Hemodynaomics Map: A Machine Learning-Derived Functional Hemodynamic Framework for Mechanism-Guided Hypertension Management

Background Hypertension is a disorder of integrated cardiovascular physiology driven by cardiac, vascular, and volume dysregulation. Current clinical management remains largely empirical, limiting therapeutic precision. Objectives We introduce Hemodynaomics, a functional omics framework in cardiovascular medicine, and evaluate a machine learning-derived Hemodynaomics Map for mechanism-guided antihypertensive therapy. Methods Large-scale noninvasive hemodynamic data from a generally healthy Chinese adult population were used to train machine learning models to generate individualized reference distributions for seven key parameters (SBP, DBP, HR, CI, AS, SVRI, TBR). Deviations were visualized as standardized histograms linked to targeted drug classes. Clinical effectiveness was evaluated in three randomized controlled trials (total n = 484) comparing Hemodynaomics-guided therapy with standard care. Results Relative to standard care, Hemodynaomics-guided therapy achieved significant reductions in office (-6.1/-3.2 mmHg), home (-8.2/-4.8 mmHg), and ambulatory (-8.7/-4.3 mmHg) blood pressure (all p < 0.05) without increasing medication burden. Mechanism-guided management was associated with broader normalization of cardiac output, vascular resistance, arterial stiffness, and volume parameters. Conclusions Hemodynaomics provides a structured, machine learning-derived framework that translates integrated hemodynamic physiology into standardized therapeutic guidance. By aligning treatment with dominant physiologic drivers, it enables mechanism-guided hypertension management and establishes a scalable foundation for AI-assisted precision cardiovascular care.