Machine Learning Prediction for Prognosis of Patients With Aortic Stenosis.

ABSTRACT:

Background

Aortic valve stenosis of any degree is associated with poor outcomes.

Objectives

The authors aimed to develop a risk prediction model for aortic stenosis (AS) prognosis using machine learning techniques.

Methods

A prognostic algorithm was developed using an AS registry of 10,407 patients undergoing echocardiography between 2008 and 2020. Clinical, echocardiographic, laboratory, and medication data were used to train and test a time-to-event model, the random survival forest (RSF), for AS patient's prognosis. The composite outcome included aortic valve replacement or mortality. The SHapley Additive exPlanations method attributed the importance of variables and provided personalized risk assessment. The algorithm was validated in 2 external cohorts of 11,738

SUBMITTER: Shimoni S 

PROVIDER: S-EPMC11450950 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature