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. 2022 Jul 22:9:895836.
doi: 10.3389/fcvm.2022.895836. eCollection 2022.

Identifying novel subgroups in heart failure patients with unsupervised machine learning: A scoping review

Jin Sun  1 , Hua Guo  1 , Wenjun Wang  2   3 , Xiao Wang  2   3 , Junyu Ding  1 , Kunlun He  2   3 , Xizhou Guan  4
Affiliations

Identifying novel subgroups in heart failure patients with unsupervised machine learning: A scoping review

Jin Sun et al. Front Cardiovasc Med. .

Abstract

Background: Heart failure is currently divided into three main forms, HFrEF, HFpEF, and HFmrEF, but its etiology is diverse and highly heterogeneous. Many studies reported a variety of novel subgroups in heart failure patients, with unsupervised machine learning methods. The aim of this scoping review is to provide insights into how these techniques can diagnose and manage HF faster and better, thus providing direction for future research and facilitating its routine use in clinical practice.

Methods: The review was performed following PRISMA-SCR guideline. We searched the PubMed database for eligible publications. Studies were included if they defined new subgroups in HF patients using clustering analysis methods, and excluded if they are (1) Reviews, commentary, or editorials, (2) Studies not about defining new sub-types, or (3) Studies not using unsupervised algorithms. All study screening and data extraction were conducted independently by two investigators and narrative integration of data extracted from included studies was performed.

Results: Of the 498 studies identified, 47 were included in the analysis. Most studies (61.7%) were published in 2020 and later. The largest number of studies (46.8%) coming from the United States, and most of the studies were authored and included in the same country. The most commonly used machine learning method was hierarchical cluster analysis (46.8%), the most commonly used cluster variable type was comorbidity (61.7%), and the least used cluster variable type was genomics (12.8%). Most of the studies used data sets of less than 500 patients (48.9%), and the sample size had negative correlation with the number of clustering variables. The majority of studies (85.1%) assessed the association between cluster grouping and at least one outcomes, with death and hospitalization being the most commonly used outcome measures.

Conclusion: This scoping review provides an overview of recent studies proposing novel HF subgroups based on clustering analysis. Differences were found in study design, study population, clustering methods and variables, and outcomes of interests, and we provided insights into how these studies were conducted and identify the knowledge gaps to guide future research.

Keywords: clustering analysis; heart failure; machine learning; scoping review; subtype.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
PRISMA flow diagram for study inclusion.
FIGURE 2
FIGURE 2
Number of publication per year by HF sub-types.
FIGURE 3
FIGURE 3
Relationship between data sources and corresponding authors.
FIGURE 4
FIGURE 4
Types of machine learning methods used in identifying HF subgroups.
FIGURE 5
FIGURE 5
Types of clustering variables used in identifying HF subgroups.
FIGURE 6
FIGURE 6
Features of the clusters identified in the included studies.
See this image and copyright information in PMC

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