Literature DB >> 33529665

A new approach to the clinical subclassification of heart failure with preserved ejection fraction.

Hirmand Nouraei1, Simon W Rabkin2.   

Abstract

OBJECTIVE: Heart failure with preserved ejection (HFpEF) represents nearly half of all patients with heart failure (HF). The objective of this study was to determine whether patient characteristics identify discrete kinds of HFpEF.
METHODS: Data were collected on 196 patients with HFpEF in a non-hospitalized setting. Clinical and laboratory variables were collected, and 47 candidate variables were examined by the unsupervised clustering strategy partitioning around medoids. The Meta-analysis Global Group in Chronic Heart Failure (MAGGIC) risk score was calculated. Follow-up data on all-cause mortality, cardiovascular mortality, and HF exacerbation, were collected and were not part of the data used to identify subgroups.
RESULTS: Six significantly different groups or clusters were found. There were three groups of women (i) individuals with a low proportion of vascular risk factors (HFpEF1) (ii) individuals with a high proportion of hypertension and diabetes, but lower proportion of kidney disease and diastolic dysfunction (HFpEF3) (iii) older individuals with high rates of atrial fibrillation (AF), chronic kidney disease. They had the worst long-term outcomes (HFpEF4). There were three groups of men (i) individuals with a high proportion of coronary artery disease (CAD), dyslipidemia, higher serum creatinine, and diastolic dysfunction (HFpEF2)(ii) individuals with highest BMI, and high proportion of CAD, obstructive sleep apnea, and poorly controlled diabetes (HFpEF5) (iii) individuals with high rates of AF, elevated BNP, biventricular remodeling (HFpEF6). They had a high cardiovascular mortality.
CONCLUSIONS: HFpEF consists of a heterogenous group of individuals with six distinct clinical subsets that have different long-term outcomes.
Copyright © 2021 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Cluster analysis; Heart failure; Preserved ejection fraction; Unsupervised machine learning

Year:  2021        PMID: 33529665     DOI: 10.1016/j.ijcard.2021.01.052

Source DB:  PubMed          Journal:  Int J Cardiol        ISSN: 0167-5273            Impact factor:   4.164


  5 in total

Review 1.  Mimicking Metabolic Disturbance in Establishing Animal Models of Heart Failure With Preserved Ejection Fraction.

Authors:  Hui Li; Yi-Yuan Xia; Chun-Lei Xia; Zheng Li; Yi Shi; Xiao-Bo Li; Jun-Xia Zhang
Journal:  Front Physiol       Date:  2022-05-03       Impact factor: 4.755

Review 2.  Evaluating the adverse outcome of subtypes of heart failure with preserved ejection fraction defined by machine learning: A systematic review focused on defining high risk phenogroups.

Authors:  Simon W Rabkin
Journal:  EXCLI J       Date:  2022-02-22       Impact factor: 4.068

3.  Comparison of Unsupervised Machine Learning Approaches for Cluster Analysis to Define Subgroups of Heart Failure with Preserved Ejection Fraction with Different Outcomes.

Authors:  Hirmand Nouraei; Hooman Nouraei; Simon W Rabkin
Journal:  Bioengineering (Basel)       Date:  2022-04-16

4.  Identifying novel subgroups in heart failure patients with unsupervised machine learning: A scoping review.

Authors:  Jin Sun; Hua Guo; Wenjun Wang; Xiao Wang; Junyu Ding; Kunlun He; Xizhou Guan
Journal:  Front Cardiovasc Med       Date:  2022-07-22

5.  Effect of diabetes mellitus on the development of left ventricular contractile dysfunction in women with heart failure and preserved ejection fraction.

Authors:  Ke Shi; Meng-Xi Yang; Shan Huang; Wei-Feng Yan; Wen-Lei Qian; Yuan Li; Ying-Kun Guo; Zhi-Gang Yang
Journal:  Cardiovasc Diabetol       Date:  2021-09-14       Impact factor: 9.951

  5 in total

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