Development and validation of a chronic kidney disease progression model using patient-level simulations

Abstract

Chronic disease progression models are available for several highly prevalent conditions. For chronic kidney disease (CKD), the scope of existing progression models is limited to the risk of kidney failure and major cardiovascular (CV) events. The aim of this project was to develop a comprehensive CKD progression model (CKD-PM) that simulates the risk of CKD progression and a broad range of complications in patients with CKD. A series of literature reviews informed the selection of risk factors and identified existing risk equations/algorithms for kidney replacement therapy (KRT), CV events, other CKD-related complications, and mortality. Risk equations and transition probabilities were primarily sourced from publications produced by large US and international CKD registries. A patient-level, state-transition model was developed with health states defined by the Kidney Disease Improving Global Outcomes categories. Model validation was performed by comparing predicted outcomes with observed outcomes in the source cohorts used in model development (internal validation) and other cohorts (external validation). The CKD-PM demonstrated satisfactory modeling properties. Accurate prediction of all-cause and CV mortality was achieved without calibration, while prediction of CV events through CKD-specific equations required implementation of a calibration factor to balance time-dependent versus baseline risk. Predicted annual changes in estimated glomerular filtration rate (eGFR) and urine albumin-creatinine ratio were acceptable in comparison to external values. A flexible eGFR threshold for KRT equations enabled accurate prediction of these events. This CKD-PM demonstrated reliable modeling properties. Both internal and external validation revealed robust outcomes.

Keywords

• Chronic kidney disease
• disease progression model
• microsimulation
• validation