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

Justin C Niestroy, View ORCID ProfileJ Randall Moorman, Maxwell A Levinson, Sadnan Al Manir, Timothy W Clark, Karen D Fairchild, Douglas E Lake
doi: https://doi.org/10.1101/2021.03.26.437138
Justin C Niestroy
1Department of Public Health Sciences, University of Virginia
2Center for Advanced Medical Analytics, University of Virginia
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J Randall Moorman
2Center for Advanced Medical Analytics, University of Virginia
3Department of Medicine, University of Virginia
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  • ORCID record for J Randall Moorman
  • For correspondence: rm3h@virginia.edu
Maxwell A Levinson
1Department of Public Health Sciences, University of Virginia
2Center for Advanced Medical Analytics, University of Virginia
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Sadnan Al Manir
1Department of Public Health Sciences, University of Virginia
2Center for Advanced Medical Analytics, University of Virginia
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Timothy W Clark
1Department of Public Health Sciences, University of Virginia
2Center for Advanced Medical Analytics, University of Virginia
6School of Data Science, University of Virginia
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Karen D Fairchild
2Center for Advanced Medical Analytics, University of Virginia
4Department of Pediatrics, University of Virginia
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Douglas E Lake
2Center for Advanced Medical Analytics, University of Virginia
3Department of Medicine, University of Virginia
5Department of Statistics, University of Virginia
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Abstract

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

Design We collected 0.5Hz heart rate and oxygen saturation vital signs of infants in the University of Virginia NICU from 2009 to 2019. We applied 4988 algorithmic operations from 11 mathematical families to random daily ten-minute segments. We clustered the results and selected a representative from each, and examined multivariable logistic regression models.

Setting Neonatal ICU

Patients 5957 NICU infants; 205 died.

Measurements and main results 3555 operations were usable; 20 cluster medoids held more than 81% of the information. A multivariable model had AUC 0.83. Five 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.

Conclusions Highly comparative time-series analysis revealed new vital sign measures to identify NICU patients at the highest risk of death in the next week.

Competing Interest Statement

JRM and DEL own stock in Medical Predictive Science Corporation, Charlottesville, VA; JRM owns stock and is an officer of Advanced Medical Predictive Devices, Diagnostics and Displays, Charlottesville, VA.

Footnotes

  • Conflict of interest: JRM and DEL own stock in Medical Predictive Science Corporation, Charlottesville, VA; JRM owns stock and is an officer of Advanced Medical Predictive Devices, Diagnostics and Displays, Charlottesville, VA. The other authors declare no competing interests.

  • Updated statistical analyses; enlarged clinical discussion.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted September 07, 2021.
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Discovery of signatures of fatal neonatal illness in vital signs using highly comparative time-series analysis
Justin C Niestroy, J Randall Moorman, Maxwell A Levinson, Sadnan Al Manir, Timothy W Clark, Karen D Fairchild, Douglas E Lake
bioRxiv 2021.03.26.437138; doi: https://doi.org/10.1101/2021.03.26.437138
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Discovery of signatures of fatal neonatal illness in vital signs using highly comparative time-series analysis
Justin C Niestroy, J Randall Moorman, Maxwell A Levinson, Sadnan Al Manir, Timothy W Clark, Karen D Fairchild, Douglas E Lake
bioRxiv 2021.03.26.437138; doi: https://doi.org/10.1101/2021.03.26.437138

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