Literature DB >> 16914106

Bioimpedance to prevent heart failure hospitalization.

Nancy M Albert1.   

Abstract

Transthoracic and whole-body bioimpedance monitoring has been commercially available for years; however, attention to its use as a diagnostic and event-monitoring modality has not been routinely applied in patients with heart failure (HF). In 2005, intrathoracic bioimpedance monitoring via an implantable cardioverter defibrillator brought new awareness of bioimpedance technology. In addition, new knowledge about congestion in HF, including length of time a patient is congested before seeking emergency care, lack of sensitivity of common signs and symptoms used to monitor congestion and diagnose HF exacerbation, and poor clinical outcomes when hypervolemia is present, heightened the need for more aggressive assessment and management. Bioimpedance device monitoring provides data needed to make treatment decisions that promote euvolemia and optimal cardiac performance. This review summarizes three options for measurement of bioimpedance hemodynamic data, discusses its use in preventing HF hospitalization, and describes issues that need to be overcome before bioimpedance monitoring can be routinely used in HF management.

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Year:  2006        PMID: 16914106     DOI: 10.1007/s11897-006-0013-y

Source DB:  PubMed          Journal:  Curr Heart Fail Rep        ISSN: 1546-9530


  22 in total

1.  Equivalence of the bioimpedance and thermodilution methods in measuring cardiac output in hospitalized patients with advanced, decompensated chronic heart failure.

Authors:  Nancy M Albert; Melanie D Hail; Jianbo Li; James B Young
Journal:  Am J Crit Care       Date:  2004-11       Impact factor: 2.228

2.  Comparison of cardiac output determined by bioimpedance, thermodilution, and the Fick method.

Authors:  Milo Engoren; Daniel Barbee
Journal:  Am J Crit Care       Date:  2005-01       Impact factor: 2.228

3.  Functional valvular incompetence in decompensated heart failure: noninvasive monitoring and response to medical management.

Authors:  Paulo Cesar Campos; Ivan A D'Cruz; Lillie S Johnson; Amit Malhotra; Kodangudi B Ramanathan; Karl T Weber
Journal:  Am J Med Sci       Date:  2005-05       Impact factor: 2.378

4.  The limited reliability of physical signs for estimating hemodynamics in chronic heart failure.

Authors:  L W Stevenson; J K Perloff
Journal:  JAMA       Date:  1989-02-10       Impact factor: 56.272

5.  Non-invasive measurement of cardiac output: whole-body impedance cardiography in simultaneous comparison with thermodilution and direct oxygen Fick methods.

Authors:  T Kööbi; S Kaukinen; T Ahola; V M Turjanmaa
Journal:  Intensive Care Med       Date:  1997-11       Impact factor: 17.440

6.  Emergency diagnosis of congestive heart failure: impact of signs and symptoms.

Authors:  Christian Mueller; Barbara Frana; Daniel Rodriguez; Kirsten Laule-Kilian; André P Perruchoud
Journal:  Can J Cardiol       Date:  2005-09       Impact factor: 5.223

7.  Intrathoracic impedance monitoring in patients with heart failure: correlation with fluid status and feasibility of early warning preceding hospitalization.

Authors:  Cheuk-Man Yu; Li Wang; Elaine Chau; Raymond Hon-Wah Chan; Shun-Ling Kong; Man-Oi Tang; Jill Christensen; Robert W Stadler; Chu-Pak Lau
Journal:  Circulation       Date:  2005-08-01       Impact factor: 29.690

8.  Association of impedance cardiography parameters with changes in functional and quality-of-life measures in patients with chronic heart failure.

Authors:  Kris Vijayaraghavan; Sue Crum; Sangita Cherukuri; Leslie Barnett-Avery
Journal:  Congest Heart Fail       Date:  2004 Mar-Apr

9.  Clinical, radiographic, and hemodynamic correlations in chronic congestive heart failure: conflicting results may lead to inappropriate care.

Authors:  S Chakko; D Woska; H Martinez; E de Marchena; L Futterman; K M Kessler; R J Myerberg
Journal:  Am J Med       Date:  1991-03       Impact factor: 4.965

10.  A comparison of two impedance cardiographs using head-up tilting and trend analysis.

Authors:  Lester A H Critchley; Ye Zhang; Julian A J H Critchley; Raymond C K Chung
Journal:  J Clin Monit Comput       Date:  2002-02       Impact factor: 2.502

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  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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