Biomarkers for prediction of acute kidney injury in pediatric patients: a systematic review and meta-analysis of diagnostic test accuracy studies.

Abstract

Severity of acute kidney injury (AKI) confers higher odds of mortality. Timely recognition and early initiation of preventive measures may help mitigate the injury further. Novel biomarkers may aid in the early detection of AKI. The utility of these biomarkers across various clinical settings in children has not been evaluated systematically. To synthesize the currently available evidence on different novel biomarkers for the early diagnosis of AKI in pediatric patients. We searched four electronic databases (PubMed, Web of Science, Embase, and Cochrane Library) for studies published between 2004 and May 2022. Cohort and cross-sectional studies evaluating the diagnostic performance of biomarkers in predicting AKI in children were included. Participants in the study included children (aged less than 18years) at risk of AKI. We used the QUADAS-2 tool for the quality assessment of the included studies. The area under the receiver operating characteristics (AUROC) was meta-analyzed using the random-effect inverse-variance method. Pooled sensitivity and specificity were generated using the hierarchical summary receiver operating characteristic (HSROC) model. We included 92 studies evaluating 13,097 participants. Urinary NGAL and serum cystatin C were the two most studied biomarkers, with summary AUROC of 0.82 (0.77-0.86) and 0.80 (0.76-0.85), respectively. Among others, urine TIMP-2*IGFBP7, L-FABP, and IL-18 showed fair to good predicting ability for AKI. We observed good diagnostic performance for predicting severe AKI by urine L-FABP, NGAL, and serum cystatin C. Limitations were significant heterogeneity and lack of well-defined cutoff value for various biomarkers. Urine NGAL, L-FABP, TIMP-2*IGFBP7, and cystatin C showed satisfactory diagnostic accuracy in the early prediction of AKI. To further improve the performance of biomarkers, they need to be integrated with other risk stratification models. PROSPERO (CRD42021222698). A higher resolution version of the Graphical abstract is available as "Supplementary information".