Detection of cardiac amyloidosis using machine learning on routine echocardiographic measurements.

ABSTRACT:

Background

Cardiac amyloidosis (CA) is an underdiagnosed, progressive and lethal disease. Machine learning applied to common measurements derived from routine echocardiogram studies can inform suspicion of CA.

Objectives

Our objectives were to test a random forest (RF) model in detecting CA.

Methods

We used 3603 echocardiogram studies from 636 patients at Cedars-Sinai Medical Center to train an RF model to predict CA from echocardiographic parameters. 231 patients with CA were compared with 405 control patients with negative pyrophosphate scans or clinical diagnosis of hypertrophic cardiomyopathy. 19 common echocardiographic measurements from echocardiogram reports were used as input into the RF model. Data was split by patient into a training data set of 2882 studies

SUBMITTER: Chang RS 

PROVIDER: S-EPMC11667434 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature