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Association between Remote Dielectric Sensing and Estimated Plasma Volume to Assess Body Fluid Distribution

Background: Pulmonary congestion is quantified by a remote dielectric sensing (ReDS(TM)) system, while systemic congestion is estimated by calculated plasma volume. The type of clinical patient profile as defined by the ReDS system and calculated plasma volume remains uncertain. Methods: Hospitalize...

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Autores principales: Imamura, Teruhiko, Izumida, Toshihide, Narang, Nikhil, Onoda, Hiroshi, Nakagaito, Masaki, Tanaka, Shuhei, Nakamura, Makiko, Ushijima, Ryuichi, Fujioka, Hayato, Kakeshita, Kota, Kinugawa, Koichiro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
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Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9866345/
https://www.ncbi.nlm.nih.gov/pubmed/36675391
http://dx.doi.org/10.3390/jcm12020463
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author Imamura, Teruhiko
Izumida, Toshihide
Narang, Nikhil
Onoda, Hiroshi
Nakagaito, Masaki
Tanaka, Shuhei
Nakamura, Makiko
Ushijima, Ryuichi
Fujioka, Hayato
Kakeshita, Kota
Kinugawa, Koichiro
author_facet Imamura, Teruhiko
Izumida, Toshihide
Narang, Nikhil
Onoda, Hiroshi
Nakagaito, Masaki
Tanaka, Shuhei
Nakamura, Makiko
Ushijima, Ryuichi
Fujioka, Hayato
Kakeshita, Kota
Kinugawa, Koichiro
author_sort Imamura, Teruhiko
collection PubMed
description Background: Pulmonary congestion is quantified by a remote dielectric sensing (ReDS(TM)) system, while systemic congestion is estimated by calculated plasma volume. The type of clinical patient profile as defined by the ReDS system and calculated plasma volume remains uncertain. Methods: Hospitalized patients with or without heart failure were included in this prospective study. On admission, ReDS values were measured and plasma volume status (PVS) was estimated using their body weight at the same time. Cutoffs of ReDS value and PVS were defined at 34% and −2.7%, respectively. The association between the two parameters was assessed. Results: A total of 482 patients (median 76 years, 288 men) were included. The median ReDS value was 28% (25%, 32%) and median PVS was −16.4% (−26.3%, −5.9%). Of the patients, 64 had high ReDS value (and low PVS) and 80 had high PVS (and low ReDS value). The high ReDS group had a higher prevalence of clinical heart failure with a more elevated echocardiographic E/e’ ratio, whereas the high PVS group had a higher prevalence of chronic kidney disease (p < 0.05 for all). Four out of a total of six patients with high ReDS value and high PVS had both heart failure and chronic kidney disease profiles. Conclusion: The combination of ReDS value and PVS was able to clinically stratify the types of body fluid distribution and patient profiles. Utilizing these tools may assist the clinician in constructing a therapeutic strategy for the at-risk hospitalized patient.
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spelling pubmed-98663452023-01-22 Association between Remote Dielectric Sensing and Estimated Plasma Volume to Assess Body Fluid Distribution Imamura, Teruhiko Izumida, Toshihide Narang, Nikhil Onoda, Hiroshi Nakagaito, Masaki Tanaka, Shuhei Nakamura, Makiko Ushijima, Ryuichi Fujioka, Hayato Kakeshita, Kota Kinugawa, Koichiro J Clin Med Article Background: Pulmonary congestion is quantified by a remote dielectric sensing (ReDS(TM)) system, while systemic congestion is estimated by calculated plasma volume. The type of clinical patient profile as defined by the ReDS system and calculated plasma volume remains uncertain. Methods: Hospitalized patients with or without heart failure were included in this prospective study. On admission, ReDS values were measured and plasma volume status (PVS) was estimated using their body weight at the same time. Cutoffs of ReDS value and PVS were defined at 34% and −2.7%, respectively. The association between the two parameters was assessed. Results: A total of 482 patients (median 76 years, 288 men) were included. The median ReDS value was 28% (25%, 32%) and median PVS was −16.4% (−26.3%, −5.9%). Of the patients, 64 had high ReDS value (and low PVS) and 80 had high PVS (and low ReDS value). The high ReDS group had a higher prevalence of clinical heart failure with a more elevated echocardiographic E/e’ ratio, whereas the high PVS group had a higher prevalence of chronic kidney disease (p < 0.05 for all). Four out of a total of six patients with high ReDS value and high PVS had both heart failure and chronic kidney disease profiles. Conclusion: The combination of ReDS value and PVS was able to clinically stratify the types of body fluid distribution and patient profiles. Utilizing these tools may assist the clinician in constructing a therapeutic strategy for the at-risk hospitalized patient. MDPI 2023-01-06 /pmc/articles/PMC9866345/ /pubmed/36675391 http://dx.doi.org/10.3390/jcm12020463 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Imamura, Teruhiko
Izumida, Toshihide
Narang, Nikhil
Onoda, Hiroshi
Nakagaito, Masaki
Tanaka, Shuhei
Nakamura, Makiko
Ushijima, Ryuichi
Fujioka, Hayato
Kakeshita, Kota
Kinugawa, Koichiro
Association between Remote Dielectric Sensing and Estimated Plasma Volume to Assess Body Fluid Distribution
title Association between Remote Dielectric Sensing and Estimated Plasma Volume to Assess Body Fluid Distribution
title_full Association between Remote Dielectric Sensing and Estimated Plasma Volume to Assess Body Fluid Distribution
title_fullStr Association between Remote Dielectric Sensing and Estimated Plasma Volume to Assess Body Fluid Distribution
title_full_unstemmed Association between Remote Dielectric Sensing and Estimated Plasma Volume to Assess Body Fluid Distribution
title_short Association between Remote Dielectric Sensing and Estimated Plasma Volume to Assess Body Fluid Distribution
title_sort association between remote dielectric sensing and estimated plasma volume to assess body fluid distribution
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9866345/
https://www.ncbi.nlm.nih.gov/pubmed/36675391
http://dx.doi.org/10.3390/jcm12020463
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