OBJECTIVES Autosomal dominant polycystic kidney disease (ADPKD) is a major cause of end-stage renal disease (ESRD) affecting approximately 4 per 10,000 people in Europe. There is a paucity of research regarding the natural history of ADPKD progression. This study aimed to utilise the results of a systematic literature review characterising predictors of ADPKD progression to construct a natural history disease model for ADPKD. METHODS An individual patient-level lifetime simulation was developed in Microsoft Excel, driven by baseline and time-dependent age, estimated glomerular filtration rate (eGFR) and total kidney volume (TKV). Rates of progression were informed by a large naturalistic study. Dialysis modality, transplant status and disease-specific mortality were also modelled. Relevant ADPKD complications were stratified by chronic kidney disease stages. Modification of disease progression rate was investigated in order to assess the potential of the model for evaluating treatment interventions.  RESULTS On visual inspection, modelled and published eGFR trajectories for the general ADPKD patient were consistent (median age at ESRD of approximately 55 years). When patients are stratified by baseline TKV the model predicts variable rates of progression to ESRD, aligning with the assertion that the baseline TKV is the most important prognostic indicator for ADPKD progression. Modification of the risk equations to incorporate the impact of an intervention has shown promise to estimate important outcomes such as delay to ESRD. CONCLUSIONS The model has demonstrated both face and predictive validity and is capable of predicting outcomes consistent with those reported in the ADPKD literature. It represents the first model capable of informing on important clinical outcomes relevant to both clinicians and patients, such as time to ESRD, with the potential to evaluate the long-term impact of treatment interventions on ADPKD progression.