Key result
Unsupervised machine learning identified three distinct heart failure phenotypes (uraemic, hypotensive, congestive) with a silhouette score of 0.72, driven by blood pressure, age, and LVEF.
Why the study?
Heart failure diagnosis and therapy are mainly guided by LVEF alone, prompting the use of unsupervised machine learning to phenotype patients across multiple clinical parameters.
Observational (n=636)
Unsupervised machine learning can identify distinct heart failure phenotypes based on routine clinical parameters like blood pressure and age, potentially aiding risk stratification in resource-limited settings without echocardiography.
ML-derived HF clusters may refine phenotyping beyond LVEF; leaves open prognostic or therapeutic utility pending validation.
Background: The diagnosis and therapy of heart failure are guided mainly by a single imaging parameter, the left ventricular ejection fraction (LVEF). Recent studies have reported on the value of machine learning in characterising the various phenotypes of heart failure patients. Therefore, this study aims to use unsupervised machine learning algorithms to phenotype heart failure patients into different clusters using multiple clinical parameters. Methods: Seven unsupervised machine learning clustering algorithms were used to cluster heart failure patients hospitalised with acute and chronic heart failure. Results: The agglomerative clustering algorithm identified three clusters with a silhouette score of 0.72. Cluster 1 (uraemic cluster) comprised 229 (36.0%) patients with a mean age of 56.2 ± 17.2 years and a serum urea of 14.5 ± 31.3 mmol/L. Cluster 2 (hypotensive cluster) comprised 117 (18.4%) patients with a minimum systolic and diastolic blood pressure of 91 and 60 mmHg, respectively. In cluster 3 (congestive cluster), patients predominantly had symptoms of fluid overload, and 93 (64.6%) patients had ascites. Among the 636 heart failure patients studied, the median LVEF was 32% (interquartile range: 25–45), and the rate of in-hospital all-cause mortality was 14.5%. Systolic and diastolic blood pressure, age, and the LVEF had the most substantial impact on discriminating between the three clusters. Conclusions: Clinicians without access to echocardiography could potentially rely on blood pressure measurements and age to risk stratify heart failure patients. However, larger prospective studies are mandatory for the validation of these clinical parameters.
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Mpanya et al. (2023) conducted an observational in Heart failure (n=636). Unsupervised machine learning clustering algorithms was evaluated on Identification of heart failure phenotypes/clusters. Unsupervised machine learning identified three distinct heart failure phenotypes (uraemic, hypotensive, congestive) with a silhouette score of 0.72, driven by blood pressure, age, and LVEF.
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