Multiomic biomarkers after cardiac arrest

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 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.

Keywords: Artificial intelligence; Biomarkers; Cardiac arrest; Clinical outcomes; Machine learning; Multiomics; Prognosis.

Fig. 1

Multicomponent approach for prognostication after cardiac arrest. A multicomponent approach is essential to predict neurological outcome after cardiac arrest (good or poor neurological outcome). Integration of novel omics data with clinical data, classical biomarkers, electrophysiological tests and imaging data, can improve prognostication and guide treatment strategies. Neuron-specific enolase (NSE), calcium binding protein beta (S100b), the glial fibrillary acidic protein (GFAP), neurofilament light (Nf-L), cardiopulmonary resuscitation (CPR), return of spontaneous circulation (ROSC), cerebral performance category score (CPC), magnetic resonance imaging (MRI), computed tomography scanner of the brain (CT scan), electroencephalogram (EEG), somatosensory evoked potential (SSEP)