Spatio-temporal distribution, prediction and relationship of three major acute cardiovascular events: Out-of-hospital cardiac arrest, ST-elevation myocardial infarction and stroke.

ABSTRACT:

Background

Predicting the incidence of time-sensitive cardiovascular diseases like out-of-hospital cardiac arrest (OHCA), ST-elevation myocardial infarction (STEMI), and stroke can reduce time to treatment and improve outcomes. This study analysed the spatio-temporal distribution of OHCAs, STEMIs, and strokes, their spatio-temporal correlation, and the performance of different prediction algorithms.

Methods

Adults who experienced an OHCA, STEMI, or stroke in Canton Ticino, Switzerland from 2005 to 2022 were included. Datasets were divided into training and validation samples. To estimate and predict the yearly per-capita population incidences of OHCA, STEMI, and stroke, the integrated nested Laplace approximation (INLA), machine learning meta model (MLMM), the Naïve predicti

SUBMITTER: Auricchio A 

PROVIDER: S-EPMC11550346 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature