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Derivation and validation of the renal angina index to improve the prediction of acute kidney injury in critically ill children
Open accessAbstract
Reliable prediction of severe acute kidney injury (AKI) has the potential to optimize treatment. Here we operationalized the empiric concept of renal angina with a renal angina index (RAI) and determined the predictive performance of RAI. This was assessed on admission to the pediatric intensive care unit, for subsequent severe AKI (over 200% rise in serum creatinine) 72 h later (Day-3 AKI). In a multicenter four cohort appraisal (one derivation and three validation), incidence rates for a Day 0 RAI of 8 or more were 15-68% and Day-3 AKI was 13-21%. In all cohorts, Day-3 AKI rates were higher in patients with an RAI of 8 or more with the area under the curve of RAI for predicting Day-3 AKI of 0.74-0.81. An RAI under 8 had high negative predictive values (92-99%) for Day-3 AKI. RAI outperformed traditional markers of pediatric severity of illness (Pediatric Risk of Mortality-II) and AKI risk factors alone for prediction of Day-3 AKI. Additionally, the RAI outperformed all KDIGO stages for prediction of Day-3 AKI. Thus, we operationalized the renal angina concept by deriving and validating the RAI for prediction of subsequent severe AKI. The RAI provides a clinically feasible and applicable methodology to identify critically ill children at risk of severe AKI lasting beyond functional injury. The RAI may potentially reduce capricious AKI biomarker use by identifying patients in whom further testing would be most beneficial.
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“In addition, stem cell transplantation information, originally used in RAI [17] but not in the model, may have contributed to the higher performance of previously reported RAI compared to those computed here”
From Machine learning model for early prediction of acute kidney injury (AKI) in pediatric critical care (2021) · cites this paper as [17]
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“The baseline comparator RAI, using mainly creatinine to predict AKI [17], performed worse than the model”
From Machine learning model for early prediction of acute kidney injury (AKI) in pediatric critical care (2021) · cites this paper as [17]
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“The model outperforms a more simple comparator, the RAI, shown to be predictive of AKI in critically ill children [17].”
From Machine learning model for early prediction of acute kidney injury (AKI) in pediatric critical care (2021) · cites this paper as [17]
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“The model outperforms a more simple comparator, the RAI, shown to be predictive of AKI in critically ill children [17].”
MethodResultInfluential
Metadata
- Authors
- Basu, R. K., Zappitelli, M., Brunner, L., Wang, Y., Wong, H. R., Chawla, L. S., Wheeler, D. S., & Goldstein, S. L.
- Affiliations
- Cincinnati Children's Hospital Medical Center (US)
- Montreal Children's Hospital (CA)
- George Washington University (US)
- Corresponding author
- Basu, Rajit K.
- ORCID
- Venue
- Kidney International
- Publisher
- Elsevier BV
- Published
- 2014-03
- Volume
- 85
- Issue
- 3
- Pages
- 659-667
- ISSN
- 0085-2538 (ISSN-L)
- PMC
- PMC4659420
- OpenAlex
- W2007813089
- Cited by
- 297
- References
- 49
- Citations by year
- 2026: 22 · 2025: 34 · 2024: 21 · 2023: 28 · 2022: 31 · 2021: 27 · 2020: 18 · 2019: 22 · 2018: 28 · 2017: 15 · 2016: 21 · 2015: 16 · 2014: 12
- FWCI
- 12.63
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Kidney International (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (5ul1rr026314-03, 5rc1hl100474-02, UL1TR000077)
- National Heart, Lung, and Blood Institute (RC1-HL100474)
- National Institute of General Medical Sciences (R01GM064619, R01GM108025, R01GM099773)
- National Institute of Diabetes and Digestive and Kidney Diseases (P50DK096418)
- National Center for Research Resources (UL1-RR026314, UL1RR02631401)
- National Center for Advancing Translational Sciences
- Topics
- Health Sciences › Medicine › Nephrology › Acute Kidney Injury Research
- Health Sciences › Medicine › Nephrology › Chronic Kidney Disease and Diabetes
- Health Sciences › Medicine › Critical Care and Intensive Care Medicine › Sepsis Diagnosis and Treatment
- Keywords
- renal angina index, acute kidney injury, pediatric intensive care, critically ill children, AKI prediction, risk stratification, serum creatinine, KDIGO staging, kidney injury biomarkers, negative predictive value
- MeSH terms
- Child, Child, Preschool, Creatinine, Female, Humans, Infant, Male, Severity of Illness Index, Biomarkers, Critical Illness, Acute Kidney Injury
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