A machine learning based approach to identify carotid subclinical atherosclerosis endotypes.

ABSTRACT:

Aims

To define endotypes of carotid subclinical atherosclerosis.

Methods and results

We integrated demographic, clinical, and molecular data (n = 124) with ultrasonographic carotid measurements from study participants in the IMPROVE cohort (n = 3340). We applied a neural network algorithm and hierarchical clustering to identify carotid atherosclerosis endotypes. A measure of carotid subclinical atherosclerosis, the c-IMTmean-max, was used to extract atherosclerosis-related features and SHapley Additive exPlanations (SHAP) to reveal endotypes. The association of endotypes with carotid ultrasonographic measurements at baseline, after 30 months, and with the 3-year atherosclerotic cardiovascular disease (ASCVD) risk was estimated by linear (β, SE) and Cox [hazard ratio (HR), 95

SUBMITTER: Chen QS 

PROVIDER: S-EPMC10730242 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature