A Machine-Learning Framework to Identify Distinct Phenotypes of Aortic Stenosis Severity.

ABSTRACT:

Objectives

The authors explored the development and validation of machine-learning models for augmenting the echocardiographic grading of aortic stenosis (AS) severity.

Background

In AS, symptoms and adverse events develop secondarily to valvular obstruction and left ventricular decompensation. The current echocardiographic grading of AS severity focuses on the valve and is limited by diagnostic uncertainty.

Methods

Using echocardiography (ECHO) measurements (ECHO cohort, n = 1,052), we performed patient similarity analysis to derive high-severity and low-severity phenogroups of AS. We subsequently developed a supervised machine-learning classifier and validated its performance with independent markers of disease severity obtained using computed tomography (CT) (CT co

SUBMITTER: Sengupta PP 

PROVIDER: S-EPMC8434951 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature