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Exploring Early Pre-Symptomatic Detection of Influenza Using Continuous Monitoring of Advanced Physiological Parameters during a Randomized Controlled Trial

Early detection of influenza may improve responses against outbreaks. This study was part of a clinical study assessing the efficacy of a novel influenza vaccine, aiming to discover distinct, highly predictive patterns of pre-symptomatic illness based on changes in advanced physiological parameters...

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Autores principales: Goldstein, Nir, Eisenkraft, Arik, Arguello, Carlos J., Yang, Ge Justin, Sand, Efrat, Ishay, Arik Ben, Merin, Roei, Fons, Meir, Littman, Romi, Nachman, Dean, Gepner, Yftach
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8584386/
https://www.ncbi.nlm.nih.gov/pubmed/34768722
http://dx.doi.org/10.3390/jcm10215202
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author Goldstein, Nir
Eisenkraft, Arik
Arguello, Carlos J.
Yang, Ge Justin
Sand, Efrat
Ishay, Arik Ben
Merin, Roei
Fons, Meir
Littman, Romi
Nachman, Dean
Gepner, Yftach
author_facet Goldstein, Nir
Eisenkraft, Arik
Arguello, Carlos J.
Yang, Ge Justin
Sand, Efrat
Ishay, Arik Ben
Merin, Roei
Fons, Meir
Littman, Romi
Nachman, Dean
Gepner, Yftach
author_sort Goldstein, Nir
collection PubMed
description Early detection of influenza may improve responses against outbreaks. This study was part of a clinical study assessing the efficacy of a novel influenza vaccine, aiming to discover distinct, highly predictive patterns of pre-symptomatic illness based on changes in advanced physiological parameters using a novel wearable sensor. Participants were frequently monitored 24 h before and for nine days after the influenza challenge. Viral load was measured daily, and self-reported symptoms were collected twice a day. The Random Forest classifier model was used to classify the participants based on changes in the measured parameters. A total of 116 participants with ~3,400,000 data points were included. Changes in parameters were detected at an early stage of the disease, before the development of symptomatic illness. Heart rate, blood pressure, cardiac output, and systemic vascular resistance showed the greatest changes in the third post-exposure day, correlating with viral load. Applying the classifier model identified participants as flu-positive or negative with an accuracy of 0.81 ± 0.05 two days before major symptoms appeared. Cardiac index and diastolic blood pressure were the leading predicting factors when using data from the first and second day. This study suggests that frequent remote monitoring of advanced physiological parameters may provide early pre-symptomatic detection of flu.
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spelling pubmed-85843862021-11-12 Exploring Early Pre-Symptomatic Detection of Influenza Using Continuous Monitoring of Advanced Physiological Parameters during a Randomized Controlled Trial Goldstein, Nir Eisenkraft, Arik Arguello, Carlos J. Yang, Ge Justin Sand, Efrat Ishay, Arik Ben Merin, Roei Fons, Meir Littman, Romi Nachman, Dean Gepner, Yftach J Clin Med Article Early detection of influenza may improve responses against outbreaks. This study was part of a clinical study assessing the efficacy of a novel influenza vaccine, aiming to discover distinct, highly predictive patterns of pre-symptomatic illness based on changes in advanced physiological parameters using a novel wearable sensor. Participants were frequently monitored 24 h before and for nine days after the influenza challenge. Viral load was measured daily, and self-reported symptoms were collected twice a day. The Random Forest classifier model was used to classify the participants based on changes in the measured parameters. A total of 116 participants with ~3,400,000 data points were included. Changes in parameters were detected at an early stage of the disease, before the development of symptomatic illness. Heart rate, blood pressure, cardiac output, and systemic vascular resistance showed the greatest changes in the third post-exposure day, correlating with viral load. Applying the classifier model identified participants as flu-positive or negative with an accuracy of 0.81 ± 0.05 two days before major symptoms appeared. Cardiac index and diastolic blood pressure were the leading predicting factors when using data from the first and second day. This study suggests that frequent remote monitoring of advanced physiological parameters may provide early pre-symptomatic detection of flu. MDPI 2021-11-08 /pmc/articles/PMC8584386/ /pubmed/34768722 http://dx.doi.org/10.3390/jcm10215202 Text en © 2021 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
Goldstein, Nir
Eisenkraft, Arik
Arguello, Carlos J.
Yang, Ge Justin
Sand, Efrat
Ishay, Arik Ben
Merin, Roei
Fons, Meir
Littman, Romi
Nachman, Dean
Gepner, Yftach
Exploring Early Pre-Symptomatic Detection of Influenza Using Continuous Monitoring of Advanced Physiological Parameters during a Randomized Controlled Trial
title Exploring Early Pre-Symptomatic Detection of Influenza Using Continuous Monitoring of Advanced Physiological Parameters during a Randomized Controlled Trial
title_full Exploring Early Pre-Symptomatic Detection of Influenza Using Continuous Monitoring of Advanced Physiological Parameters during a Randomized Controlled Trial
title_fullStr Exploring Early Pre-Symptomatic Detection of Influenza Using Continuous Monitoring of Advanced Physiological Parameters during a Randomized Controlled Trial
title_full_unstemmed Exploring Early Pre-Symptomatic Detection of Influenza Using Continuous Monitoring of Advanced Physiological Parameters during a Randomized Controlled Trial
title_short Exploring Early Pre-Symptomatic Detection of Influenza Using Continuous Monitoring of Advanced Physiological Parameters during a Randomized Controlled Trial
title_sort exploring early pre-symptomatic detection of influenza using continuous monitoring of advanced physiological parameters during a randomized controlled trial
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8584386/
https://www.ncbi.nlm.nih.gov/pubmed/34768722
http://dx.doi.org/10.3390/jcm10215202
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