Article

A health policy model of CKD: 1. Model construction, assumptions, and validation of health consequences.

RTI International, Research Triangle Park, NC 27709, USA.
American Journal of Kidney Diseases (Impact Factor: 5.76). 03/2010; 55(3):452-62. DOI: 10.1053/j.ajkd.2009.11.016
Source: PubMed

ABSTRACT A cost-effectiveness model that accurately represents disease progression, outcomes, and associated costs is necessary to evaluate the cost-effectiveness of interventions for chronic kidney disease (CKD).
We developed a microsimulation model of the incidence, progression, and treatment of CKD. The model was validated by comparing its predictions with survey and epidemiologic data sources.
US patients. MODEL, PERSPECTIVE, & TIMEFRAME: The model follows up disease progression in a cohort of simulated patients aged 30 until age 90 years or death. The model consists of 7 mutually exclusive states representing no CKD, 5 stages of CKD, and death. Progression through the stages is governed by a person's glomerular filtration rate and albuminuria status. Diabetes, hypertension, and other risk factors influence CKD and the development of CKD complications in the model. Costs are evaluated from the health care system perspective.
Usual care, including incidental screening for persons with diabetes or hypertension.
Progression to CKD stages, complications, and mortality.
The model provides reasonably accurate estimates of CKD prevalence by stage. The model predicts that 47.1% of 30-year-olds will develop CKD during their lifetime, with 1.7%, 6.9%, 27.3%, 6.9%, and 4.4% ending at stages 1-5, respectively. Approximately 11% of persons who reach stage 3 will eventually progress to stage 5. The model also predicts that 3.7% of persons will develop end-stage renal disease compared with an estimate of 3.0% based on current end-stage renal disease lifetime incidence.
The model synthesizes data from multiple sources rather than a single source and relies on explicit assumptions about progression. The model does not include acute kidney failure.
The model is well validated and can be used to evaluate the cost-effectiveness of CKD interventions. The model also can be updated as better data for CKD progression become available.

1 Bookmark
 · 
330 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: De novo postdonation renal diseases, such as glomerulonephritis or diabetic nephropathy, are infrequent and distinct from the loss of GFR at donation that all living kidney donors experience. Medical findings that increase risks of disease (e.g. microscopic hematuria, borderline hemoglobin A1C) often prompt donor refusal by centers. These risk factors are part of more comprehensive risks of low GFR and end-stage renal disease (ESRD) from kidney diseases in the general population that are equally relevant. Such data profile the ages of onset, rates of progression, prevalence and severity of loss of GFR from generically characterized kidney diseases. Kidney diseases typically begin in middle age and take decades to reach ESRD, at a median age of 64. Diabetes produces about half of yearly ESRD and even more lifetime near-ESRD. Such data predict that (1) 10- to 15-year studies will not capture the lifetime risks of postdonation ESRD; (2) normal young donors are at demonstrably higher risk than normal older candidates; (3) low-normal predonation GFRs become risk factors for ESRD when kidney diseases arise and (4) donor nephrectomy always increases individual risk. Such population-based risk data apply to all donor candidates and should be used to make acceptance standards and counseling more uniform and defensible.
    American Journal of Transplantation 02/2014; · 6.19 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Predicting the timing and number of end-stage renal disease (ESRD) cases from a population of individuals with pre-ESRD chronic kidney disease (CKD) has not previously been reported. The objective is to predict the timing and number of cases of ESRD occurring over the lifetime of a cohort of hypothetical CKD patients in the US based on a range of baseline estimated glomerular filtration rate (eGFR) values and varying rates of eGFR decline.
    International Journal of Nephrology and Renovascular Disease 01/2014; 7:271-80.
  • [Show abstract] [Hide abstract]
    ABSTRACT: AimIt is necessary to screen the people at high risk for proteinuria with an economic, reliable and convenient method. The aim of this study is to establish a new approach to predict 24-hour urine protein (24-hour UP) by routine laboratory assays.Methods Five centers were included and totally 4211 hospitalized patients were enrolled. All samples were assayed for dipstick protein (DSP), specific gravity (SG), 24-hour UP and serum albumin (ALB) simultaneously. 4211 patients were randomly divided into two groups for establishing and testing the equations. Equations were built by multiple log-linear regressions.Results(1) DSP is significantly correlated to 24-hour UP in a logarithmic pattern; (2) SG interprets 24-hour UP for specific DSP; (3) Equation 1 = 0.203 x 10 dummy-variable Fx [100 (SG-1)] -0.470; (4) Equation 2 =13.366 x 10 dummy-variable F x [100 (SG-1)] -0.547 x [ALB (g/L)] -1.130. The dummy-variable F had a point-to-point accordance to DSP (detailed in text).Conclusionscombination of DSP and SG can interpret normal-range proteinuria well; and helped by ALB, their interpretation for macro proteinuria is much improved. It is dependable and economic for routine urinalysis to evaluate pathological proteinuria by equation.
    Nephrology 07/2014; · 1.86 Impact Factor