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Sepsis Mortality Prediction Using Wearable Monitoring in Low–Middle Income Countries

Sepsis is associated with high mortality—particularly in low–middle income countries (LMICs). Critical care management of sepsis is challenging in LMICs due to the lack of care providers and the high cost of bedside monitors. Recent advances in wearable sensor technology and machine learning (ML) mo...

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Autores principales: Ghiasi, Shadi, Zhu, Tingting, Lu, Ping, Hagenah, Jannis, Khanh, Phan Nguyen Quoc, Hao, Nguyen Van, Thwaites, Louise, Clifton, David A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9145695/
https://www.ncbi.nlm.nih.gov/pubmed/35632275
http://dx.doi.org/10.3390/s22103866
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author Ghiasi, Shadi
Zhu, Tingting
Lu, Ping
Hagenah, Jannis
Khanh, Phan Nguyen Quoc
Hao, Nguyen Van
Thwaites, Louise
Clifton, David A.
author_facet Ghiasi, Shadi
Zhu, Tingting
Lu, Ping
Hagenah, Jannis
Khanh, Phan Nguyen Quoc
Hao, Nguyen Van
Thwaites, Louise
Clifton, David A.
author_sort Ghiasi, Shadi
collection PubMed
description Sepsis is associated with high mortality—particularly in low–middle income countries (LMICs). Critical care management of sepsis is challenging in LMICs due to the lack of care providers and the high cost of bedside monitors. Recent advances in wearable sensor technology and machine learning (ML) models in healthcare promise to deliver new ways of digital monitoring integrated with automated decision systems to reduce the mortality risk in sepsis. In this study, firstly, we aim to assess the feasibility of using wearable sensors instead of traditional bedside monitors in the sepsis care management of hospital admitted patients, and secondly, to introduce automated prediction models for the mortality prediction of sepsis patients. To this end, we continuously monitored 50 sepsis patients for nearly 24 h after their admission to the Hospital for Tropical Diseases in Vietnam. We then compared the performance and interpretability of state-of-the-art ML models for the task of mortality prediction of sepsis using the heart rate variability (HRV) signal from wearable sensors and vital signs from bedside monitors. Our results show that all ML models trained on wearable data outperformed ML models trained on data gathered from the bedside monitors for the task of mortality prediction with the highest performance (area under the precision recall curve = 0.83) achieved using time-varying features of HRV and recurrent neural networks. Our results demonstrate that the integration of automated ML prediction models with wearable technology is well suited for helping clinicians who manage sepsis patients in LMICs to reduce the mortality risk of sepsis.
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spelling pubmed-91456952022-05-29 Sepsis Mortality Prediction Using Wearable Monitoring in Low–Middle Income Countries Ghiasi, Shadi Zhu, Tingting Lu, Ping Hagenah, Jannis Khanh, Phan Nguyen Quoc Hao, Nguyen Van Thwaites, Louise Clifton, David A. Sensors (Basel) Article Sepsis is associated with high mortality—particularly in low–middle income countries (LMICs). Critical care management of sepsis is challenging in LMICs due to the lack of care providers and the high cost of bedside monitors. Recent advances in wearable sensor technology and machine learning (ML) models in healthcare promise to deliver new ways of digital monitoring integrated with automated decision systems to reduce the mortality risk in sepsis. In this study, firstly, we aim to assess the feasibility of using wearable sensors instead of traditional bedside monitors in the sepsis care management of hospital admitted patients, and secondly, to introduce automated prediction models for the mortality prediction of sepsis patients. To this end, we continuously monitored 50 sepsis patients for nearly 24 h after their admission to the Hospital for Tropical Diseases in Vietnam. We then compared the performance and interpretability of state-of-the-art ML models for the task of mortality prediction of sepsis using the heart rate variability (HRV) signal from wearable sensors and vital signs from bedside monitors. Our results show that all ML models trained on wearable data outperformed ML models trained on data gathered from the bedside monitors for the task of mortality prediction with the highest performance (area under the precision recall curve = 0.83) achieved using time-varying features of HRV and recurrent neural networks. Our results demonstrate that the integration of automated ML prediction models with wearable technology is well suited for helping clinicians who manage sepsis patients in LMICs to reduce the mortality risk of sepsis. MDPI 2022-05-19 /pmc/articles/PMC9145695/ /pubmed/35632275 http://dx.doi.org/10.3390/s22103866 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
Ghiasi, Shadi
Zhu, Tingting
Lu, Ping
Hagenah, Jannis
Khanh, Phan Nguyen Quoc
Hao, Nguyen Van
Thwaites, Louise
Clifton, David A.
Sepsis Mortality Prediction Using Wearable Monitoring in Low–Middle Income Countries
title Sepsis Mortality Prediction Using Wearable Monitoring in Low–Middle Income Countries
title_full Sepsis Mortality Prediction Using Wearable Monitoring in Low–Middle Income Countries
title_fullStr Sepsis Mortality Prediction Using Wearable Monitoring in Low–Middle Income Countries
title_full_unstemmed Sepsis Mortality Prediction Using Wearable Monitoring in Low–Middle Income Countries
title_short Sepsis Mortality Prediction Using Wearable Monitoring in Low–Middle Income Countries
title_sort sepsis mortality prediction using wearable monitoring in low–middle income countries
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9145695/
https://www.ncbi.nlm.nih.gov/pubmed/35632275
http://dx.doi.org/10.3390/s22103866
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