A Clustering-Based Grey Modeling Approach for Tacrolimus Concentration Prediction Under Sparse Therapeutic Drug...

Tacrolimus therapeutic drug monitoring after liver transplantation is characterized by significant interindividual variability and sparse, irregularly sampled concentration data, which limits the applicability of conventional pharmacokinetic and data-intensive modelling approaches.

We developed a hierarchical prediction framework integrating density-based spatial clustering of applications with noise-derived patient stratification with self-memory algorithm-based nonlinear grey Bernoulli model (SA-NGBM) using retrospective data from 129 liver transplant recipients. Patients were stratified into homogeneous subgroups using routinely available clinical indicators, and cluster-specific SA-NGBM models calibrated on representative patients were used to predict subsequent tacrolimus trough concentrations. Performance was further evaluated in an independent same-center validation cohort of 60 patients.

In the development cohort, the overall mean absolute relative prediction error for the next tacrolimus concentration decreased from 41.4% with the nonclustered baseline to 21.2% with the clustered SA-NGBM framework. In the independent validation cohort, consistent performance gains were observed, with the mean absolute relative prediction error decreasing from 56.8% to 27.3% in the largest patient subgroup. Full longitudinal concentration profiles were required for only 4 representative patients.

Overall, this clustered SA-NGBM framework reduces prediction error under sparse and irregular therapeutic drug monitoring conditions and provides a data-efficient stratified modelling strategy. However, further refinement and prospective validation are required before clinical implementation.