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Table 2 Features that may influence the performance of automated AKI alerts based on the KDIGO AKI criteria

From: Applications for detection of acute kidney injury using electronic medical records and clinical information systems: workgroup statements from the 15th ADQI Consensus Conference

KDIGO AKI criteria Feature
Serum creatinine Calibration of measure according to IDMS standard
Optimal measurement using enzymatic assay [15]
Comparison across laboratories or measurement techniques [16]
Relevancy of e-alert systems using estimated baseline creatinine
If previous creatinine available, chosen definition of baseline creatinine
Management of outliers measures
Significance of small changes in serum creatinine in patients with low weight/body surface or with pre-existing CKD
Performance of e-alert system in unselected population of patients.
Management of multiple alert in a same patient
Influence of fluid balance/dilution [17].
Urine output Difference in measurement according to setting (ICU vs. Ward, Specificity of paediatric units, rate of Foley catheter use).
Management of missing data
Errors in reading [8]
Errors related to manual entry of urinary output
Differences related to measurement (hourly vs. by shift vs. daily)
Recognition of the lack of specificity of oliguria [1820]
Cross-tabulation between serum creatinine and UO