Risk prediction score for clinical outcome in atrial fibrillation and stable coronary artery disease.

ABSTRACT:

Objective

Antithrombotic therapy is essential for patients with atrial fibrillation (AF) and stable coronary artery disease (CAD) because of the high risk of thrombosis, whereas a combination of antiplatelets and anticoagulants is associated with a high risk of bleeding. We sought to develop and validate a machine-learning-based model to predict future adverse events.

Methods

Data from 2215 patients with AF and stable CAD enrolled in the Atrial Fibrillation and Ischaemic Events With Rivaroxaban in Patients With Stable Coronary Artery Disease trial were randomly assigned to the development and validation cohorts. Using the random survival forest (RSF) and Cox regression models, risk scores were developed for net adverse clinical events (NACE) defined as all-cause death, myoca

SUBMITTER: Ishii M 

PROVIDER: S-EPMC10186465 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature