and weaknesses of the main methods used, identify gaps in the literature, and suggest recommendations for future research.

慢性阻塞性肺疾病（COPD）是一种高度异质性疾病，预计到2030年将成为全球第三大死亡原因。为了更好地表征这种疾病，临床医生将具有某些症状特征（例如症状强度和急性发作史）的患者分类为不同的表型。近年来，越来越多地使用机器学习算法，尤其是聚类分析，已有望通过整合其他患者特征（包括合并症，生物标志物和基因组信息）来促进这种分类。这种组合将使研究人员能够更可靠地识别新的COPD表型，并更好地表征现有的COPD表型，以改善诊断和开发新的治疗方法。这里，我们系统地回顾了最近十年的研究进展，该研究使用聚类分析来识别COPD表型。我们集体提供了现有证据的系统化描述，描述了所用主要方法的优缺点，找出了文献中的空白，并为以后的研究提供了建议。