title: Estimating time to ESRD using kidney failure risk equations: results from the African American Study of Kidney Disease and Hypertension (AASK).
abstract: BACKGROUND
  Planning for renal replacement therapy, such as referral for arteriovenous fistula placement and transplantation, often is guided by level of estimated glomerular filtration rate (eGFR). The use of risk equations might enable more accurate estimation of time to end-stage renal disease (ESRD), thus improving patient care.
  
  
  METHODS
  Prospective observational study.
  
  
  METHODS
  1,094 participants in the African American Study of Kidney Disease and Hypertension (AASK) cohort.
  
  
  METHODS
  Age, sex, urine protein-creatinine ratio ≥ 1g/g, APOL1 high-risk status, and 3-year antecedent eGFR decline.
  
  
  RESULTS
  Cumulative incidence of ESRD from 5 different starting points: eGFR of 30 and 15mL/min/1.73m(2) and 1-year ESRD risk of 5%, 10%, and 20%, estimated by a published 4-variable kidney failure risk equation.
  
  
  RESULTS
  566 participants developed eGFR of 30mL/min/1.73m(2), 244 developed eGFR of 15mL/min/1.73m(2), and 437, 336, and 259 developed 1-year ESRD risks of 5%, 10%, and 20%, respectively. The 1-year cumulative incidence of ESRD was 4.3% from eGFR of 30mL/min/1.73m(2), 49.0% from eGFR of 15mL/min/1.73m(2), 6.7% from 5% ESRD risk, 15.0% from 10% ESRD risk, and 29% from 20% ESRD risk. From eGFR of 30mL/min/1.73m(2), there were several risk factors that predicted ESRD risk. From eGFR of 15mL/min/1.73m(2), only level of proteinuria did; median time to ESRD was 9 and 19 months in those with higher and lower proteinuria, respectively. Median times were less variable from corresponding ESRD risk thresholds. For example, median times to ESRD from 20% ESRD risk were 22 and 25 months among those with higher and lower proteinuria, respectively.
  
  
  CONCLUSIONS
  Relatively homogeneous population of African Americans with hypertensive kidney disease.
  
  
  CONCLUSIONS
  Results of the present study suggest the potential benefit of incorporating kidney failure risk equations into clinical care, with selection of a specific threshold guided by its intended use.
authors.affiliation: Division of Nephrology, Johns Hopkins University School of Medicine, Baltimore, MD; Department of Epidemiology, Johns Hopkins University School of Public Health, Baltimore, MD; Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins University, Baltimore, MD. Electronic address: mgrams2@jhmi.edu.
authors.forename: Morgan E
authors.initials: ME
authors.lastname: Grams
authors.affiliation: University of Texas MD Anderson Cancer Center, Houston, TX.
authors.forename: Liang
authors.initials: L
authors.lastname: Li
authors.affiliation: Division of Clinical Epidemiology, University of Utah School of Medicine, Salt Lake City, UT.
authors.forename: Tom H
authors.initials: TH
authors.lastname: Greene
authors.affiliation: Department of Epidemiology, Johns Hopkins University School of Public Health, Baltimore, MD.
authors.forename: Adrienne
authors.initials: A
authors.lastname: Tin
authors.affiliation: Department of Epidemiology, Johns Hopkins University School of Public Health, Baltimore, MD.
authors.forename: Yingying
authors.initials: Y
authors.lastname: Sang
authors.affiliation: Department of Epidemiology, Johns Hopkins University School of Public Health, Baltimore, MD; Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins University, Baltimore, MD.
authors.forename: W H Linda
authors.initials: WH
authors.lastname: Kao
authors.affiliation: Division of Nephrology, Georgetown University School of Medicine, Washington, DC.
authors.forename: Michael S
authors.initials: MS
authors.lastname: Lipkowitz
authors.affiliation: Department of Medicine, Case Western Reserve University, Cleveland, OH.
authors.forename: Jackson T
authors.initials: JT
authors.lastname: Wright
authors.affiliation: Division of Nephrology, Geisinger Health System, Danville, PA.
authors.forename: Alex R
authors.initials: AR
authors.lastname: Chang
authors.affiliation: Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI; Department of Population Health Sciences, University of Wisconsin School of Medicine and Public Health, Madison, WI.
authors.forename: Brad C
authors.initials: BC
authors.lastname: Astor
authors.affiliation: Department of Epidemiology, Johns Hopkins University School of Public Health, Baltimore, MD; University of Texas MD Anderson Cancer Center, Houston, TX; Division of General Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, MD.
authors.forename: Lawrence J
authors.initials: LJ
authors.lastname: Appel
mesh_terms: D000328:Adult; D000368:Aged; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D001741:Black or African American / Q000208:ethnology; D018450:Disease Progression; D005500:Follow-Up Studies; D005919:Glomerular Filtration Rate; D006973:Hypertension / Q000175:diagnosis / Q000208:ethnology / Q000503:physiopathology; D007676:Kidney Failure, Chronic / Q000175:diagnosis / Q000208:ethnology / Q000503:physiopathology; D011446:Prospective Studies; D051437:Renal Insufficiency / Q000175:diagnosis / Q000208:ethnology / Q000503:physiopathology; D012307:Risk Factors
keywords: African American Study of Kidney Disease and Hypertension (AASK); End-stage renal disease (ESRD); clinical decision making; disease progression; disease trajectory; estimated glomerular filtration rate (eGFR); hypertensive kidney disease; kidney failure risk equations; prognosis; proteinuria; risk