title: Defining acute kidney injury in database studies: the effects of varying the baseline kidney function assessment period and considering CKD status.
abstract: BACKGROUND
  Existing acute kidney injury (AKI) definitions are not well adapted for database studies, leading to a great variety of methods used in research. Variations in time before hospitalization used to assess baseline kidney function when identifying episodes of AKI may lead to different case samples and mortality risks in database studies, but the magnitude of these effects is not known.
  
  
  METHODS
  A retrospective cohort study.
  
  
  METHODS
  1,126,636 veterans hospitalized at least once within the US Department of Veterans Affairs health care system between 2000 and 2005.
  
  
  METHODS
  AKI was defined by comparing (using ratio [≥1.5] or difference [increase of 0.3-0.5 mg/dL]) the highest serum creatinine level during hospitalization with the lowest level during 4 different baseline assessment periods (in-hospital only and 3, 6, or 12 months preadmission).
  
  
  METHODS
  In-hospital mortality risk was estimated using multivariable logistic regression models.
  
  
  RESULTS
  Using the ratio definition, the cumulative incidence of AKI ranged from 12.5% (in-hospital only) to 18.3% (12 months preadmission). Newly added cases had milder AKI and lower mortality risk. The discriminative power increased slightly (C statistic increased from 0.846 to 0.855; P = 0.001) by extending the baseline period to at least 3 months. Both the ratio and difference definitions did not perform well in patients with chronic kidney disease stages 4 and 5.
  
  
  CONCLUSIONS
  Possibility of residual confounding and under-representation of women (4.5%).
  
  
  CONCLUSIONS
  Many additional AKI cases may be identified by extending the baseline assessment period; however, added cases may be less severe with lower mortality risk. The relative strengths of these biases and combined effects of reducing misclassification (identification of more AKI cases) and increasing risk dilution (identifying milder cases) may vary across populations. Consensus regarding how baseline kidney function should be determined in database studies should be reached.
authors.affiliation: Center for Health Quality, Outcomes, and Economic Research, Edith Nourse Rogers Memorial Veterans Hospital, Bedford, MA 01730, USA. jplaf@bu.edu
authors.forename: Jean-Philippe
authors.initials: JP
authors.lastname: Lafrance
authors.forename: Donald R
authors.initials: DR
authors.lastname: Miller
mesh_terms: D058186:Acute Kidney Injury / Q000175:diagnosis* / Q000453:epidemiology* / Q000628:therapy; D000368:Aged; D015331:Cohort Studies; D003404:Creatinine / Q000097:blood*; D016208:Databases, Factual; D018450:Disease Progression; D005260:Female; D005500:Follow-Up Studies; D017052:Hospital Mortality / Q000639:trends*; D006801:Humans; D015994:Incidence; D007676:Kidney Failure, Chronic / Q000175:diagnosis* / Q000453:epidemiology* / Q000628:therapy; D007677:Kidney Function Tests / Q000379:methods*; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D006435:Renal Dialysis; D012189:Retrospective Studies; D018570:Risk Assessment; D012720:Severity of Illness Index; D015996:Survival Rate; D013997:Time Factors; D016896:Treatment Outcome
chemical_list: D003404:Creatinine