Surrey Business School, University of Surrey, Guildford, GU2 7HX, UK.
Surrey Business School, University of Surrey, Guildford, GU2 7HX, UK; The Organizational Neuroscience Laboratory, London, WC1N 3AX, UK.
Respir Med. 2020 Sep;171:106093. doi: 10.1016/j.rmed.2020.106093. Epub 2020 Jul 28.
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）是一种高度异质性的疾病，预计到 2030 年将成为全球第三大致死原因。为了更好地描述这种疾病，临床医生根据某些症状特征（如症状强度和加重史）将具有相似特征的患者分为不同的表型。近年来，机器学习算法的应用日益广泛，尤其是聚类分析，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）来推进这种分类。这种组合将使研究人员能够更可靠地识别新的 COPD 表型，并更好地描述现有的表型，从而改善诊断并开发新的治疗方法。在这里，我们系统地回顾了过去十年使用聚类分析来识别 COPD 表型的研究进展。总的来说，我们提供了一个对现有证据的系统描述，描述了主要方法的优缺点，确定了文献中的空白，并提出了对未来研究的建议。
