A Multi-Signal Convergence Model for Ultra-Precision Prediction of Cardiovascular Disease Onset: The ALERA v7.3 Engine

Current cardiovascular diagnostic frameworks rely on static biomarker thresholds that often result in a high rate of false positives due to physiological noise. This research presents the ALERA v7.3 engine, a deterministic prognostic framework that models cardiovascular failure as a systemic collapse governed by multi-signal convergence. By introducing the Cardiovascular Decay Constant (Vstandard) and a majority-vote logic gate, we simulate the kinetic erosion of hemodynamic stability. Our findings indicate that by requiring the convergence of at least two independent pathological signals (e.g., NT-proBNP, GLS, and Troponin), we achieve > 99% sensitivity and specificity. This model shifts the clinical paradigm from probabilistic risk scoring to deterministic temporal forecasting, identifying the exact window of transition to critical hemodynamic fragility.