Performance and Limitations of Administrative Data in the Identification of AKI

BACKGROUND AND OBJECTIVES: Billing codes are frequently used to identify AKI events in epidemiologic research. The goals of this study were to validate billing code-identified AKI against the current AKI consensus definition and to ascertain whether sensitivity and specificity vary by patient characteristic or over time. DESIGN, SETTING, PARTICIPANTS, 1353 had a billing code for AKI. The sensitivity of billing code-identified AKI was 17. 2% (95% confidence interval 95% CI, 13. 2% to 21. 2%) compared with AKIcr (n=1970 hospitalizations) and 11. 7% (95% CI, 8. 8% to 14. 5%) compared with AKIcrᵤop (n=1839 hospitalizations). Specificity was >98% in both cases. Sensitivity was significantly higher in the more recent time period (2002-2008) and among participants aged 65 years and older. Billing code-identified AKI captured a more severe spectrum of disease than did AKIcr and AKIcrᵤop, with a larger proportion of patients with stage 3 AKI (34. 9%, 19. 7%, and 11. 5%, respectively) and higher in-hospital mortality (41. 2%, 18. 7%, and 12. 8%, respectively). CONCLUSIONS: The use of billing codes to identify AKI has low sensitivity compared with the current KDIGO consensus definition, especially when the urine output criterion is included, and results in the identification of a more severe phenotype. Epidemiologic studies using billing codes may benefit from a high specificity, but the variation in sensitivity may result in bias, particularly when trends over time are the outcome of interest.