Literature DB >> 30351489

Bioelectrical impedance analysis in the management of heart failure in adult patients with congenital heart disease.

Masaki Sato1, Kei Inai1,2, Mikiko Shimizu1, Hisashi Sugiyama1, Toshio Nakanishi2.   

Abstract

OBJECTIVE: The recognition of fluid retention is critical in treating heart failure (HF). Bioelectrical impedance analysis (BIA) is a well-known noninvasive method; however, data on its role in managing patients with congenital heart disease (CHD) are limited. Here, we aimed to clarify the correlation between BIA and HF severity as well as the prognostic value of BIA in adult patients with CHD.
DESIGN: This prospective single-center study included 170 patients with CHD admitted between 2013 and 2015. We evaluated BIA parameters (intra- and extracellular water, protein, and mineral levels, edema index [EI, extracellular water-to-total body water ratio]), laboratory values, and HF-related admission prevalence.
RESULTS: Patients with New York Heart Association (NYHA) functional classes III-IV had a higher EI than those with NYHA classes I-II (mean ± SD, 0.398 ± 0.011 vs 0.384 ± 0.017, P < .001). EI was significantly correlated with brain natriuretic peptide level (r = 0.51, P < .001). During the mean follow-up period of 7.1 months, Kaplan-Meier analysis showed that a discharge EI > 0.386, the median value in the present study, was significantly associated with a future increased risk of HF-related admission (HR = 4.15, 95% CI = 1.70-11.58, P < .001). A body weight reduction during hospitalization was also related to EI reduction.
CONCLUSIONS: EI determined using BIA could be a useful marker for HF severity that could predict future HF-related admissions in adult patients with CHD.
© 2018 Wiley Periodicals, Inc.

Entities:  

Keywords:  bioelectrical impedance analysis; congenital heart disease; fluid retention; heart failure

Mesh:

Year:  2018        PMID: 30351489     DOI: 10.1111/chd.12683

Source DB:  PubMed          Journal:  Congenit Heart Dis        ISSN: 1747-079X            Impact factor:   2.007


  1 in total

1.  Machine Learning Model Based on Transthoracic Bioimpedance and Heart Rate Variability for Lung Fluid Accumulation Detection: Prospective Clinical Study.

Authors:  Natasa Reljin; Hugo F Posada-Quintero; Caitlin Eaton-Robb; Sophia Binici; Emily Ensom; Eric Ding; Anna Hayes; Jarno Riistama; Chad Darling; David McManus; Ki H Chon
Journal:  JMIR Med Inform       Date:  2020-08-27
  1 in total

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