Kidney Week

Abstract: FR-PO033

A Population-Based Imputation Approach for Missing Baseline Serum Creatinine Values

Session Information

• AKI: Epidemiology, Risk Factors, and Prevention - 2
  October 25, 2024 | Location: Exhibit Hall, Convention Center
  Abstract Time: 10:00 AM - 12:00 PM

Category: Acute Kidney Injury

• 101 AKI: Epidemiology, Risk Factors, and Prevention

Authors

• Chang, David R., China Medical University Hospital Department of Internal Medicine, Taichung, Taiwan

David R. Chang, MD

• Chiang, Hsiu-Yin, Big Data Center, China Medical University Hospital, Taichung, Taiwan

Hsiu-Yin Chiang, MS, PhD

• Chang, Yi-Ching, Big Data Center, China Medical University Hospital, Taichung, Taiwan

Yi-Ching Chang, MS

• Takeuchi, Tomonori, The University of Alabama at Birmingham, Birmingham, Alabama, United States

Tomonori Takeuchi, MD

• Lin, Che-Chen, Big Data Center, China Medical University Hospital, Taichung, Taiwan

Che-Chen Lin, MS

• Chen, Jin, The University of Alabama at Birmingham, Birmingham, Alabama, United States

Jin Chen, PhD

• Neyra, Javier A., The University of Alabama at Birmingham, Birmingham, Alabama, United States

Javier A. Neyra, MD, MS, FASN

• Kuo, Chin-Chi, Big Data Center, China Medical University Hospital, Taichung, Taiwan

Chin-Chi Kuo, MD, PhD, MPH

Background

Diagnosing acute kidney injury (AKI) requires a baseline serum creatinine (S-Cre), which is often missing in the real world. We aimed to develop and validate a new method for estimating baseline S-Cre and compare it to existing methods

Methods

From all S-Cre measurements collected from adult outpatients at CMUH in 19 years, we excluded those were 1) <0.3 or >30 mg/dL, 2) from patients with end-stage kidney disease or nephrectomy, 3) within 72 hours following dialysis or cardiopulmonary resuscitation, or 4) within 24 hours after massive transfusion, resulting in 3403528 measurements for baseline S-Cre reference. We developed the “median” imputation methods that utilized the median S-Cre within subgroups stratified by age, gender, and chronic kidney disease (median A), or in combination with body mass index (median B). A sepsis cohort meeting Sepsis-3 was used to assess the performance of 4 imputation methods (median A, median B, eGFR 75, and multiple imputation [MI]) in discriminating AKI according to KDIGO criteria. Bland-Altman plot was used to visualize bias between measured and imputed S-Cre.

Results

In the sepsis cohort, median A and B method had a smaller mean of difference, compared to eGFR 75 and MI method. Median A method demonstrated the highest agreement for AKI (84%; Kappa 0.68), compared with eGFR 75 (82%; 0.63) and MI method (81%; 0.62). eGFR 75 had superior sensitivity (85% vs 82%), while MI showed higher specificity compared to median A method (92% vs 86%).

Conclusion

The median methods showed satisfactory agreement in AKI detection comparable to eGFR 75 and MI approaches, offering a simple and intuitive approach for real-world application for baseline S-Cre estimation. External validation in the US population is underway.

Performance of baseline S-Cre imputation methods using sepsis AKI cohort.

Bland-Altman plots of true and imputed baseline S-Cre using different imputation methods.