This review provides an overview of how chronic kidney disease (CKD) is typically modelled, with glomerular filtration rate (GFR) and albuminuria, respectively, typically utilised as the key prognostic factor within CKD and diabetes model frameworks.

Most of the models identified were Markov models and/or utilised input data at cohort mean levels, and many of the current methods did not explicitly consider patient heterogeneity or underlying disease aetiology, except for diabetes, providing limited clinical rationale for the choice of model design.

Given the heterogenous nature of individual CKD patients’ characteristics and clinical prognoses, a model structure designed around the prediction of individual patients’ GFR trajectories may be preferred over cohort-based modelling frameworks when simulating patients with CKD. However, model choice should be informed and justified based on clinical rationale and availability of data to ensure validity of model results.