Using Machine Learning to Predict Adverse Events in Patients Undergoing Transcatheter Left Atrial Appendage Occlusion.

ABSTRACT:

Background

Standard cardiovascular risk models generally have limited performance and cannot predict rare outcomes. Machine learning may overcome these limitations.

Objectives

This study sought to determine how predictive performance varies for standard and machine learning methods in the National Cardiovascular Data Registry Left Atrial Appendage Occlusion (LAAO) Registry.

Methods

Logistic regression (LR), least absolute shrinkage and selection operator (LASSO), and eXtreme Gradient Boosting (XGBoost) were used to predict combined in-hospital major adverse events (MAEs) and individual events in patients undergoing transcatheter LAAO. Randomly selected 70% development and 30% validation cohorts were used for model creation and assessment with 16 variables from the pre

SUBMITTER: Faridi KF 

PROVIDER: S-EPMC12859245 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature