Early prediction of acute kidney injury in neonates with cardiac surgery. Issue 2 (25th June 2020)

• Record Type: Journal Article
• Authors: Shi, Shanshan; Fan, Jiajie; Shu, Qiang
• Is Part Of: World journal of pediatric surgery. Volume 3:Issue 2(2020)
• Publication Date: 2020-06-25
• Subjects: cardiac surgery -- neonatology -- intensive care -- nephrology; Children -- Surgery -- Periodicals; 617.98
• DOI: 10.1136/wjps-2019-000107
• Languages: English
• ISSNs: 2516-5410

Abstract

Abstract : Background: Acute kidney injury (AKI) occurs in 42%–64% of the neonatal patients experiencing cardiac surgery, contributing to postoperative morbidity and mortality. Current diagnostic criteria, which are mainly based on serum creatinine and hourly urine output, are not sufficiently sensitive and precise to diagnose neonatal AKI promptly. The purpose of this review is to screen the recent literature, to summarize the novel and cost-effective biomarkers and approaches for neonatal AKI after cardiac surgery (CS-AKI), and to provide a possible research direction for future work. Data sources: We searched PubMed for articles published before November 2019 with pertinent terms. Sixty-seven articles were found and screened. After excluding 48 records, 19 articles were enrolled for final analysis. Results: Nineteen articles were enrolled, and 18 possible urinary biomarkers were identified and evaluated for their ability to diagnose CS-AKI. Urinary neutrophil gelatinase-associated lipocalin (uNGAL), serum cystatin C (sCys), urinary human kidney injury molecule-1 (uKIM-1), urinary liver fatty acid-binding protein (uL-FABP) and interleukin-18 (uIL-18) were the most frequently described as the early predictors of neonatal CS-AKI. Conclusions: Neonates are vulnerable to CS-AKI. UNGAL, sCys, uL-FABP, uKIM-1 and uIL-18 are potential biomarkers for early prediction of neonatal CS-AKI. Renal regional oxygen saturation by near-infrared spectroscopy is a non-invasive approach for early identification of neonatal AKI. Further work should focus on exploring a sensitive and specific combined diagnostic model that includes novel biomarkers and other complementary methods.