title: Validity of administrative database coding for kidney disease: a systematic review.
abstract: BACKGROUND
  Information in health administrative databases increasingly guides renal care and policy.
  
  
  METHODS
  Systematic review of observational studies.
  
  
  METHODS
  Studies describing the validity of codes for acute kidney injury (AKI) and chronic kidney disease (CKD) in administrative databases operating in any jurisdiction.
  
  
  METHODS
  After searching 13 medical databases, we included observational studies published from database inception though June 2009 that validated renal diagnostic and procedural codes for AKI or CKD against a reference standard.
  
  
  METHODS
  Renal diagnostic or procedural administrative data codes.
  
  
  METHODS
  Patient chart review, laboratory values, or data from a high-quality patient registry.
  
  
  RESULTS
  25 studies of 13 databases in 4 countries were included. Validation of diagnostic and procedural codes for AKI was present in 9 studies, and validation for CKD was present in 19 studies. Sensitivity varied across studies and generally was poor (AKI median, 29%; range, 15%-81%; CKD median, 41%; range, 3%-88%). Positive predictive values often were reasonable, but results also were variable (AKI median, 67%; range, 15%-96%; CKD median, 78%; range, 29%-100%). Defining AKI and CKD by only the use of dialysis generally resulted in better code validity. The study characteristic associated with sensitivity in multivariable meta-regression was whether the reference standard used laboratory values (P < 0.001); sensitivity was 39% lower when laboratory values were used (95% CI, 23%-56%).
  
  
  CONCLUSIONS
  Missing data in primary studies limited some of the analyses that could be done.
  
  
  CONCLUSIONS
  Administrative database analyses have utility, but must be conducted and interpreted judiciously to avoid bias arising from poor code validity.
authors.affiliation: Department of Medicine, University of Western Ontario, London, Canada.
authors.forename: Meghan E O
authors.initials: ME
authors.lastname: Vlasschaert
authors.forename: Shayna A D
authors.initials: SA
authors.lastname: Bejaimal
authors.forename: Daniel G
authors.initials: DG
authors.lastname: Hackam
authors.forename: Robert
authors.initials: R
authors.lastname: Quinn
authors.forename: Meaghan S
authors.initials: MS
authors.lastname: Cuerden
authors.forename: Matthew J
authors.initials: MJ
authors.lastname: Oliver
authors.forename: Arthur
authors.initials: A
authors.lastname: Iansavichus
authors.forename: Nabil
authors.initials: N
authors.lastname: Sultan
authors.forename: Alison
authors.initials: A
authors.lastname: Mills
authors.forename: Amit X
authors.initials: AX
authors.lastname: Garg
mesh_terms: D001315:Australia; D002170:Canada; D059019:Clinical Coding; D016208:Databases, Factual; D006302:Health Services Research; D006801:Humans; D007674:Kidney Diseases / Q000145:classification*; D012680:Sensitivity and Specificity; D013030:Spain; D014481:United States