title: A simple prediction score for kidney disease in the Korean population.
abstract: OBJECTIVE
  Screening algorithms for chronic kidney disease have been developed and validated in American populations. Given the worldwide burden of kidney disease, developing algorithms for populations outside the USA is needed.
  
  
  METHODS
  Using simple, non-invasive questions, we developed a prediction model for chronic kidney disease from national population samples in Korea. The Korean National Health and Nutrition Examination Survey (n = 6565) was used for model development while validation was performed in two independent population samples, internal (n = 2921) and external datasets (n = 8166). Chronic kidney disease was defined as glomerular filtration rate < 60 mL/min per 1.73 m(2).
  
  
  RESULTS
  Seven factors - age, female gender, anaemia, hypertension, diabetes mellitus, cardiovascular disease and proteinuria - were significantly associated with prevalent chronic kidney disease. Integer scores were assigned to variables based on the magnitude of associations: 2 for age 50-59 years, 3 for age 60-69 years and 4 for age 70 years or older, and 1 for female gender, anaemia, hypertension, diabetes, proteinuria and cardiovascular dis ase. Based on the Youden index, a value of 4 or greater defined a high risk population with sensitivity 89%, specificity 71%, and positive predictive value 19%, and negative predictive value 99%. The area under the curve was 0.83 for the development set, and 0.87 and 0.78 in the two validation datasets.
  
  
  CONCLUSIONS
  This prediction algorithm, weighted towards common non-invasive variables, had good performance characteristics in an Asian population, and provides new evidence of the similarity of the algorithms for Western and Eastern populations.
authors.affiliation: Department of Preventive Medicine, Chonbuk National University Medical School, Jeonju, Korea.
authors.forename: Keun-Sang
authors.initials: KS
authors.lastname: Kwon
authors.forename: Heejung
authors.initials: H
authors.lastname: Bang
authors.forename: Andrew S
authors.initials: AS
authors.lastname: Bomback
authors.forename: Dai-Ha
authors.initials: DH
authors.lastname: Koh
authors.forename: Jung-Ho
authors.initials: JH
authors.lastname: Yum
authors.forename: Ju-Hyung
authors.initials: JH
authors.lastname: Lee
authors.forename: Sik
authors.initials: S
authors.lastname: Lee
authors.forename: Sung K
authors.initials: SK
authors.lastname: Park
authors.forename: Keun-Young
authors.initials: KY
authors.lastname: Yoo
authors.forename: Sue K
authors.initials: SK
authors.lastname: Park
authors.forename: Soung-Hoon
authors.initials: SH
authors.lastname: Chang
authors.forename: Hyun-Sul
authors.initials: HS
authors.lastname: Lim
authors.forename: Joong Myung
authors.initials: JM
authors.lastname: Choi
authors.forename: Abhijit V
authors.initials: AV
authors.lastname: Kshirsagar
mesh_terms: D000368:Aged; D000465:Algorithms; D002908:Chronic Disease; D005260:Female; D006801:Humans; D007674:Kidney Diseases / Q000175:diagnosis* / Q000209:etiology; D007723:Korea; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D012307:Risk Factors