Machine Learning Approaches for Neurological Risk Stratification Within 6 Hours Of Cardiac Arrest

Authors

Richard R. Riker, Maine Medical CenterFollow
Qingchu Jin, Maine Medical CenterFollow
Hunter Williams
Teresa May, Maine Medical CenterFollow
David Seder, Maine Medical CenterFollow
Raimond L. Winslow

Files

Description

Background:

• Prognostication of neurological outcome should be delayed at least 72 hours after cardiac arrest. Early stratification of risk severity provides important information enabling improved medical decision making, research enrollment, and precision medicine approaches.

• We propose that electroencephalography (EEG)-derived indices are valid biomarkers of severity of neurological injury very early after cardiac arrest.

• This study was designed to assess the relative impact of early clinical data and processed EEG indices in the first 6 hours after recovery of spontaneous circulation (ROSC) after cardiac arrest.

Institution/Department

Maine Medical Center

Publication Date

5-2024

Publisher

MaineHealth

City

Portland, Maine

Keywords

Neurological Risk, Cardiac Arrest, EEG recordings, Suppression Ratio, Bispectral Index

Disciplines

Critical Care

Recommended Citation

Riker, Richard R.; Jin, Qingchu; Williams, Hunter; May, Teresa; Seder, David; and Winslow, Raimond L., "Machine Learning Approaches for Neurological Risk Stratification Within 6 Hours Of Cardiac Arrest" (2024). Costas T. Lambrew Research Retreat 2024. 1.
https://knowledgeconnection.mainehealth.org/lambrew-retreat-2024/1