Derivation and validation of the renal angina index to improve the prediction of acute kidney injury in critically ill children
Author(s) -
Rajit K. Basu,
Michael Zappitelli,
Lori Brunner,
Yu Wang,
Hector R. Wong,
Lakhmir S. Chawla,
Derek S. Wheeler,
Stuart L. Goldstein
Publication year - 2013
Publication title -
kidney international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.499
H-Index - 276
eISSN - 1523-1755
pISSN - 0085-2538
DOI - 10.1038/ki.2013.349
Subject(s) - medicine , acute kidney injury , critically ill , intensive care medicine , index (typography) , angina , cardiology , computer science , myocardial infarction , world wide web
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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