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Multiomic biomarkers after cardiac arrest

Stopa, Victoria ; Lileikyte, Gabriele LU orcid ; Bakochi, Anahita LU orcid ; Agarwal, Prasoon LU orcid ; Beske, Rasmus ; Stammet, Pascal ; Hassager, Christian ; Årman, Filip LU ; Nielsen, Niklas LU and Devaux, Yvan (2024) In Intensive Care Medicine Experimental 12(1).
Abstract

Cardiac arrest is a sudden cessation of heart function, leading to an abrupt loss of blood flow and oxygen to vital organs. This life-threatening emergency requires immediate medical intervention and can lead to severe neurological injury or death. Methods and biomarkers to predict neurological outcome are available but lack accuracy. Such methods would allow personalizing healthcare and help clinical decisions. Extensive research has been conducted to identify prognostic omic biomarkers of cardiac arrest. With the emergence of technologies allowing to combine different levels of omics data, and with the help of artificial intelligence and machine learning, there is a potential to use multiomic signatures as prognostic biomarkers after... (More)

Cardiac arrest is a sudden cessation of heart function, leading to an abrupt loss of blood flow and oxygen to vital organs. This life-threatening emergency requires immediate medical intervention and can lead to severe neurological injury or death. Methods and biomarkers to predict neurological outcome are available but lack accuracy. Such methods would allow personalizing healthcare and help clinical decisions. Extensive research has been conducted to identify prognostic omic biomarkers of cardiac arrest. With the emergence of technologies allowing to combine different levels of omics data, and with the help of artificial intelligence and machine learning, there is a potential to use multiomic signatures as prognostic biomarkers after cardiac arrest. This review article delves into the current knowledge of cardiac arrest biomarkers across various omic fields and suggests directions for future research aiming to integrate multiple omics data layers to improve outcome prediction and cardiac arrest patient’s care.

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author
; ; ; ; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Artificial intelligence, Biomarkers, Cardiac arrest, Clinical outcomes, Machine learning, Multiomics, Prognosis
in
Intensive Care Medicine Experimental
volume
12
issue
1
article number
83
publisher
Springer Nature
external identifiers
  • pmid:39331333
  • scopus:85205697036
ISSN
2197-425X
DOI
10.1186/s40635-024-00675-y
language
English
LU publication?
yes
id
a59c9805-49cf-4588-b656-8b2c18bf4d47
date added to LUP
2024-11-27 14:42:51
date last changed
2025-07-24 10:24:35
@article{a59c9805-49cf-4588-b656-8b2c18bf4d47,
  abstract     = {{<p>Cardiac arrest is a sudden cessation of heart function, leading to an abrupt loss of blood flow and oxygen to vital organs. This life-threatening emergency requires immediate medical intervention and can lead to severe neurological injury or death. Methods and biomarkers to predict neurological outcome are available but lack accuracy. Such methods would allow personalizing healthcare and help clinical decisions. Extensive research has been conducted to identify prognostic omic biomarkers of cardiac arrest. With the emergence of technologies allowing to combine different levels of omics data, and with the help of artificial intelligence and machine learning, there is a potential to use multiomic signatures as prognostic biomarkers after cardiac arrest. This review article delves into the current knowledge of cardiac arrest biomarkers across various omic fields and suggests directions for future research aiming to integrate multiple omics data layers to improve outcome prediction and cardiac arrest patient’s care.</p>}},
  author       = {{Stopa, Victoria and Lileikyte, Gabriele and Bakochi, Anahita and Agarwal, Prasoon and Beske, Rasmus and Stammet, Pascal and Hassager, Christian and Årman, Filip and Nielsen, Niklas and Devaux, Yvan}},
  issn         = {{2197-425X}},
  keywords     = {{Artificial intelligence; Biomarkers; Cardiac arrest; Clinical outcomes; Machine learning; Multiomics; Prognosis}},
  language     = {{eng}},
  month        = {{09}},
  number       = {{1}},
  publisher    = {{Springer Nature}},
  series       = {{Intensive Care Medicine Experimental}},
  title        = {{Multiomic biomarkers after cardiac arrest}},
  url          = {{http://dx.doi.org/10.1186/s40635-024-00675-y}},
  doi          = {{10.1186/s40635-024-00675-y}},
  volume       = {{12}},
  year         = {{2024}},
}