The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.
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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Cites
Surviving Sepsis Campaign: International Guidelines for Management of Severe Sepsis and Septic Shock
Dellinger, R. P., Levy, M. M., Rhodes, A., Annane, D., Gerlach, H., Opal, S. M., Sevransky, J. E., Sprung, C. L., Douglas, I. S., Jaeschke, R., et al. (2013) · Critical Care Medicine · doi:10.1097/ccm.0b013e31827e83af · Cited by 9,694
Dellinger, R. P., Levy, M. M., Rhodes, A., Annane, D., Gerlach, H., Opal, S. M., Sevransky, J., Sprung, C. L., Douglas, I. S., Jaeschke, R., et al. (2013) · Intensive Care Medicine · doi:10.1007/s00134-012-2769-8 · Cited by 7,371
Goldstein, B., Giroir, B. P., & Randolph, A. G. (2005) · Pediatric Critical Care Medicine · doi:10.1097/01.pcc.0000149131.72248.e6 · Cited by 3,675
Acute Kidney Injury, Mortality, Length of Stay, and Costs in Hospitalized Patients
Chertow, G. M., Burdick, E., Honour, M. M., Bonventre, J. V., & Bates, D. W. (2005) · Journal of the American Society of Nephrology · doi:10.1681/asn.2004090740 · Cited by 3,384
Mishra, J., Dent, C., Tarabishi, R., Mitsnefes, M. M., Ma, Q., Kelly, C., Ruff, S. M., Zahedi, K., Shao, M., Bean, J., et al. (2005) · The Lancet · doi:10.1016/s0140-6736(05)74811-x · Cited by 2,394
Pediatric risk of mortality (PRISM) score
Pollack, M. M., Ruttimann, U. E., & Getson, P. R. (1988) · Critical Care Medicine · doi:10.1097/00003246-198811000-00006 · Cited by 1,396
Modified RIFLE criteria in critically ill children with acute kidney injury
Akcan‐Arikan, A., Zappitelli, M., Loftis, L. L., Washburn, K. K., Jefferson, L. S., & Goldstein, S. L. (2007) · Kidney International · doi:10.1038/sj.ki.5002231 · Cited by 1,389
Self-citationKidney Disease: Improving Global Outcomes
Eckardt, K., & Kasiske, B. L. (2009) · Nature Reviews Nephrology · doi:10.1038/nrneph.2009.153 · Cited by 743
Sutherland, S. M., Zappitelli, M., Alexander, S. R., Chua, A. N., Brophy, P. D., Bunchman, T. E., Hackbarth, R. M., Somers, M. J. G., Baum, M. A., Symons, J. M., et al. (2009) · American Journal of Kidney Diseases · doi:10.1053/j.ajkd.2009.10.048 · Cited by 695
Self-citationUrinary IL-18 is an early predictive biomarker of acute kidney injury after cardiac surgery
Parikh, C. R., Mishra, J., Thiessen‐Philbrook, H., Dursun, B., Ma, Q., Kelly, C. N., Krawczeski, C. D., Devarajan, P., & Edelstein, C. L. (2006) · Kidney International · doi:10.1038/sj.ki.5001527 · Cited by 612
Cited by
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Bellomo, R., Kellum, J. A., Ronco, C., Wald, R., Mårtensson, J., Maiden, M. J., Bagshaw, S. M., Glassford, N. J., Lankadeva, Y. R., Vaara, S. T., et al. (2017) · Intensive Care Medicine · doi:10.1007/s00134-017-4755-7 · Cited by 794
Ostermann, M., Lumlertgul, N., Jeong, R., See, E. J., Joannidis, M., & James, M. T. (2025) · The Lancet · doi:10.1016/s0140-6736(24)02385-7 · Cited by 485
Sepsis-Associated Acute Kidney Injury
Alobaidi, R., Basu, R. K., Goldstein, S. L., & Bagshaw, S. M. (2015) · Seminars in Nephrology · doi:10.1016/j.semnephrol.2015.01.002 · Cited by 385
Self-citationZappitelli, M., Ambalavanan, N., Askenazi, D. J., Moxey‐Mims, M., Kimmel, P. L., Star, R. A., Abitbol, C. L., Brophy, P. D., Hidalgo, G., Hanna, M. H., et al. (2017) · Pediatric Research · doi:10.1038/pr.2017.136 · Cited by 257
Self-citationMachine learning model for early prediction of acute kidney injury (AKI) in pediatric critical care
Dong, J., Feng, T., Thapa-Chhetry, B., Cho, B. G., Shum, T., Inwald, D. P., Newth, C. J. L., & Vaidya, V. U. (2021) · Critical Care · doi:10.1186/s13054-021-03724-0 · Cited by 233
“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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