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Discovery of signatures of fatal neonatal illness in vital signs using highly comparative time-series analysis

To seek new signatures of illness in heart rate and oxygen saturation vital signs from Neonatal Intensive Care Unit (NICU) patients, we implemented highly comparative time-series analysis to discover features of all-cause mortality in the next 7 days. We collected 0.5 Hz heart rate and oxygen satura...

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Autores principales: Niestroy, Justin C., Moorman, J. Randall, Levinson, Maxwell A., Manir, Sadnan Al, Clark, Timothy W., Fairchild, Karen D., Lake, Douglas E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8764068/
https://www.ncbi.nlm.nih.gov/pubmed/35039624
http://dx.doi.org/10.1038/s41746-021-00551-z
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author Niestroy, Justin C.
Moorman, J. Randall
Levinson, Maxwell A.
Manir, Sadnan Al
Clark, Timothy W.
Fairchild, Karen D.
Lake, Douglas E.
author_facet Niestroy, Justin C.
Moorman, J. Randall
Levinson, Maxwell A.
Manir, Sadnan Al
Clark, Timothy W.
Fairchild, Karen D.
Lake, Douglas E.
author_sort Niestroy, Justin C.
collection PubMed
description To seek new signatures of illness in heart rate and oxygen saturation vital signs from Neonatal Intensive Care Unit (NICU) patients, we implemented highly comparative time-series analysis to discover features of all-cause mortality in the next 7 days. We collected 0.5 Hz heart rate and oxygen saturation vital signs of infants in the University of Virginia NICU from 2009 to 2019. We applied 4998 algorithmic operations from 11 mathematical families to random daily 10 min segments from 5957 NICU infants, 205 of whom died. We clustered the results and selected a representative from each, and examined multivariable logistic regression models. 3555 operations were usable; 20 cluster medoids held more than 81% of the information, and a multivariable model had AUC 0.83. New algorithms outperformed others: moving threshold, successive increases, surprise, and random walk. We computed provenance of the computations and constructed a software library with links to the data. We conclude that highly comparative time-series analysis revealed new vital sign measures to identify NICU patients at the highest risk of death in the next week.
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spelling pubmed-87640682022-02-04 Discovery of signatures of fatal neonatal illness in vital signs using highly comparative time-series analysis Niestroy, Justin C. Moorman, J. Randall Levinson, Maxwell A. Manir, Sadnan Al Clark, Timothy W. Fairchild, Karen D. Lake, Douglas E. NPJ Digit Med Article To seek new signatures of illness in heart rate and oxygen saturation vital signs from Neonatal Intensive Care Unit (NICU) patients, we implemented highly comparative time-series analysis to discover features of all-cause mortality in the next 7 days. We collected 0.5 Hz heart rate and oxygen saturation vital signs of infants in the University of Virginia NICU from 2009 to 2019. We applied 4998 algorithmic operations from 11 mathematical families to random daily 10 min segments from 5957 NICU infants, 205 of whom died. We clustered the results and selected a representative from each, and examined multivariable logistic regression models. 3555 operations were usable; 20 cluster medoids held more than 81% of the information, and a multivariable model had AUC 0.83. New algorithms outperformed others: moving threshold, successive increases, surprise, and random walk. We computed provenance of the computations and constructed a software library with links to the data. We conclude that highly comparative time-series analysis revealed new vital sign measures to identify NICU patients at the highest risk of death in the next week. Nature Publishing Group UK 2022-01-17 /pmc/articles/PMC8764068/ /pubmed/35039624 http://dx.doi.org/10.1038/s41746-021-00551-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Niestroy, Justin C.
Moorman, J. Randall
Levinson, Maxwell A.
Manir, Sadnan Al
Clark, Timothy W.
Fairchild, Karen D.
Lake, Douglas E.
Discovery of signatures of fatal neonatal illness in vital signs using highly comparative time-series analysis
title Discovery of signatures of fatal neonatal illness in vital signs using highly comparative time-series analysis
title_full Discovery of signatures of fatal neonatal illness in vital signs using highly comparative time-series analysis
title_fullStr Discovery of signatures of fatal neonatal illness in vital signs using highly comparative time-series analysis
title_full_unstemmed Discovery of signatures of fatal neonatal illness in vital signs using highly comparative time-series analysis
title_short Discovery of signatures of fatal neonatal illness in vital signs using highly comparative time-series analysis
title_sort discovery of signatures of fatal neonatal illness in vital signs using highly comparative time-series analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8764068/
https://www.ncbi.nlm.nih.gov/pubmed/35039624
http://dx.doi.org/10.1038/s41746-021-00551-z
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