Traditional risk stratification tools, such as the PREVENT (Predicting Risk of cardiovascular disease EVENTs) equations, estimate 10-year risk using static baseline variables. Simply adjusting the UACR input in the PREVENT calculator offers only a crude approximation and does not capture the dynamic, nonlinear effects of ongoing pharmacological treatment, biological hysteresis, and competing risks. To address these limitations, we will develop a high-fidelity digital twin simulation framework powered by the Aeterna Deep computational engine. This in silico trial will project 10-year cardio-renal outcomes for finerenone, empagliflozin, and their combination, while quantifying the mechanistic contribution of UACR reduction relative to other cardio-renal factors. The reference population will be defined according to the CONFIDENCE trial criteria, including adults with chronic kidney disease (eGFR 30-90 ml/min/1.73 m²), type 2 diabetes, and persistent albuminuria (UACR 100-5000 mg/g), receiving maximally tolerated renin-angiotensin system inhibitors. From this base, a synthetic cohort of 100,000 digital twins will be constructed using a four phase instantiation framework designed to ensure biological plausibility. First, marginal distributions for key demographic and clinical variables (age, sex, eGFR, UACR, blood pressure, HbA1c) will be parameterized from validated real-world datasets. Second, nonlinear physiological interdependencies will be reconstructed through multivariate coupling functions to preserve the in vivo covariance structure. Third, individual phenotypes will be generated via high-dimensional stochastic sampling to capture the full range of cardio renal metabolic trajectories. Finally, all instantiated profiles will undergo thermodynamic and biophysical truncation, whereby parameter combinations violating conservation principles or exceeding human homeostatic limits will be excluded and regenerated, ensuring that only physiologically viable digital entities will be retained for downstream analyses.
