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

Background 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.Objective To synthesize the currently available evidence on different novel biomarkers for the early diagnosis of AKI in pediatric patients.Data sources We searched four electronic databases (PubMed, Web of Science, Embase, and Cochrane Library) for studies published between 2004 and May 2022.Study eligibility criteria Cohort and cross-sectional studies evaluating the diagnostic performance of biomarkers in predicting AKI in children were included.Participants and interventions Participants in the study included children (aged less than 18 years) at risk of AKI.Study appraisal and synthesis methods 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.Results 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 Limitations were significant heterogeneity and lack of well-defined cutoff value for various biomarkers.Conclusions and implications of key findings 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.