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Clustering based on comorbidities in patients with chronic heart failure: an illustration of clinical diversity
AIMS: It 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 influ...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8787997/ https://www.ncbi.nlm.nih.gov/pubmed/34796690 http://dx.doi.org/10.1002/ehf2.13704 |
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author | Uszko‐Lencer, Nicole H.M.K. Janssen, Daisy J.A. Gaffron, Swetlana Vanfleteren, Lowie E.G.W. Janssen, Eefje Werter, Christ Franssen, Frits M.E. Wouters, Emiel F.M. Rechberger, Simon Brunner La Rocca, Hans‐Peter Spruit, Martijn A. |
author_facet | Uszko‐Lencer, Nicole H.M.K. Janssen, Daisy J.A. Gaffron, Swetlana Vanfleteren, Lowie E.G.W. Janssen, Eefje Werter, Christ Franssen, Frits M.E. Wouters, Emiel F.M. Rechberger, Simon Brunner La Rocca, Hans‐Peter Spruit, Martijn A. |
author_sort | Uszko‐Lencer, Nicole H.M.K. |
collection | PubMed |
description | AIMS: It 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 RESULTS: A 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. CONCLUSIONS: Distinct 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. |
format | Online Article Text |
id | pubmed-8787997 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-87879972022-01-31 Clustering based on comorbidities in patients with chronic heart failure: an illustration of clinical diversity Uszko‐Lencer, Nicole H.M.K. Janssen, Daisy J.A. Gaffron, Swetlana Vanfleteren, Lowie E.G.W. Janssen, Eefje Werter, Christ Franssen, Frits M.E. Wouters, Emiel F.M. Rechberger, Simon Brunner La Rocca, Hans‐Peter Spruit, Martijn A. ESC Heart Fail Original Articles AIMS: It 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 RESULTS: A 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. CONCLUSIONS: Distinct 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. John Wiley and Sons Inc. 2021-11-18 /pmc/articles/PMC8787997/ /pubmed/34796690 http://dx.doi.org/10.1002/ehf2.13704 Text en © 2021 The Authors. ESC Heart Failure published by John Wiley & Sons Ltd on behalf of European Society of Cardiology. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. |
spellingShingle | Original Articles Uszko‐Lencer, Nicole H.M.K. Janssen, Daisy J.A. Gaffron, Swetlana Vanfleteren, Lowie E.G.W. Janssen, Eefje Werter, Christ Franssen, Frits M.E. Wouters, Emiel F.M. Rechberger, Simon Brunner La Rocca, Hans‐Peter Spruit, Martijn A. Clustering based on comorbidities in patients with chronic heart failure: an illustration of clinical diversity |
title | Clustering based on comorbidities in patients with chronic heart failure: an illustration of clinical diversity |
title_full | Clustering based on comorbidities in patients with chronic heart failure: an illustration of clinical diversity |
title_fullStr | Clustering based on comorbidities in patients with chronic heart failure: an illustration of clinical diversity |
title_full_unstemmed | Clustering based on comorbidities in patients with chronic heart failure: an illustration of clinical diversity |
title_short | Clustering based on comorbidities in patients with chronic heart failure: an illustration of clinical diversity |
title_sort | clustering based on comorbidities in patients with chronic heart failure: an illustration of clinical diversity |
topic | Original Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8787997/ https://www.ncbi.nlm.nih.gov/pubmed/34796690 http://dx.doi.org/10.1002/ehf2.13704 |
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