How much information is needed to predict outcomes after cardiac arrest?

ABSTRACT:

Introduction

Prognostication after cardiac arrest is challenging but may be improved with machine learning (ML). ML accommodates large quantities of data, but in practice these arise from heterogeneous sources that may be challenging to assemble. We compared ML performance with combinations of registry, electronic health record (EHR) and electroencephalography (EEG) data to test if only a subset of sources was sufficient.

Methods

We performed a cohort study including consecutive adults treated between January 2010 and February 2022 at a single hospital who were unresponsive after cardiac arrest. We developed ML models to predict poor outcome (discharge Cerebral Performance Category (CPC) of 4 or 5) from various combinations of registry, EHR and EEG data. We developed sequent

SUBMITTER: Elmer J 

PROVIDER: S-EPMC12741859 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature