Development and validation of echocardiography-based machine-learning models to predict mortality.

ABSTRACT:

Background

Echocardiography (echo) based machine learning (ML) models may be useful in identifying patients at high-risk of all-cause mortality.

Methods

We developed ML models (ResNet deep learning using echo videos and CatBoost gradient boosting using echo measurements) to predict 1-year, 3-year, and 5-year mortality. Models were trained on the Mackay dataset, Taiwan (6083 echos, 3626 patients) and validated in the Alberta HEART dataset, Canada (997 echos, 595 patients). We examined the performance of the models overall, and in subgroups (healthy controls, at risk of heart failure (HF), HF with reduced ejection fraction (HFrEF) and HF with preserved ejection fraction (HFpEF)). We compared the models' performance to the MAGGIC risk score, and examined the correlation between

SUBMITTER: Valsaraj A 

PROVIDER: S-EPMC10006431 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature