Performance and limitations of administrative data in the identification of AKI.
OBJECTIVE
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.


METHODS
The study population included 10,056 Atherosclerosis Risk in Communities study participants hospitalized between 1996 and 2008. Billing code-identified AKI was compared with the 2012 Kidney Disease Improving Global Outcomes (KDIGO) creatinine-based criteria (AKIcr) and an approximation of the 2012 KDIGO creatinine- and urine output-based criteria (AKIcr_uop) in a subset with available outpatient data. Sensitivity and specificity of billing code-identified AKI were evaluated over time and according to patient age, race, sex, diabetes status, and CKD status in 546 charts selected for review, with estimates adjusted for sampling technique.


RESULTS
A total of 34,179 hospitalizations were identified; 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 AKIcr_uop (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 AKIcr_uop, 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.
Division of Nephrology and, §Division of General Internal Medicine, Department of Medicine, Johns Hopkins University, Baltimore, Maryland;, †Renal Division, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, ‡Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.
Morgan E
ME
Grams
Sushrut S
SS
Waikar
Blaithin
B
MacMahon
Seamus
S
Whelton
Shoshana H
SH
Ballew
Josef
J
Coresh
D058186:Acute Kidney Injury / Q000097:blood / Q000175:diagnosis* / Q000453:epidemiology* / Q000401:mortality / Q000503:physiopathology; D000368:Aged; D015415:Biomarkers / Q000097:blood; D059019:Clinical Coding; D003404:Creatinine / Q000097:blood; D057225:Data Mining; D016208:Databases, Factual; D057286:Electronic Health Records; D005260:Female; D017052:Hospital Mortality; D006760:Hospitalization; D006801:Humans; D038801:International Classification of Diseases; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D011379:Prognosis; D015203:Reproducibility of Results; D012720:Severity of Illness Index; D013997:Time Factors; D014481:United States / Q000453:epidemiology; D014554:Urination
D015415:Biomarkers; D003404:Creatinine