Abstract
Cardiac arrest is the most important cause of death worldwide. Often, those who survive have an increased mortality and disability risk that is mainly associated with the development of hypoxic-ischemic brain injury (HIBI). This review examines current methods and recent advancements in neuroprognostication after cardiac arrest, focusing on the multimodal approach recommended by current guidelines.
Recent studies have shown that a multimodal approach for neuroprognostication has the highest specificity to determine unfavorable outcomes after cardiac arrest. New biomarkers, such as neurofilament light chain alongside advancements in machine learning models, have shown promising results in predicting outcomes. Although several prognostic scoring systems have been developed to predict neurological outcomes as early as hospital admission, their prognostic efficacy is still being determined due to several associated limitations.
Although several strategies for improving neurological outcomes during and after cardiac arrest exist, HIBI remains the leading cause of disability among survivors. A multimodal approach, including at least two diagnostic modalities, is crucial for accurate prognostication. Emerging technologies, including machine learning models and biomarkers, offer potential improvements to existing prognostic strategies, emphasizing the need for consistent guideline adherence to optimize patient care.
Adela Bazbaz, Joseph Varon
