Kaur Palvinder, Ha Joey, Raye Natalie, Ouwerkerk Wouter, van Essen Bart J, Tan Laurence, Tan Chong Keat, Hum Allyn, Cook Alex R, Tromp Jasper
Health Services and Outcomes Research, National Healthcare Group, Singapore; Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore.
Health Services and Outcomes Research, National Healthcare Group, Singapore.
Int J Cardiol. 2025 Feb 1;420:132748. doi: 10.1016/j.ijcard.2024.132748. Epub 2024 Nov 23.
Clustering algorithms can identify distinct heart failure (HF) subgroups. The choice of algorithms, modelling process, and input variables can impact clustering outcomes. Therefore, we reviewed analytical methods and variables used in studies that performed clustering in patients with HF.
We systematically searched CINAHL, COCHRANE, EMBASE, OVID Medline, and Web of Science for eligible articles between inception and April 2023. We included primary studies that identified distinct HF multimorbid subgroups and were appraised for risk-of-bias and against methodological recommendations for cluster analysis. A narrative synthesis was performed.
Our analysis included 43 studies, mostly following a cohort design (n = 34, 79 %) and conducted primarily in Europe (n = 15, 35 %) and North America (n = 13, 30 %). Model-based (n = 22, 48 %), centre-based (n = 10, 22 %), and hierarchical class clustering (n = 9, 20 %) were the most frequently employed algorithms, identifying a range of 2-10 multimorbid clusters. Most studies used a combination of multi-modal parameters (i.e., socio-demographics, biochemistry, clinical characteristics, comorbidities and risk factors, cardiac imaging, and biomarkers) (n = 27, 63 %), followed by disease-based parameters (i.e., comorbidities and risk factors) (n = 11, 26 %) as input variables for clustering. Notably, variables used for clustering reflected cardiovascular and metabolic conditions. The phenogroups identified differed by input variables and algorithms used for clustering. We found substantial quality gaps in developing clustering models, variable selection, reporting of modelling processes, and model validation.
Cluster analysis results differed based on the clustering algorithms used and input variables. This review found substantial gaps in analysis quality and reporting. Implementing a methodological framework to develop, validate, and report clustering analysis can improve the clinical utility and reproducibility of clustering outcomes.
聚类算法可识别不同的心衰（HF）亚组。算法的选择、建模过程和输入变量会影响聚类结果。因此，我们回顾了在HF患者中进行聚类分析的研究中所使用的分析方法和变量。
我们系统检索了CINAHL、COCHRANE、EMBASE、OVID Medline和Web of Science数据库，以查找从建库至2023年4月期间的符合条件的文章。我们纳入了识别出不同HF共病亚组的原发性研究，并对其进行了偏倚风险评估以及对照聚类分析的方法学建议进行评估。进行了叙述性综合分析。
我们的分析纳入了43项研究，大多数采用队列设计（n = 34，79%），主要在欧洲（n = 15，35%）和北美（n = 13，30%）开展。基于模型的聚类算法（n = 22，48%）、基于中心的聚类算法（n = 10，22%）和层次分类聚类算法（n = 9，20%）是最常使用的算法，识别出2 - 10个共病聚类。大多数研究使用多模式参数组合（即社会人口统计学、生物化学、临床特征、共病和危险因素、心脏成像和生物标志物）作为聚类的输入变量（n = 27，63%），其次是基于疾病的参数（即共病和危险因素）（n = 11，26%）。值得注意的是，用于聚类的变量反映了心血管和代谢状况。所识别的表型组因用于聚类的输入变量和算法而异。我们发现在聚类模型开发、变量选择、建模过程报告和模型验证方面存在较大质量差距。
聚类分析结果因所使用的聚类算法和输入变量而异。本综述发现分析质量和报告方面存在较大差距。实施一个用于开发、验证和报告聚类分析的方法框架可以提高聚类结果的临床实用性和可重复性。
