Digital signatures for early traumatic brain injury outcome prediction in the intensive care unit

Abstract Traumatic brain injury (TBI) is a leading neurological cause of death and disability across the world. Early characterization of TBI severity could provide a window for therapeutic intervention and contribute to improved outcome. We hypothesized that granular electronic health record data a...

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Autores principales:	Anil K. Palepu, Aditya Murali, Jenna L. Ballard, Robert Li, Samiksha Ramesh, Hieu Nguyen, Hanbiehn Kim, Sridevi Sarma, Jose I. Suarez, Robert D. Stevens
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8e1932ab8403438ba6b918f49dfeab32
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spelling	oai:doaj.org-article:8e1932ab8403438ba6b918f49dfeab322021-12-02T19:16:11ZDigital signatures for early traumatic brain injury outcome prediction in the intensive care unit10.1038/s41598-021-99397-42045-2322https://doaj.org/article/8e1932ab8403438ba6b918f49dfeab322021-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-99397-4https://doaj.org/toc/2045-2322Abstract Traumatic brain injury (TBI) is a leading neurological cause of death and disability across the world. Early characterization of TBI severity could provide a window for therapeutic intervention and contribute to improved outcome. We hypothesized that granular electronic health record data available in the first 24 h following admission to the intensive care unit (ICU) can be used to differentiate outcomes at discharge. Working from two ICU datasets we focused on patients with a primary admission diagnosis of TBI whose length of stay in ICU was ≥ 24 h (N = 1689 and 127). Features derived from clinical, laboratory, medication, and physiological time series data in the first 24 h after ICU admission were used to train elastic-net regularized Generalized Linear Models for the prediction of mortality and neurological function at ICU discharge. Model discrimination, determined by area under the receiver operating characteristic curve (AUC) analysis, was 0.903 and 0.874 for mortality and neurological function, respectively. Model performance was successfully validated in an external dataset (AUC 0.958 and 0.878 for mortality and neurological function, respectively). These results demonstrate that computational analysis of data routinely collected in the first 24 h after admission accurately and reliably predict discharge outcomes in ICU stratum TBI patients.Anil K. PalepuAditya MuraliJenna L. BallardRobert LiSamiksha RameshHieu NguyenHanbiehn KimSridevi SarmaJose I. SuarezRobert D. StevensNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Anil K. Palepu Aditya Murali Jenna L. Ballard Robert Li Samiksha Ramesh Hieu Nguyen Hanbiehn Kim Sridevi Sarma Jose I. Suarez Robert D. Stevens Digital signatures for early traumatic brain injury outcome prediction in the intensive care unit
description	Abstract Traumatic brain injury (TBI) is a leading neurological cause of death and disability across the world. Early characterization of TBI severity could provide a window for therapeutic intervention and contribute to improved outcome. We hypothesized that granular electronic health record data available in the first 24 h following admission to the intensive care unit (ICU) can be used to differentiate outcomes at discharge. Working from two ICU datasets we focused on patients with a primary admission diagnosis of TBI whose length of stay in ICU was ≥ 24 h (N = 1689 and 127). Features derived from clinical, laboratory, medication, and physiological time series data in the first 24 h after ICU admission were used to train elastic-net regularized Generalized Linear Models for the prediction of mortality and neurological function at ICU discharge. Model discrimination, determined by area under the receiver operating characteristic curve (AUC) analysis, was 0.903 and 0.874 for mortality and neurological function, respectively. Model performance was successfully validated in an external dataset (AUC 0.958 and 0.878 for mortality and neurological function, respectively). These results demonstrate that computational analysis of data routinely collected in the first 24 h after admission accurately and reliably predict discharge outcomes in ICU stratum TBI patients.
format	article
author	Anil K. Palepu Aditya Murali Jenna L. Ballard Robert Li Samiksha Ramesh Hieu Nguyen Hanbiehn Kim Sridevi Sarma Jose I. Suarez Robert D. Stevens
author_facet	Anil K. Palepu Aditya Murali Jenna L. Ballard Robert Li Samiksha Ramesh Hieu Nguyen Hanbiehn Kim Sridevi Sarma Jose I. Suarez Robert D. Stevens
author_sort	Anil K. Palepu
title	Digital signatures for early traumatic brain injury outcome prediction in the intensive care unit
title_short	Digital signatures for early traumatic brain injury outcome prediction in the intensive care unit
title_full	Digital signatures for early traumatic brain injury outcome prediction in the intensive care unit
title_fullStr	Digital signatures for early traumatic brain injury outcome prediction in the intensive care unit
title_full_unstemmed	Digital signatures for early traumatic brain injury outcome prediction in the intensive care unit
title_sort	digital signatures for early traumatic brain injury outcome prediction in the intensive care unit
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/8e1932ab8403438ba6b918f49dfeab32
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Digital signatures for early traumatic brain injury outcome prediction in the intensive care unit

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