Cargando…
Identifying Phenogroups in patients with subclinical diastolic dysfunction using unsupervised statistical learning
BACKGROUND: Subclinical diastolic dysfunction is a precursor for developing heart failure with preserved ejection fraction (HFpEF); yet not all patients progress to HFpEF. Our objective was to evaluate clinical and echocardiographic variables to identify patients who develop HFpEF. METHODS: Clinical...
Autores principales: | Kaptein, Yvonne E., Karagodin, Ilya, Zuo, Hongquan, Lu, Yu, Zhang, Jun, Kaptein, John S., Strande, Jennifer L. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7427922/ https://www.ncbi.nlm.nih.gov/pubmed/32795252 http://dx.doi.org/10.1186/s12872-020-01620-z |
Ejemplares similares
-
Aortic stiffening precedes onset of heart failure with preserved ejection fraction in patients with asymptomatic diastolic dysfunction
por: Karagodin, Ilya, et al.
Publicado: (2017) -
Echocardiographic phenogrouping by machine learning for risk stratification in the general population
por: Sabovčik, František, et al.
Publicado: (2021) -
Identifying high risk clinical phenogroups of pulmonary hypertension through a clustering analysis
por: Rambarat, Paula, et al.
Publicado: (2023) -
Heart failure with preserved ejection fraction phenogroup classification using machine learning
por: Kyodo, Atsushi, et al.
Publicado: (2023) -
A preclinical model for phenogroup 3 HFpEF
por: Yousefi, Keyvan, et al.
Publicado: (2019)