From Real-World Data to Virtual Intervention: A Probabilistic Neural Network for Simulating Kidney Function Preservation via Proteinuria Reduction

Predicting the long-term kidney function decline is critical for timely intervention but remains challenging. While the urinary protein-to-creatinine ratio (uPCR) is a potential surrogate endpoint, its short-term reduction's link to long-term nephroprotection requires investigation. This study aimed to develop a probabilistic neural network model to capture both the estimated glomerular filtration rate (eGFR) slope and its uncertainty based on baseline clinical characteristics. Using a retrospective dataset, we designed a neural network to output a predictive distribution (mean and standard deviation {sigma}) for the eGFR slope. SHAP (SHapley Additive exPlanations) was used for model interpretation, and a simulation study quantified the impact of uPCR reduction. In the validation set, the model achieved a Pearson's correlation coefficient of 0.56 and an RMSE of 2.81 ml/min/1.73m^2/year between predicted and actual slopes. SHAP analysis identified uPCR as the most potent predictor, with higher baseline levels associated with a more rapid eGFR decline. Furthermore, a simulated 62% uPCR reduction demonstrated a significant improvement in the predicted eGFR slope, an effect most pronounced in patients with high baseline uPCR. This proof-of-concept study reinforces the critical role of uPCR in predicting eGFR slope and suggests its reduction may contribute to long-term kidney function preservation, warranting validation in larger, diverse real-world datasets.