External Validation and Bayesian Forecasting of Rivaroxaban Population Pharmacokinetic Models in Older Chinese Patients with Nonvalvular Atrial Fibrillation.

Background: Population pharmacokinetic (PopPK) models have been developed to characterize rivaroxaban exposure; however, their transferability across different clinical populations remains uncertain. This study aimed to externally evaluate published rivaroxaban PopPK models in older Chinese patients with nonvalvular atrial fibrillation (NVAF) and assess whether Bayesian forecasting could improve individual concentration prediction. Methods: Six published rivaroxaban PopPK models were evaluated using an independent cohort from the RIVA-GAP study. Predictive performance was assessed using prediction error metrics reflecting bias, precision, and prediction coverage, together with normalized prediction distribution error (NPDE) diagnostics. Bayesian forecasting was performed using paired steady-state trough-peak concentrations to evaluate changes in individual peak concentration prediction. Results: A total of 135 patients with 257 rivaroxaban concentration observations were included for external validation, and 122 patients with paired trough-peak samples were included for Bayesian forecasting. Predictive performance varied substantially among the six models, and none consistently met predefined acceptance criteria. Model A showed comparatively lower prediction bias among the evaluated models but still demonstrated limited precision (MDPE 33.64%, MDAPE 53.72%, F30 31.52%). Bayesian forecasting reduced the MDIPE of Model A from 33.64% to 12.38%, but MAIPE remained 37.33% and IF30 was 41.8%. Similar model-dependent effects were observed across the other models. Conclusion: Published rivaroxaban PopPK models demonstrated heterogeneous transferability in older Chinese patients with NVAF. Bayesian forecasting using a single trough concentration provided modest and model-dependent improvement in concentration prediction but did not consistently overcome limitations of the underlying models. Clinical Trial/Study Registration: Chinese Clinical Trial Registry, ChiCTR2300074934; registered 21 August 2023.