Prediction model based on machine learning for short- and long-term adverse events in left atrial appendage closure.

ABSTRACT:

Background

At present, the prediction of adverse events (AE) had practical significance in clinic and the accuracy of AE prediction model after left atrial appendage closure (LAAC) needed to be improved. To identify a good prediction model based on machine learning for short- and long-term AE after LAAC.

Methods

In this study, 869 patients were included from the Department of Cardiovascular Medicine of Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital during 2017 and 2021. Univariate and multivariate analyses were conducted for short-term AE after LAAC to determine possible risk factors related with AE. We compared 8 machine learning algorithms for prediction short-term AE, and XGBoost was found to have the best performance. In addition, Cox-regr

SUBMITTER: Zhang X 

PROVIDER: S-EPMC9264065 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature