Predicting neurological outcomes after in-hospital cardiac arrests for patients with Coronavirus Disease 2019.

ABSTRACT:

Background

Machine learning models are more accurate than standard tools for predicting neurological outcomes in patients resuscitated after cardiac arrest. However, their accuracy in patients with Coronavirus Disease 2019 (COVID-19) is unknown. Therefore, we compared their performance in a cohort of cardiac arrest patients with COVID-19.

Methods

We conducted a retrospective analysis of resuscitation survivors in the Get With The Guidelines®-Resuscitation (GWTG-R) COVID-19 registry between February 2020 and May 2021. The primary outcome was a favorable neurological outcome, indicated by a discharge Cerebral Performance Category score ≤ 2. Pre- and peri-arrest variables were used as predictors. We applied our published logistic regression, neural network, and gradient boosted

SUBMITTER: Mayampurath A 

PROVIDER: S-EPMC9295318 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature