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Meijs, C. ; Handoko, M.L.* ; Savarese, G.* ; Vernooij, R.W.M.* ; Vaartjes, I.* ; Banerjee, A.* ; Koudstaal, S.* ; Brugts, J.J.* ; Asselbergs, F.W.* ; Uijl, A.*

Discovering distinct phenotypical clusters in heart failure across the ejection fraction spectrum: A systematic review.

Curr. Heart Fail. Rep. 20, 333-349 (2023)
Verlagsversion DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
REVIEW PURPOSE: This systematic review aims to summarise clustering studies in heart failure (HF) and guide future clinical trial design and implementation in routine clinical practice. FINDINGS: 34 studies were identified (n = 19 in HF with preserved ejection fraction (HFpEF)). There was significant heterogeneity invariables and techniques used. However, 149/165 described clusters could be assigned to one of nine phenotypes: 1) young, low comorbidity burden; 2) metabolic; 3) cardio-renal; 4) atrial fibrillation (AF); 5) elderly female AF; 6) hypertensive-comorbidity; 7) ischaemic-male; 8) valvular disease; and 9) devices. There was room for improvement on important methodological topics for all clustering studies such as external validation and transparency of the modelling process. The large overlap between the phenotypes of the clustering studies shows that clustering is a robust approach for discovering clinically distinct phenotypes. However, future studies should invest in a phenotype model that can be implemented in routine clinical practice and future clinical trial design. HF = heart failure, EF = ejection fraction, HFpEF = heart failure with preserved ejection fraction, HFrEF = heart failure with reduced ejection fraction, CKD = chronic kidney disease, AF = atrial fibrillation, IHD = ischaemic heart disease, CAD = coronary artery disease, ICD = implantable cardioverter-defibrillator, CRT = cardiac resynchronization therapy, NT-proBNP = N-terminal pro b-type natriuretic peptide, BMI = Body Mass Index, COPD = Chronic obstructive pulmonary disease.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Review
Schlagwörter Clustering ; Heart Failure ; Machine Learning ; Phenotyping ; Precision Medicine; Chronic Kidney-disease; Atrial-fibrillation; Outcomes; Mechanisms; Prognosis; Midrange; Insights; Anemia
Sprache englisch
Veröffentlichungsjahr 2023
HGF-Berichtsjahr 2023
ISSN (print) / ISBN 1546-9530
e-ISSN 1546-9549
Zeitschrift Current Heart Failure Reports
Quellenangaben Band: 20, Heft: 5, Seiten: 333-349 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Campus, 4 Crinan St, London, N1 9xw, England
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-554700-001
Förderungen
European Union
UK Research and Innovation
British Medical Association
National Institute for Health and Care Research (NIHR)
Dutch Heart Foundation (NHS)
Dutch CardioVascular Alliance
Dutch Heart Foundation
UCL Hospitals NIHR Biomedical Research Centre
EU/EFPIA Innovative Medicines Initiative 2 Joint Undertaking BigData@Heart
DOI 10.1007/s11897-023-00615-z
WOS ID 001035560400001
Scopus ID 85165242078
PubMed ID 37477803
Erfassungsdatum 2023-10-06
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