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. 2025 Feb 1:420:132748.
doi: 10.1016/j.ijcard.2024.132748. Epub 2024 Nov 23.

A systematic review of multimorbidity clusters in heart failure: Effects of methodologies

Palvinder Kaur  1 , Joey Ha  2 , Natalie Raye  3 , Wouter Ouwerkerk  4 , Bart J van Essen  5 , Laurence Tan  6 , Chong Keat Tan  7 , Allyn Hum  8 , Alex R Cook  9 , Jasper Tromp  10
Affiliations

A systematic review of multimorbidity clusters in heart failure: Effects of methodologies

Palvinder Kaur et al. Int J Cardiol. .

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusion: 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.

Keywords: Cluster; Cluster analysis; Framework; Heart failure; Multimorbidity.

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Conflict of interest statement

Declaration of competing interest JT is supported by the National University of Singapore Start-up grant, the tier 1 grant from the Ministry of Education and the CS-IRG New Investigator Grant from the National Medical Research Council; has received research support from AstraZeneca and consulting or speaker fees from Daiichi-Sankyo, Boehringer Ingelheim, Roche diagnostics and Us2.ai, and owns patent US-10702247-B2 unrelated to the present work. The remaining authors declare that they have no conflict of interest. All authors take responsibility for all aspects of the reliability and freedom from bias of the data presented and their discussed interpretation.

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