Predicting survival and neurological outcome in out-of-hospital cardiac arrest using machine learning: the SCARS model.

ABSTRACT:

Background

A prediction model that estimates survival and neurological outcome in out-of-hospital cardiac arrest patients has the potential to improve clinical management in emergency rooms.

Methods

We used the Swedish Registry for Cardiopulmonary Resuscitation to study all out-of-hospital cardiac arrest (OHCA) cases in Sweden from 2010 to 2020. We had 393 candidate predictors describing the circumstances at cardiac arrest, critical time intervals, patient demographics, initial presentation, spatiotemporal data, socioeconomic status, medications, and comorbidities before arrest. To develop, evaluate and test an array of prediction models, we created stratified (on the outcome measure) random samples of our study population. We created a training set (60% of data), evaluation

SUBMITTER: Hessulf F 

PROVIDER: S-EPMC9945645 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature