Enhancing cardiovascular risk prediction through AI-enabled calcium-omics.

ABSTRACT:

Background

Coronary artery calcium (CAC) is a powerful predictor of major adverse cardiovascular events (MACE). Traditional Agatston score simply sums the calcium, albeit in a non-linear way, leaving room for improved calcification assessments that will more fully capture the extent of disease.

Objective

To determine if AI methods using detailed calcification features (i.e., calcium-omics) can improve MACE prediction.

Methods

We investigated additional features of calcification including assessment of mass, volume, density, spatial distribution, territory, etc. We used a Cox model with elastic-net regularization on 2457 CT calcium score (CTCS) enriched for MACE events obtained from a large no-cost CLARIFY program (ClinicalTrials.gov Identifier: NCT04075162). We employ

SUBMITTER: Hoori A 

PROVIDER: S-EPMC10473778 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature