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iCVS—Inferring Cardio-Vascular hidden States from physiological signals available at the bedside

Intensive care medicine is complex and resource-demanding. A critical and common challenge lies in inferring the underlying physiological state of a patient from partially observed data. Specifically for the cardiovascular system, clinicians use observables such as heart rate, arterial and venous bl...

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Autores principales: Ravid Tannenbaum, Neta, Gottesman, Omer, Assadi, Azadeh, Mazwi, Mjaye, Shalit, Uri, Eytan, Danny
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10503777/
https://www.ncbi.nlm.nih.gov/pubmed/37669284
http://dx.doi.org/10.1371/journal.pcbi.1010835
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author Ravid Tannenbaum, Neta
Gottesman, Omer
Assadi, Azadeh
Mazwi, Mjaye
Shalit, Uri
Eytan, Danny
author_facet Ravid Tannenbaum, Neta
Gottesman, Omer
Assadi, Azadeh
Mazwi, Mjaye
Shalit, Uri
Eytan, Danny
author_sort Ravid Tannenbaum, Neta
collection PubMed
description Intensive care medicine is complex and resource-demanding. A critical and common challenge lies in inferring the underlying physiological state of a patient from partially observed data. Specifically for the cardiovascular system, clinicians use observables such as heart rate, arterial and venous blood pressures, as well as findings from the physical examination and ancillary tests to formulate a mental model and estimate hidden variables such as cardiac output, vascular resistance, filling pressures and volumes, and autonomic tone. Then, they use this mental model to derive the causes for instability and choose appropriate interventions. Not only this is a very hard problem due to the nature of the signals, but it also requires expertise and a clinician’s ongoing presence at the bedside. Clinical decision support tools based on mechanistic dynamical models offer an appealing solution due to their inherent explainability, corollaries to the clinical mental process, and predictive power. With a translational motivation in mind, we developed iCVS: a simple, with high explanatory power, dynamical mechanistic model to infer hidden cardiovascular states. Full model estimation requires no prior assumptions on physiological parameters except age and weight, and the only inputs are arterial and venous pressure waveforms. iCVS also considers autonomic and non-autonomic modulations. To gain more information without increasing model complexity, both slow and fast timescales of the blood pressure traces are exploited, while the main inference and dynamic evolution are at the longer, clinically relevant, timescale of minutes. iCVS is designed to allow bedside deployment at pediatric and adult intensive care units and for retrospective investigation of cardiovascular mechanisms underlying instability. In this paper, we describe iCVS and inference system in detail, and using a dataset of critically-ill children, we provide initial indications to its ability to identify bleeding, distributive states, and cardiac dysfunction, in isolation and in combination.
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spelling pubmed-105037772023-09-16 iCVS—Inferring Cardio-Vascular hidden States from physiological signals available at the bedside Ravid Tannenbaum, Neta Gottesman, Omer Assadi, Azadeh Mazwi, Mjaye Shalit, Uri Eytan, Danny PLoS Comput Biol Research Article Intensive care medicine is complex and resource-demanding. A critical and common challenge lies in inferring the underlying physiological state of a patient from partially observed data. Specifically for the cardiovascular system, clinicians use observables such as heart rate, arterial and venous blood pressures, as well as findings from the physical examination and ancillary tests to formulate a mental model and estimate hidden variables such as cardiac output, vascular resistance, filling pressures and volumes, and autonomic tone. Then, they use this mental model to derive the causes for instability and choose appropriate interventions. Not only this is a very hard problem due to the nature of the signals, but it also requires expertise and a clinician’s ongoing presence at the bedside. Clinical decision support tools based on mechanistic dynamical models offer an appealing solution due to their inherent explainability, corollaries to the clinical mental process, and predictive power. With a translational motivation in mind, we developed iCVS: a simple, with high explanatory power, dynamical mechanistic model to infer hidden cardiovascular states. Full model estimation requires no prior assumptions on physiological parameters except age and weight, and the only inputs are arterial and venous pressure waveforms. iCVS also considers autonomic and non-autonomic modulations. To gain more information without increasing model complexity, both slow and fast timescales of the blood pressure traces are exploited, while the main inference and dynamic evolution are at the longer, clinically relevant, timescale of minutes. iCVS is designed to allow bedside deployment at pediatric and adult intensive care units and for retrospective investigation of cardiovascular mechanisms underlying instability. In this paper, we describe iCVS and inference system in detail, and using a dataset of critically-ill children, we provide initial indications to its ability to identify bleeding, distributive states, and cardiac dysfunction, in isolation and in combination. Public Library of Science 2023-09-05 /pmc/articles/PMC10503777/ /pubmed/37669284 http://dx.doi.org/10.1371/journal.pcbi.1010835 Text en © 2023 Ravid Tannenbaum et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Ravid Tannenbaum, Neta
Gottesman, Omer
Assadi, Azadeh
Mazwi, Mjaye
Shalit, Uri
Eytan, Danny
iCVS—Inferring Cardio-Vascular hidden States from physiological signals available at the bedside
title iCVS—Inferring Cardio-Vascular hidden States from physiological signals available at the bedside
title_full iCVS—Inferring Cardio-Vascular hidden States from physiological signals available at the bedside
title_fullStr iCVS—Inferring Cardio-Vascular hidden States from physiological signals available at the bedside
title_full_unstemmed iCVS—Inferring Cardio-Vascular hidden States from physiological signals available at the bedside
title_short iCVS—Inferring Cardio-Vascular hidden States from physiological signals available at the bedside
title_sort icvs—inferring cardio-vascular hidden states from physiological signals available at the bedside
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10503777/
https://www.ncbi.nlm.nih.gov/pubmed/37669284
http://dx.doi.org/10.1371/journal.pcbi.1010835
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