Evaluation of risk stratification for acute kidney injury : a comparative analysis of EKFC, 2009 and 2021 CKD-EPI glomerular filtration estimating equations
© 2024. The Author(s)..
BACKGROUND: The adoption of the 2021 CKD-EPIcr equation for glomerular filtration rate (GFR) estimation provided a race-free eGFR calculation. However, the discriminative performance for AKI risk has been rarely validated. We aimed to evaluate the differences in acute kidney injury (AKI) prediction or reclassification power according to the three eGFR equations.
METHODS: We performed a retrospective observational study within a tertiary hospital from 2011 to 2021. Acute kidney injury was defined according to KDIGO serum creatinine criteria. Glomerular filtration rate estimates were calculated by three GFR estimating equations: 2009 and 2021 CKD-EPIcr, and EKFC. In three equations, AKI prediction performance was evaluated with area under receiver operator curves (AUROC) and reclassification power was evaluated with net reclassification improvement analysis.
RESULTS: A total of 187,139 individuals, including 27,447 (14.7%) AKI and 159,692 (85.3%) controls, were enrolled. In the multivariable regression prediction model, the 2009 CKD-EPIcr model (continuous eGFR model 2, 0.7583 [0.755-0.7617]) showed superior performance in AKI prediction to the 2021 CKD-EPIcr (0.7564 [0.7531-0.7597], < 0.001) or EKFC model in AUROC (0.7577 [0.7543-0.761], < 0.001). Moreover, in reclassification of AKI, the 2021 CKD-EPIcr and EKFC models showed a worse classification performance than the 2009 CKD-EPIcr model. (- 7.24 [- 8.21-- 6.21], - 2.38 [- 2.72-- 1.97]).
CONCLUSION: Regarding AKI risk stratification, the 2009 CKD-EPIcr equation showed better discriminative performance compared to the 2021 CKD-EPIcr equation in the study population.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2024 |
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| Enthalten in: |
Journal of nephrology - (2024) vom: 12. Feb. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Cho, Jeong Min [VerfasserIn] |
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| Links: |
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| Themen: |
Acute kidney injury |
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| Anmerkungen: |
Date Revised 12.02.2024 published: Print-Electronic Citation Status Publisher |
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| doi: |
10.1007/s40620-023-01883-7 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM368358305 |
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| 041 | |a eng | ||
| 100 | 1 | |a Cho, Jeong Min |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Evaluation of risk stratification for acute kidney injury |b a comparative analysis of EKFC, 2009 and 2021 CKD-EPI glomerular filtration estimating equations |
| 264 | 1 | |c 2024 | |
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| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status Publisher | ||
| 520 | |a © 2024. The Author(s). | ||
| 520 | |a BACKGROUND: The adoption of the 2021 CKD-EPIcr equation for glomerular filtration rate (GFR) estimation provided a race-free eGFR calculation. However, the discriminative performance for AKI risk has been rarely validated. We aimed to evaluate the differences in acute kidney injury (AKI) prediction or reclassification power according to the three eGFR equations | ||
| 520 | |a METHODS: We performed a retrospective observational study within a tertiary hospital from 2011 to 2021. Acute kidney injury was defined according to KDIGO serum creatinine criteria. Glomerular filtration rate estimates were calculated by three GFR estimating equations: 2009 and 2021 CKD-EPIcr, and EKFC. In three equations, AKI prediction performance was evaluated with area under receiver operator curves (AUROC) and reclassification power was evaluated with net reclassification improvement analysis | ||
| 520 | |a RESULTS: A total of 187,139 individuals, including 27,447 (14.7%) AKI and 159,692 (85.3%) controls, were enrolled. In the multivariable regression prediction model, the 2009 CKD-EPIcr model (continuous eGFR model 2, 0.7583 [0.755-0.7617]) showed superior performance in AKI prediction to the 2021 CKD-EPIcr (0.7564 [0.7531-0.7597], < 0.001) or EKFC model in AUROC (0.7577 [0.7543-0.761], < 0.001). Moreover, in reclassification of AKI, the 2021 CKD-EPIcr and EKFC models showed a worse classification performance than the 2009 CKD-EPIcr model. (- 7.24 [- 8.21-- 6.21], - 2.38 [- 2.72-- 1.97]) | ||
| 520 | |a CONCLUSION: Regarding AKI risk stratification, the 2009 CKD-EPIcr equation showed better discriminative performance compared to the 2021 CKD-EPIcr equation in the study population | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Acute kidney injury | |
| 650 | 4 | |a Creatinine | |
| 650 | 4 | |a Glomerular filtration rate | |
| 650 | 4 | |a Kidney function tests | |
| 700 | 1 | |a Koh, Jung Hun |e verfasserin |4 aut | |
| 700 | 1 | |a Kim, Minsang |e verfasserin |4 aut | |
| 700 | 1 | |a Jung, Sehyun |e verfasserin |4 aut | |
| 700 | 1 | |a Cho, Semin |e verfasserin |4 aut | |
| 700 | 1 | |a Lee, Soojin |e verfasserin |4 aut | |
| 700 | 1 | |a Kim, Yaerim |e verfasserin |4 aut | |
| 700 | 1 | |a Kim, Yong Chul |e verfasserin |4 aut | |
| 700 | 1 | |a Lee, Hajeong |e verfasserin |4 aut | |
| 700 | 1 | |a Han, Seung Seok |e verfasserin |4 aut | |
| 700 | 1 | |a Oh, Kook-Hwan |e verfasserin |4 aut | |
| 700 | 1 | |a Joo, Kwon Wook |e verfasserin |4 aut | |
| 700 | 1 | |a Kim, Yon Su |e verfasserin |4 aut | |
| 700 | 1 | |a Kim, Dong Ki |e verfasserin |4 aut | |
| 700 | 1 | |a Park, Sehoon |e verfasserin |4 aut | |
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