Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Identifying novel phenotypes of acute heart failure using cluster analysis of clinical variables.

International journal of cardiology - 1 Jul 2018

Horiuchi Yu, Tanimoto Shuzou, Latif A H M Mahbub, Urayama Kevin Y, Aoki Jiro, Yahagi Kazuyuki, Okuno Taishi, Sato Yu, Tanaka Tetsu, Koseki Keita, Komiyama Kota, Nakajima Hiroyoshi, Hara Kazuhiro, Tanabe Kengo

Abstract excerpt

BACKGROUND: Acute heart failure (AHF) is a heterogeneous disease caused by various cardiovascular (CV) pathophysiology and multiple non-CV comorbidities. We aimed to identify clinically important subgroups to improve our understanding of the pathophysiology of AHF and inform clinical decision-making. METHODS: We evaluated detailed clinical data of 345 consecutive AHF patients using non-hierarchical cluster...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Acute Disease
Share to Clinical Decision-Making

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Share to Cluster Analysis

Share to Echocardiography

Share to Follow-Up Studies

Share to Heart Failure

Share to Hospitalization

Share to Stroke Volume

Source

Identifiers

PMID: 29622508
DOI: 10.1016/j.ijcard.2018.03.098

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-08.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence. Graph snapshot: 2026-09-29.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Identifying novel phenotypes of acute heart failure using cluster analysis of clinical variables.PMID 29622508Read ↗

Clinical Profiles in Acute Heart FailureSemantic proximity Read ↗

Clinical implications of chronic heart failure phenotypes defined by cluster analysis.Semantic proximity Read ↗

Prognostic Clinical Phenotypes of Patients with Acute Decompensated Heart Failure.Semantic proximity Read ↗

Clinical phenotypes of heart failure patients with supranormal ejection fraction.Semantic proximity Read ↗

Unsupervised Cluster Analysis of Patients With Aortic Stenosis Reveals Distinct Population With Different Phenotypes and Outcomes.Semantic proximity Read ↗

Phenotype clustering of hospitalized high-risk patients with COVID-19 - a machine learning approach within the multicentre, multinational PCHF-COVICAV registry.Semantic proximity Read ↗

Phenotyping of Patients With Clinical Features of Advanced Heart Failure: A Latent Class Analysis of a Multicenter Registry of Patients Hospitalized With Heart Failure.Semantic proximity Read ↗

Profiling heart failure with preserved or mildly reduced ejection fraction by cluster analysis.Semantic proximity Read ↗

Discovering Distinct Phenotypical Clusters in Heart Failure Across the Ejection Fraction Spectrum: a Systematic Review.Semantic proximity Read ↗

Phenogroups and Their Prognosis of Acute Decompensated Heart Failure with Preserved Ejection Fraction.Semantic proximity Read ↗

Machine Learning Methods Improve Prognostication, Identify Clinically Distinct Phenotypes, and Detect Heterogeneity in Response to Therapy in a Large Cohort of Heart Failure Patients.Semantic proximity Read ↗

Unsupervised machine learning to identify subphenotypes among cardiac intensive care unit patients with heart failure.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.