title: The chronic kidney disease model: a general purpose model of disease progression and treatment.
abstract: BACKGROUND
  Chronic kidney disease (CKD) is the focus of recent national policy efforts; however, decision makers must account for multiple therapeutic options, comorbidities and complications. The objective of the Chronic Kidney Disease model is to provide guidance to decision makers. We describe this model and give an example of how it can inform clinical and policy decisions.
  
  
  METHODS
  Monte Carlo simulation of CKD natural history and treatment. Health states include myocardial infarction, stroke with and without disability, congestive heart failure, CKD stages 1-5, bone disease, dialysis, transplant and death. Each cycle is 1 month. Projections account for race, age, gender, diabetes, proteinuria, hypertension, cardiac disease, and CKD stage. Treatment strategies include hypertension control, diabetes control, use of HMG-CoA reductase inhibitors, use of angiotensin converting enzyme inhibitors, nephrology specialty care, CKD screening, and a combination of these. The model architecture is flexible permitting updates as new data become available. The primary outcome is quality adjusted life years (QALYs). Secondary outcomes include health state events and CKD progression rate.
  
  
  RESULTS
  The model was validated for GFR change/year -3.0 ± 1.9 vs. -1.7 ± 3.4 (in the AASK trial), and annual myocardial infarction and mortality rates 3.6 ± 0.9% and 1.6 ± 0.5% vs. 4.4% and 1.6% in the Go study. To illustrate the model's utility we estimated lifetime impact of a hypothetical treatment for primary prevention of vascular disease. As vascular risk declined, QALY improved but risk of dialysis increased. At baseline, 20% and 60% reduction: QALYs = 17.6, 18.2, and 19.0 and dialysis = 7.7%, 8.1%, and 10.4%, respectively.
  
  
  CONCLUSIONS
  The CKD Model is a valid, general purpose model intended as a resource to inform clinical and policy decisions improving CKD care. Its value as a tool is illustrated in our example which projects a relationship between decreasing cardiac disease and increasing ESRD.
authors.affiliation: Duke University, 3475 Erwin Rd, Wallace Clinic Ste #204, Durham, NC 27705, USA. lorlando@notes.duke.edu
authors.forename: Lori A
authors.initials: LA
authors.lastname: Orlando
authors.forename: Eric J
authors.initials: EJ
authors.lastname: Belasco
authors.forename: Uptal D
authors.initials: UD
authors.lastname: Patel
authors.forename: David B
authors.initials: DB
authors.lastname: Matchar
mesh_terms: D015897:Comorbidity; D018450:Disease Progression; D006801:Humans; D008962:Models, Theoretical; D009010:Monte Carlo Method; D019057:Quality-Adjusted Life Years; D051436:Renal Insufficiency, Chronic / Q000150:complications / Q000453:epidemiology / Q000628:therapy*; D012307:Risk Factors