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Estimation of cardiac output and systemic vascular resistance using a multivariate regression model with features selected from the finger photoplethysmogram and routine cardiovascular measurements

BACKGROUND: Cardiac output (CO) and systemic vascular resistance (SVR) are two important parameters of the cardiovascular system. The ability to measure these parameters continuously and noninvasively may assist in diagnosing and monitoring patients with suspected cardiovascular diseases, or other c...

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Autores principales: Lee, Qim Y, Redmond, Stephen J, Chan, Gregory SH, Middleton, Paul M, Steel, Elizabeth, Malouf, Philip, Critoph, Cristopher, Flynn, Gordon, O’Lone, Emma, Lovell, Nigel H
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3649882/
https://www.ncbi.nlm.nih.gov/pubmed/23452705
http://dx.doi.org/10.1186/1475-925X-12-19
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author Lee, Qim Y
Redmond, Stephen J
Chan, Gregory SH
Middleton, Paul M
Steel, Elizabeth
Malouf, Philip
Critoph, Cristopher
Flynn, Gordon
O’Lone, Emma
Lovell, Nigel H
author_facet Lee, Qim Y
Redmond, Stephen J
Chan, Gregory SH
Middleton, Paul M
Steel, Elizabeth
Malouf, Philip
Critoph, Cristopher
Flynn, Gordon
O’Lone, Emma
Lovell, Nigel H
author_sort Lee, Qim Y
collection PubMed
description BACKGROUND: Cardiac output (CO) and systemic vascular resistance (SVR) are two important parameters of the cardiovascular system. The ability to measure these parameters continuously and noninvasively may assist in diagnosing and monitoring patients with suspected cardiovascular diseases, or other critical illnesses. In this study, a method is proposed to estimate both the CO and SVR of a heterogeneous cohort of intensive care unit patients (N=48). METHODS: Spectral and morphological features were extracted from the finger photoplethysmogram, and added to heart rate and mean arterial pressure as input features to a multivariate regression model to estimate CO and SVR. A stepwise feature search algorithm was employed to select statistically significant features. Leave-one-out cross validation was used to assess the generalized model performance. The degree of agreement between the estimation method and the gold standard was assessed using Bland-Altman analysis. RESULTS: The Bland-Altman bias ±precision (1.96 times standard deviation) for CO was -0.01 ±2.70 L min(-1) when only photoplethysmogram (PPG) features were used, and for SVR was -0.87 ±412 dyn.s.cm(-5) when only one PPG variability feature was used. CONCLUSIONS: These promising results indicate the feasibility of using the method described as a non-invasive preliminary diagnostic tool in supervised or unsupervised clinical settings.
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spelling pubmed-36498822013-05-10 Estimation of cardiac output and systemic vascular resistance using a multivariate regression model with features selected from the finger photoplethysmogram and routine cardiovascular measurements Lee, Qim Y Redmond, Stephen J Chan, Gregory SH Middleton, Paul M Steel, Elizabeth Malouf, Philip Critoph, Cristopher Flynn, Gordon O’Lone, Emma Lovell, Nigel H Biomed Eng Online Research BACKGROUND: Cardiac output (CO) and systemic vascular resistance (SVR) are two important parameters of the cardiovascular system. The ability to measure these parameters continuously and noninvasively may assist in diagnosing and monitoring patients with suspected cardiovascular diseases, or other critical illnesses. In this study, a method is proposed to estimate both the CO and SVR of a heterogeneous cohort of intensive care unit patients (N=48). METHODS: Spectral and morphological features were extracted from the finger photoplethysmogram, and added to heart rate and mean arterial pressure as input features to a multivariate regression model to estimate CO and SVR. A stepwise feature search algorithm was employed to select statistically significant features. Leave-one-out cross validation was used to assess the generalized model performance. The degree of agreement between the estimation method and the gold standard was assessed using Bland-Altman analysis. RESULTS: The Bland-Altman bias ±precision (1.96 times standard deviation) for CO was -0.01 ±2.70 L min(-1) when only photoplethysmogram (PPG) features were used, and for SVR was -0.87 ±412 dyn.s.cm(-5) when only one PPG variability feature was used. CONCLUSIONS: These promising results indicate the feasibility of using the method described as a non-invasive preliminary diagnostic tool in supervised or unsupervised clinical settings. BioMed Central 2013-03-04 /pmc/articles/PMC3649882/ /pubmed/23452705 http://dx.doi.org/10.1186/1475-925X-12-19 Text en Copyright © 2013 Lee et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Lee, Qim Y
Redmond, Stephen J
Chan, Gregory SH
Middleton, Paul M
Steel, Elizabeth
Malouf, Philip
Critoph, Cristopher
Flynn, Gordon
O’Lone, Emma
Lovell, Nigel H
Estimation of cardiac output and systemic vascular resistance using a multivariate regression model with features selected from the finger photoplethysmogram and routine cardiovascular measurements
title Estimation of cardiac output and systemic vascular resistance using a multivariate regression model with features selected from the finger photoplethysmogram and routine cardiovascular measurements
title_full Estimation of cardiac output and systemic vascular resistance using a multivariate regression model with features selected from the finger photoplethysmogram and routine cardiovascular measurements
title_fullStr Estimation of cardiac output and systemic vascular resistance using a multivariate regression model with features selected from the finger photoplethysmogram and routine cardiovascular measurements
title_full_unstemmed Estimation of cardiac output and systemic vascular resistance using a multivariate regression model with features selected from the finger photoplethysmogram and routine cardiovascular measurements
title_short Estimation of cardiac output and systemic vascular resistance using a multivariate regression model with features selected from the finger photoplethysmogram and routine cardiovascular measurements
title_sort estimation of cardiac output and systemic vascular resistance using a multivariate regression model with features selected from the finger photoplethysmogram and routine cardiovascular measurements
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3649882/
https://www.ncbi.nlm.nih.gov/pubmed/23452705
http://dx.doi.org/10.1186/1475-925X-12-19
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