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Advanced Hemodynamic Monitoring Allows Recognition of Early Response Patterns to Diuresis in Congestive Heart Failure Patients
There are no clear guidelines for diuretic administration in heart failure (HF), and reliable markers are needed to tailor treatment. Continuous monitoring of multiple advanced physiological parameters during diuresis may allow better differentiation of patients into subgroups according to their res...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9821287/ https://www.ncbi.nlm.nih.gov/pubmed/36614848 http://dx.doi.org/10.3390/jcm12010045 |
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author | Dagan, Maya Kolben, Yotam Goldstein, Nir Ben Ishay, Arik Fons, Meir Merin, Roei Eisenkraft, Arik Amir, Offer Asleh, Rabea Ben-Yehuda, Arie Nachman, Dean |
author_facet | Dagan, Maya Kolben, Yotam Goldstein, Nir Ben Ishay, Arik Fons, Meir Merin, Roei Eisenkraft, Arik Amir, Offer Asleh, Rabea Ben-Yehuda, Arie Nachman, Dean |
author_sort | Dagan, Maya |
collection | PubMed |
description | There are no clear guidelines for diuretic administration in heart failure (HF), and reliable markers are needed to tailor treatment. Continuous monitoring of multiple advanced physiological parameters during diuresis may allow better differentiation of patients into subgroups according to their responses. In this study, 29 HF patients were monitored during outpatient intravenous diuresis, using a noninvasive wearable multi-parameter monitor. Analysis of changes in these parameters during the course of diuresis aimed to recognize subgroups with different response patterns. Parameters did not change significantly, however, subgroup analysis of the last quartile of treatment showed significant differences in cardiac output, cardiac index, stroke volume, pulse rate, and systemic vascular resistance according to gender, and in systolic blood pressure according to habitus. Changes in the last quartile could be differentiated using k-means, a technique of unsupervised machine learning. Moreover, patients’ responses could be best clustered into four groups. Analysis of baseline parameters showed that two of the clusters differed by baseline parameters, body mass index, and diabetes status. To conclude, we show that physiological changes during diuresis in HF patients can be categorized into subgroups sharing similar response trends, making noninvasive monitoring a potential key to personalized treatment in HF. |
format | Online Article Text |
id | pubmed-9821287 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-98212872023-01-07 Advanced Hemodynamic Monitoring Allows Recognition of Early Response Patterns to Diuresis in Congestive Heart Failure Patients Dagan, Maya Kolben, Yotam Goldstein, Nir Ben Ishay, Arik Fons, Meir Merin, Roei Eisenkraft, Arik Amir, Offer Asleh, Rabea Ben-Yehuda, Arie Nachman, Dean J Clin Med Article There are no clear guidelines for diuretic administration in heart failure (HF), and reliable markers are needed to tailor treatment. Continuous monitoring of multiple advanced physiological parameters during diuresis may allow better differentiation of patients into subgroups according to their responses. In this study, 29 HF patients were monitored during outpatient intravenous diuresis, using a noninvasive wearable multi-parameter monitor. Analysis of changes in these parameters during the course of diuresis aimed to recognize subgroups with different response patterns. Parameters did not change significantly, however, subgroup analysis of the last quartile of treatment showed significant differences in cardiac output, cardiac index, stroke volume, pulse rate, and systemic vascular resistance according to gender, and in systolic blood pressure according to habitus. Changes in the last quartile could be differentiated using k-means, a technique of unsupervised machine learning. Moreover, patients’ responses could be best clustered into four groups. Analysis of baseline parameters showed that two of the clusters differed by baseline parameters, body mass index, and diabetes status. To conclude, we show that physiological changes during diuresis in HF patients can be categorized into subgroups sharing similar response trends, making noninvasive monitoring a potential key to personalized treatment in HF. MDPI 2022-12-21 /pmc/articles/PMC9821287/ /pubmed/36614848 http://dx.doi.org/10.3390/jcm12010045 Text en © 2022 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 Dagan, Maya Kolben, Yotam Goldstein, Nir Ben Ishay, Arik Fons, Meir Merin, Roei Eisenkraft, Arik Amir, Offer Asleh, Rabea Ben-Yehuda, Arie Nachman, Dean Advanced Hemodynamic Monitoring Allows Recognition of Early Response Patterns to Diuresis in Congestive Heart Failure Patients |
title | Advanced Hemodynamic Monitoring Allows Recognition of Early Response Patterns to Diuresis in Congestive Heart Failure Patients |
title_full | Advanced Hemodynamic Monitoring Allows Recognition of Early Response Patterns to Diuresis in Congestive Heart Failure Patients |
title_fullStr | Advanced Hemodynamic Monitoring Allows Recognition of Early Response Patterns to Diuresis in Congestive Heart Failure Patients |
title_full_unstemmed | Advanced Hemodynamic Monitoring Allows Recognition of Early Response Patterns to Diuresis in Congestive Heart Failure Patients |
title_short | Advanced Hemodynamic Monitoring Allows Recognition of Early Response Patterns to Diuresis in Congestive Heart Failure Patients |
title_sort | advanced hemodynamic monitoring allows recognition of early response patterns to diuresis in congestive heart failure patients |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9821287/ https://www.ncbi.nlm.nih.gov/pubmed/36614848 http://dx.doi.org/10.3390/jcm12010045 |
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