Machine Learning Approach to Identify Phenotypes in Patients with Ischaemic Heart Failure with Reduced Ejection Fraction
Clustering reveals distinct risk subgroups in the heterogeneous array of ischaemic HFrEF patients. This classification, accessible online, could enhance future outcome predictions for ischaemic HFrEF cases.
Authors
- Bertram Pitt
- Nicolas Girerd (ORCID: https://orcid.org/0000-0002-3278-2057)
- John G.F. Cleland (ORCID: https://orcid.org/0000-0002-1471-7016)
- Faiez M. Zannad (ORCID: https://orcid.org/0000-0001-7456-1570)
- Emmanuel Bresso (ORCID: https://orcid.org/0000-0002-5650-3155)
- Stefan D. Anker
- Mandeep R. Mehra (ORCID: https://orcid.org/0000-0001-8683-7044)
- Luca Monzo (ORCID: https://orcid.org/0000-0003-2958-943X)
- Carolyn S.P. Lam (ORCID: https://orcid.org/0000-0003-1903-0018)
- Barry Greenberg
- Dirk J. van Veldhuisen
- Kenneth Dickstein
Institutions
- Brigham and Women's Hospital (US)
- University Medical Center Groningen (NL)
- Harvard University (US)
- Inserm (FR)
- National University of Singapore (SG)
- University of Groningen (NL)
- University of Michigan (US)
- Stavanger University Hospital (NO)
- University of California San Diego (US)
- National University Heart Centre Singapore (SG)
- Michigan Medicine (US)
- Duke-NUS Medical School (SG)
- Berlin-Brandenburger Centrum für Regenerative Therapien (DE)
- German Centre for Cardiovascular Research (DE)
- National Heart Centre Singapore (SG)
- The Robertson Trust (GB)
- French Clinical Research Infrastructure Network (FR)
- University of Bergen (NO)
- Charité - Universitätsmedizin Berlin (DE)
- University of Glasgow (GB)
- Université de Lorraine (FR)
Publication Details
- Journal
- European Journal of Heart Failure
- Published
- 2024-12-10
- DOI
- https://doi.org/10.1002/ejhf.3547
- Citations
- 14
- Primary Topic
- Heart Failure Treatment and Management
- Type
- article
- Field-Weighted Citation Impact
- 5.63
Funders
- European Regional Development Fund