Journal of CardioVascular Insights

Abstract

Conventional cardiovascular risk calculators rely on static clinical variables that fail to capture the dynamic,

multi-layered physiological alterations driving Major Adverse Cardiovascular Events (MACE). This paper

introduces a novel, clinical-grade hybrid systems cardiology framework that continuously stratifies risk and

guides precision preventive interventions. By combining high-throughput plasma proteomics, single-cell

multi-omics (scRNA-seq and scATAC-seq), polygenic risk scores (PRS), and continuous physiological streams

from medical-grade wearable biosensors, the framework constructs a dynamic digital twin of an individual's

cardiovascular network.These heterogeneous data layers are processed through an Explainable Machine Learning (XML) pipeline

utilizing deep neural networks alongside Shapley Additive exPlanations (SHAP). This pipeline provides

interpretable, feature-level risk attributions that uncover occult atherothrombotic activity long before clinical

symptoms present