Estimating baseline creatinine to detect acute kidney injury in patients with chronic kidney disease

Thomas Larsen, Emily J. See, Natasha E. Holmes, Rinaldo Bellomo

Research output: Contribution to journalArticleResearchpeer-review

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Abstract

Background: Accurately estimating baseline kidney function is essential for diagnosing acute kidney injury (AKI) in patients with chronic kidney disease (CKD). We developed and evaluated novel equations to estimate baseline creatinine in patients with AKI on CKD. Methods: We retrospectively analysed 5649 adults with AKI out of 11 254 CKD patients, dividing them evenly into derivation and validation groups. Using quantiles regression, we created equations to estimate baseline creatinine, considering historical creatinine values, months since measurement, age, and sex from the derivation dataset. We assessed performance against back-estimation equations and unadjusted historical creatinine values using the validation dataset. Results: The optimal equation adjusted the most recent creatinine value for time since measurement and sex. Estimates closely matched the actual baseline at AKI onset, with median (95% confidence interval) differences of just 0.9% (−0.8% to 2.1%) and 0.6% (−1.6% to 3.9%) when the most recent value was within 6 months to 30 days and 2 years to 6 months before AKI onset, respectively. The equation improved AKI event reclassification by an additional 2.5% (2.0% to 3.0%) compared to the unadjusted most recent creatinine value and 7.3% (6.2% to 8.4%) compared to the CKD-EPI 2021 back-estimation equation. Conclusion: Creatinine levels drift in patients with CKD, causing false positives in AKI detection without adjustment. Our novel equation adjusts the most recent creatinine value for drift over time. It provides more accurate baseline creatinine estimation in patients with suspected AKI on CKD, which reduces false-positive AKI detection, improving patient care and management.

Original languageEnglish
Pages (from-to)434-445
Number of pages12
JournalNephrology
Volume28
Issue number8
DOIs
Publication statusPublished - Aug 2023

Keywords

  • acute kidney injury/diagnosis
  • acute kidney injury/epidemiology
  • chronic kidney disease
  • humans
  • linear models
  • quantile regression

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