Artificial intelligence; Biomarkers; Cardiac arrest; Clinical outcomes; Machine learning; Multiomics; Prognosis; Emergency Medicine; Critical Care and Intensive Care Medicine; Physiology (medical)
Abstract :
[en] 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.
Disciplines :
Anesthesia & intensive care
Author, co-author :
Stopa, Victoria; Cardiovascular Research Unit, Department of Precision Health, Luxembourg Institute of Health, 1A-B rue Edison, 1445, Strassen, Luxembourg
Lileikyte, Gabriele; Department of Clinical Sciences Lund, Anaesthesia and Intensive Care, Lund University, Helsingborg Hospital, Svart-brödragränden 3, 251 87, Helsingborg, Sweden
Bakochi, Anahita; Swedish National Infrastructure for Biological Mass Spectrometry (BioMS), Lund University, Lund, Sweden ; Department of Clinical Sciences Lund, Infection Medicine, Lund University, Lund, Sweden
Agarwal, Prasoon; Science for Life Laboratory, Division of Occupational and Environmental Medicine, Department of Laboratory Medicine, National Bioinformatics Infrastructure Sweden (NBIS), Lund University, 22362, Lund, Sweden
Beske, Rasmus; Department of Cardiology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark ; Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
STAMMET, Pascal ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Life Sciences and Medicine (DLSM) > Medical Education ; Department of Anesthesia and Intensive Care Medicine, Centre Hospitalier de Luxembourg, Luxembourg, Luxembourg
Hassager, Christian; Department of Cardiology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark ; Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
Årman, Filip; Swedish National Infrastructure for Biological Mass Spectrometry (BioMS), Lund University, Lund, Sweden
Nielsen, Niklas; Department of Clinical Sciences Lund, Anaesthesia and Intensive Care, Lund University, Helsingborg Hospital, Svart-brödragränden 3, 251 87, Helsingborg, Sweden
DEVAUX, Yvan ; University of Luxembourg ; Cardiovascular Research Unit, Department of Precision Health, Luxembourg Institute of Health, 1A-B rue Edison, 1445, Strassen, Luxembourg. yvan.devaux@lih.lu
Horizon 2020 Framework Programme Fonds National de la Recherche Luxembourg Ministère de l'Education Nationale, de la Formation professionnelle, de l'Enseignement Supérieur et de la Recherche Scientifique Heart Foundation-Daniel Wagner Novo Nordisk Lundbeck Foundation The Danish Heart Foundation
Funding text :
Y.D. is funded by the EU Horizon 2020 project COVIRNA (grant agreement # 101016072), the National Research Fund (grants # C14/BM/8225223, C17/BM/11613033 and COVID-19/2020-1/14719577/miRCOVID), the Ministry of Higher Education and Research, and the Heart Foundation-Daniel Wagner of Luxembourg. V.S is funded by the Heart Foundation-Daniel Wagner of Luxembourg (YMCA project).C.H. is funded by The Novo Nordisk Foundation, The Lundbeck Foundation and The Danish Heart Foundation.
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