ArticleESC heart failure2022
Clustering based on comorbidities in patients with chronic heart failure: an illustration of clinical diversity.
Article in ESC heart failure, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Discovering Distinct Phenotypical Clusters in Heart Failure Across the Ejection Fraction Spectrum: a Systematic Review.Current heart failure reports · 2023Pooled it
- Comorbidity Clusters in Acute Heart Failure: Insights From the AMERICCAASS Registry.Journal of the American Heart Association · 2026Observational
- Detecting multimorbidity patterns in Alzheimer's disease using unsupervised machine learning: A nationwide emergency department study (2007-2022).Journal of Alzheimer's disease : JAD · 2026Article
- Development and validation of risk stratification and shared decision-making tool for catheter ablation for atrial fibrillation in patients with heart failure: a multicentre cohort study.EClinicalMedicine · 2025Article
- Disease Network-Based Approaches to Study Comorbidity in Heart Failure: Current State and Future Perspectives.Current heart failure reports · 2024Review
- Common non-cardiovascular multimorbidity groupings and clinical outcomes in older adults with major cardiovascular disease.Journal of the American Geriatrics Society · 2023Article
- Phenotyping left ventricular systolic dysfunction in asymptomatic individuals for improved risk stratification.European heart journal. Cardiovascular Imaging · 2023Article
- Machine Learning Identifies New Predictors on Restenosis Risk after Coronary Artery Stenting in 10,004 Patients with Surveillance Angiography.Journal of clinical medicine · 2023Article
- Clustering based on comorbidities in patients with chronic heart failure: an illustration of clinical diversity.ESC heart failure · 2022Article
- Identifying novel subgroups in heart failure patients with unsupervised machine learning: A scoping review.Frontiers in cardiovascular medicine · 2022Article
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimsIt is increasingly recognized that the presence of comorbidities substantially contributes to the disease burden in patients with heart failure (HF). Several reports have suggested that clustering of comorbidities can lead to improved characterization of the disease phenotypes, which may influence management of the individual patient. Therefore, we aimed to cluster patients with HF based on medical comorbidities and their treatment and, subsequently, compare the clinical characteristics between these clusters. METHODS AND
resultsA total of 603 patients with HF entering an outpatient HF rehabilitation programme were included [median age 65 years (interquartile range 56-71), 57% ischaemic origin of cardiomyopathy, and left ventricular ejection fraction 35% (26-45)]. Exercise performance, daily life activities, disease-specific health status, coping styles, and personality traits were assessed. In addition, the presence of 12 clinically relevant comorbidities was recorded, based on targeted diagnostics combined with applicable pharmacotherapies. Self-organizing maps (SOMs; www.viscovery.net) were used to visualize clusters, generated by using a hybrid algorithm that applies the classical hierarchical cluster method of Ward on top of the SOM topology. Five clusters were identified: (1) a least comorbidities cluster; (2) a cachectic/implosive cluster; (3) a metabolic diabetes cluster; (4) a metabolic renal cluster; and (5) a psychologic cluster. Exercise performance, daily life activities, disease-specific health status, coping styles, personality traits, and number of comorbidities were significantly different between these clusters.
conclusionsDistinct combinations of comorbidities could be identified in patients with HF. Therapy may be tailored based on these clusters as next step towards precision medicine. The effect of such an approach needs to be prospectively tested.
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