Machine learning validation through decision tree analysis of the Epidemiology-Based Mortality Score in Status Epilepticus.

ABSTRACT:

Objective

This study was undertaken to validate the accuracy of the Epidemiology-Based Mortality Score in Status Epilepticus (EMSE) in predicting the risk of death at 30 days in a large cohort of patients with status epilepticus (SE) using a machine learning system.

Methods

We included consecutive patients with SE admitted from 2013 to 2021 at Modena Academic Hospital. A decision tree analysis was performed using the 30-day mortality as a dependent variable and the EMSE predictors as input variables. We evaluated the accuracy of EMSE in predicting 30-day mortality using the area under the receiver operating characteristic curve (AUC ROC), with 95% confidence interval (CI). We performed a subgroup analysis on nonhypoxic SE.

Results

A total of 698 patients with SE were

SUBMITTER: Brigo F 

PROVIDER: S-EPMC9804228 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature