Heart rate n-variability (HRnV) measures for prediction of mortality in sepsis patients presenting at the emergency department.

Sepsis is a potentially life-threatening condition that requires prompt recognition and treatment. Recently, heart rate variability (HRV), a measure of the cardiac autonomic regulation derived from short electrocardiogram tracings, has been found to correlate with sepsis mortality. This paper presen...

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Autores principales:	Nan Liu, Marcel Lucas Chee, Mabel Zhi Qi Foo, Jeremy Zhenwen Pong, Dagang Guo, Zhi Xiong Koh, Andrew Fu Wah Ho, Chenglin Niu, Shu-Ling Chong, Marcus Eng Hock Ong
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/dbea9251d54148da864c1033975ea23a
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spelling	oai:doaj.org-article:dbea9251d54148da864c1033975ea23a2021-12-02T20:14:52ZHeart rate n-variability (HRnV) measures for prediction of mortality in sepsis patients presenting at the emergency department.1932-620310.1371/journal.pone.0249868https://doaj.org/article/dbea9251d54148da864c1033975ea23a2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0249868https://doaj.org/toc/1932-6203Sepsis is a potentially life-threatening condition that requires prompt recognition and treatment. Recently, heart rate variability (HRV), a measure of the cardiac autonomic regulation derived from short electrocardiogram tracings, has been found to correlate with sepsis mortality. This paper presents using novel heart rate n-variability (HRnV) measures for sepsis mortality risk prediction and comparing against current mortality prediction scores. This study was a retrospective cohort study on patients presenting to the emergency department of a tertiary hospital in Singapore between September 2014 to April 2017. Patients were included if they were above 21 years old and were suspected of having sepsis by their attending physician. The primary outcome was 30-day in-hospital mortality. Stepwise multivariable logistic regression model was built to predict the outcome, and the results based on 10-fold cross-validation were presented using receiver operating curve analysis. The final predictive model comprised 21 variables, including four vital signs, two HRV parameters, and 15 HRnV parameters. The area under the curve of the model was 0.77 (95% confidence interval 0.70-0.84), outperforming several established clinical scores. The HRnV measures may have the potential to allow for a rapid, objective, and accurate means of patient risk stratification for sepsis severity and mortality. Our exploration of the use of wealthy inherent information obtained from novel HRnV measures could also create a new perspective for data scientists to develop innovative approaches for ECG analysis and risk monitoring.Nan LiuMarcel Lucas CheeMabel Zhi Qi FooJeremy Zhenwen PongDagang GuoZhi Xiong KohAndrew Fu Wah HoChenglin NiuShu-Ling ChongMarcus Eng Hock OngPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0249868 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Nan Liu Marcel Lucas Chee Mabel Zhi Qi Foo Jeremy Zhenwen Pong Dagang Guo Zhi Xiong Koh Andrew Fu Wah Ho Chenglin Niu Shu-Ling Chong Marcus Eng Hock Ong Heart rate n-variability (HRnV) measures for prediction of mortality in sepsis patients presenting at the emergency department.
description	Sepsis is a potentially life-threatening condition that requires prompt recognition and treatment. Recently, heart rate variability (HRV), a measure of the cardiac autonomic regulation derived from short electrocardiogram tracings, has been found to correlate with sepsis mortality. This paper presents using novel heart rate n-variability (HRnV) measures for sepsis mortality risk prediction and comparing against current mortality prediction scores. This study was a retrospective cohort study on patients presenting to the emergency department of a tertiary hospital in Singapore between September 2014 to April 2017. Patients were included if they were above 21 years old and were suspected of having sepsis by their attending physician. The primary outcome was 30-day in-hospital mortality. Stepwise multivariable logistic regression model was built to predict the outcome, and the results based on 10-fold cross-validation were presented using receiver operating curve analysis. The final predictive model comprised 21 variables, including four vital signs, two HRV parameters, and 15 HRnV parameters. The area under the curve of the model was 0.77 (95% confidence interval 0.70-0.84), outperforming several established clinical scores. The HRnV measures may have the potential to allow for a rapid, objective, and accurate means of patient risk stratification for sepsis severity and mortality. Our exploration of the use of wealthy inherent information obtained from novel HRnV measures could also create a new perspective for data scientists to develop innovative approaches for ECG analysis and risk monitoring.
format	article
author	Nan Liu Marcel Lucas Chee Mabel Zhi Qi Foo Jeremy Zhenwen Pong Dagang Guo Zhi Xiong Koh Andrew Fu Wah Ho Chenglin Niu Shu-Ling Chong Marcus Eng Hock Ong
author_facet	Nan Liu Marcel Lucas Chee Mabel Zhi Qi Foo Jeremy Zhenwen Pong Dagang Guo Zhi Xiong Koh Andrew Fu Wah Ho Chenglin Niu Shu-Ling Chong Marcus Eng Hock Ong
author_sort	Nan Liu
title	Heart rate n-variability (HRnV) measures for prediction of mortality in sepsis patients presenting at the emergency department.
title_short	Heart rate n-variability (HRnV) measures for prediction of mortality in sepsis patients presenting at the emergency department.
title_full	Heart rate n-variability (HRnV) measures for prediction of mortality in sepsis patients presenting at the emergency department.
title_fullStr	Heart rate n-variability (HRnV) measures for prediction of mortality in sepsis patients presenting at the emergency department.
title_full_unstemmed	Heart rate n-variability (HRnV) measures for prediction of mortality in sepsis patients presenting at the emergency department.
title_sort	heart rate n-variability (hrnv) measures for prediction of mortality in sepsis patients presenting at the emergency department.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/dbea9251d54148da864c1033975ea23a
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Heart rate n-variability (HRnV) measures for prediction of mortality in sepsis patients presenting at the emergency department.

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