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:	Nir Goldstein, Arik Eisenkraft, Carlos J. Arguello, Ge Justin Yang, Efrat Sand, Arik Ben Ishay, Roei Merin, Meir Fons, Romi Littman, Dean Nachman, Yftach Gepner
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	remote patient monitoring physiological patterns bio-surveillance biological outbreak influenza photoplethysmography Medicine R
Acceso en línea:	https://doaj.org/article/796d66fb56794b759fcfb3a534140ddd
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spelling	oai:doaj.org-article:796d66fb56794b759fcfb3a534140ddd2021-11-11T17:48:30ZExploring Early Pre-Symptomatic Detection of Influenza Using Continuous Monitoring of Advanced Physiological Parameters during a Randomized Controlled Trial10.3390/jcm102152022077-0383https://doaj.org/article/796d66fb56794b759fcfb3a534140ddd2021-11-01T00:00:00Zhttps://www.mdpi.com/2077-0383/10/21/5202https://doaj.org/toc/2077-0383Early 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.Nir GoldsteinArik EisenkraftCarlos J. ArguelloGe Justin YangEfrat SandArik Ben IshayRoei MerinMeir FonsRomi LittmanDean NachmanYftach GepnerMDPI AGarticleremote patient monitoringphysiological patternsbio-surveillancebiological outbreakinfluenzaphotoplethysmographyMedicineRENJournal of Clinical Medicine, Vol 10, Iss 5202, p 5202 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	remote patient monitoring physiological patterns bio-surveillance biological outbreak influenza photoplethysmography Medicine R
spellingShingle	remote patient monitoring physiological patterns bio-surveillance biological outbreak influenza photoplethysmography Medicine R Nir Goldstein Arik Eisenkraft Carlos J. Arguello Ge Justin Yang Efrat Sand Arik Ben Ishay Roei Merin Meir Fons Romi Littman Dean Nachman Yftach Gepner Exploring Early Pre-Symptomatic Detection of Influenza Using Continuous Monitoring of Advanced Physiological Parameters during a Randomized Controlled Trial
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.
format	article
author	Nir Goldstein Arik Eisenkraft Carlos J. Arguello Ge Justin Yang Efrat Sand Arik Ben Ishay Roei Merin Meir Fons Romi Littman Dean Nachman Yftach Gepner
author_facet	Nir Goldstein Arik Eisenkraft Carlos J. Arguello Ge Justin Yang Efrat Sand Arik Ben Ishay Roei Merin Meir Fons Romi Littman Dean Nachman Yftach Gepner
author_sort	Nir Goldstein
title	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_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_sort	exploring early pre-symptomatic detection of influenza using continuous monitoring of advanced physiological parameters during a randomized controlled trial
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/796d66fb56794b759fcfb3a534140ddd
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Exploring Early Pre-Symptomatic Detection of Influenza Using Continuous Monitoring of Advanced Physiological Parameters during a Randomized Controlled Trial

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