Development and Validation of Machine Learning-Based Models to Predict In-Hospital Mortality in Life-Threatening Ventricular Arrhythmias: Retrospective Cohort Study.

ABSTRACT:

Background

Life-threatening ventricular arrhythmias (LTVAs) are main causes of sudden cardiac arrest and are highly associated with an increased risk of mortality. A prediction model that enables early identification of the high-risk individuals is still lacking.

Objective

We aimed to build machine learning (ML)-based models to predict in-hospital mortality in patients with LTVA.

Methods

A total of 3140 patients with LTVA were randomly divided into training (n=2512, 80%) and internal validation (n=628, 20%) sets. Moreover, data of 2851 patients from another database were collected as the external validation set. The primary output was the probability of in-hospital mortality. The discriminatory ability was evaluated by the area under the receiver operating characteris

SUBMITTER: Li L 

PROVIDER: S-EPMC10687678 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature