Open AccessClustering based on comorbidities in patients with chronic heart failure: an illustration of clinical diversity
Author(s) -
UszkoLencer 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 HansPeter,
Spruit Martijn A.
Publication year - 2022
Publication title -
esc heart failure
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.787
H-Index - 25
ISSN - 2055-5822
DOI - 10.1002/ehf2.13704
Subject(s) - medicine , comorbidity , heart failure , ejection fraction , interquartile range , cluster (spacecraft) , diabetes mellitus , cardiomyopathy , disease , programming language , computer science , endocrinology
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.