Multimodal Clinical Biomarker Integration for Calibrated Cardiovascular Risk Stratification
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Abstract
Cardiovascular disease remains the leading cause of morbidity and mortality globally, necessitating precise and robust methods for risk stratification. Traditional clinical risk models often rely on a limited set of demographic and unimodal physiological parameters, which frequently fail to capture the complex, heterogeneous pathophysiology of acute and chronic cardiovascular events. This paper presents a comprehensive investigation into the integration of multimodal clinical biomarkers spanning genomic, proteomic, metabolomic, imaging, and electronic health record data to achieve highly calibrated cardiovascular risk stratification. By employing advanced data harmonization and fusion techniques, this study evaluates the incremental predictive value of combining orthogonal biomarker modalities compared to standard clinical baselines. Furthermore, the research emphasizes the critical role of statistical calibration in translating raw algorithmic outputs into clinically actionable probability estimates. Through an extensive empirical analysis of a large-scale retrospective cohort, the findings demonstrate that the multimodal integration framework significantly enhances both discriminative power and calibration accuracy, leading to substantial improvements in patient reclassification across established risk thresholds. The results underscore the necessity of holistic, multi-domain biomarker profiling in personalized cardiovascular medicine, offering a foundational framework for the next generation of clinical decision support systems.
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