Abstract
Introduction: Determining CKD stage and disease progression based on eGFR in primary care is complicated by the fact that the measurements are irregularly sampled and influenced by both genuine physiological changes and external factors. Models used for these purposes would ideally capture both short- (for staging) and long-term (for progression) trends. However, existing regression algorithms such as linear, polynomial and Gaussian process regression either cannot account for these challenges or do not satisfy the key clinical requirements of providing an easily interpretable model that can elucidate short- and long-term trends. In order to balance interpretability and flexibility, an extension to broken-stick regression models is proposed in order to make them more suitable for modelling clinical time series.