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Title: Estimating baseline creatinine to detect acute kidney injury in patients with chronic kidney disease.
Austin Authors: Larsen, Thomas;See, Emily J ;Holmes, Natasha E ;Bellomo, Rinaldo 
Affiliation: Data Analytics Research and Evaluation (DARE) Centre
Department of Intensive Care, Royal Melbourne Hospital, Melbourne, Victoria, Australia
Department of Critical Care, School of Medicine, University of Melbourne, Melbourne, Victoria, Australia.
Australian and New Zealand Intensive Care Research Centre, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
Department of Nephrology, Royal Melbourne Hospital, Melbourne, Victoria, Australia.
Intensive Care
Issue Date: Aug-2023
Date: 2023
Publication information: Nephrology (Carlton, Vic.) 2023; 28(8)
Abstract: 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. 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. 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. 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.
DOI: 10.1111/nep.14191
ORCID: 0000-0002-6392-314X
Journal: Nephrology
PubMed URL: 37277898
ISSN: 1440-1797
Type: Journal Article
Subjects: acute kidney injury/diagnosis
acute kidney injury/epidemiology
chronic kidney disease
linear models
quantile regression
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