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Identification of distinct phenotypic clusters in heart failure with preserved ejection fraction

AIMS: We aimed to derive and validate clinically useful clusters of patients with heart failure with preserved ejection fraction (HFpEF; left ventricular ejection fraction ≥50%). METHODS AND RESULTS: We derived a cluster model from 6909 HFpEF patients from the Swedish Heart Failure Registry (SwedeHF...

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Autores principales: Uijl, Alicia, Savarese, Gianluigi, Vaartjes, Ilonca, Dahlström, Ulf, Brugts, Jasper J., Linssen, Gerard C.M., van Empel, Vanessa, Brunner‐La Rocca, Hans‐Peter, Asselbergs, Folkert W., Lund, Lars H., Hoes, Arno W., Koudstaal, Stefan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley & Sons, Ltd. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8359985/
https://www.ncbi.nlm.nih.gov/pubmed/33779119
http://dx.doi.org/10.1002/ejhf.2169
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author Uijl, Alicia
Savarese, Gianluigi
Vaartjes, Ilonca
Dahlström, Ulf
Brugts, Jasper J.
Linssen, Gerard C.M.
van Empel, Vanessa
Brunner‐La Rocca, Hans‐Peter
Asselbergs, Folkert W.
Lund, Lars H.
Hoes, Arno W.
Koudstaal, Stefan
author_facet Uijl, Alicia
Savarese, Gianluigi
Vaartjes, Ilonca
Dahlström, Ulf
Brugts, Jasper J.
Linssen, Gerard C.M.
van Empel, Vanessa
Brunner‐La Rocca, Hans‐Peter
Asselbergs, Folkert W.
Lund, Lars H.
Hoes, Arno W.
Koudstaal, Stefan
author_sort Uijl, Alicia
collection PubMed
description AIMS: We aimed to derive and validate clinically useful clusters of patients with heart failure with preserved ejection fraction (HFpEF; left ventricular ejection fraction ≥50%). METHODS AND RESULTS: We derived a cluster model from 6909 HFpEF patients from the Swedish Heart Failure Registry (SwedeHF) and externally validated this in 2153 patients from the Chronic Heart Failure ESC‐guideline based Cardiology practice Quality project (CHECK‐HF) registry. In SwedeHF, the median age was 80 [interquartile range 72–86] years, 52% of patients were female and most frequent comorbidities were hypertension (82%), atrial fibrillation (68%), and ischaemic heart disease (48%). Latent class analysis identified five distinct clusters: cluster 1 (10% of patients) were young patients with a low comorbidity burden and the highest proportion of implantable devices; cluster 2 (30%) patients had atrial fibrillation, hypertension without diabetes; cluster 3 (25%) patients were the oldest with many cardiovascular comorbidities and hypertension; cluster 4 (15%) patients had obesity, diabetes and hypertension; and cluster 5 (20%) patients were older with ischaemic heart disease, hypertension and renal failure and were most frequently prescribed diuretics. The clusters were reproduced in the CHECK‐HF cohort. Patients in cluster 1 had the best prognosis, while patients in clusters 3 and 5 had the worst age‐ and sex‐adjusted prognosis. CONCLUSIONS: Five distinct clusters of HFpEF patients were identified that differed in clinical characteristics, heart failure drug therapy and prognosis. These results confirm the heterogeneity of HFpEF and form a basis for tailoring trial design to individualized drug therapy in HFpEF patients.
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spelling pubmed-83599852021-08-17 Identification of distinct phenotypic clusters in heart failure with preserved ejection fraction Uijl, Alicia Savarese, Gianluigi Vaartjes, Ilonca Dahlström, Ulf Brugts, Jasper J. Linssen, Gerard C.M. van Empel, Vanessa Brunner‐La Rocca, Hans‐Peter Asselbergs, Folkert W. Lund, Lars H. Hoes, Arno W. Koudstaal, Stefan Eur J Heart Fail HFpEF AND MACHINE LEARNING AIMS: We aimed to derive and validate clinically useful clusters of patients with heart failure with preserved ejection fraction (HFpEF; left ventricular ejection fraction ≥50%). METHODS AND RESULTS: We derived a cluster model from 6909 HFpEF patients from the Swedish Heart Failure Registry (SwedeHF) and externally validated this in 2153 patients from the Chronic Heart Failure ESC‐guideline based Cardiology practice Quality project (CHECK‐HF) registry. In SwedeHF, the median age was 80 [interquartile range 72–86] years, 52% of patients were female and most frequent comorbidities were hypertension (82%), atrial fibrillation (68%), and ischaemic heart disease (48%). Latent class analysis identified five distinct clusters: cluster 1 (10% of patients) were young patients with a low comorbidity burden and the highest proportion of implantable devices; cluster 2 (30%) patients had atrial fibrillation, hypertension without diabetes; cluster 3 (25%) patients were the oldest with many cardiovascular comorbidities and hypertension; cluster 4 (15%) patients had obesity, diabetes and hypertension; and cluster 5 (20%) patients were older with ischaemic heart disease, hypertension and renal failure and were most frequently prescribed diuretics. The clusters were reproduced in the CHECK‐HF cohort. Patients in cluster 1 had the best prognosis, while patients in clusters 3 and 5 had the worst age‐ and sex‐adjusted prognosis. CONCLUSIONS: Five distinct clusters of HFpEF patients were identified that differed in clinical characteristics, heart failure drug therapy and prognosis. These results confirm the heterogeneity of HFpEF and form a basis for tailoring trial design to individualized drug therapy in HFpEF patients. John Wiley & Sons, Ltd. 2021-05-01 2021-06 /pmc/articles/PMC8359985/ /pubmed/33779119 http://dx.doi.org/10.1002/ejhf.2169 Text en © 2021 The Authors. European Journal of 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 HFpEF AND MACHINE LEARNING
Uijl, Alicia
Savarese, Gianluigi
Vaartjes, Ilonca
Dahlström, Ulf
Brugts, Jasper J.
Linssen, Gerard C.M.
van Empel, Vanessa
Brunner‐La Rocca, Hans‐Peter
Asselbergs, Folkert W.
Lund, Lars H.
Hoes, Arno W.
Koudstaal, Stefan
Identification of distinct phenotypic clusters in heart failure with preserved ejection fraction
title Identification of distinct phenotypic clusters in heart failure with preserved ejection fraction
title_full Identification of distinct phenotypic clusters in heart failure with preserved ejection fraction
title_fullStr Identification of distinct phenotypic clusters in heart failure with preserved ejection fraction
title_full_unstemmed Identification of distinct phenotypic clusters in heart failure with preserved ejection fraction
title_short Identification of distinct phenotypic clusters in heart failure with preserved ejection fraction
title_sort identification of distinct phenotypic clusters in heart failure with preserved ejection fraction
topic HFpEF AND MACHINE LEARNING
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8359985/
https://www.ncbi.nlm.nih.gov/pubmed/33779119
http://dx.doi.org/10.1002/ejhf.2169
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