Archives of Academic Emergency Medicine. 2020; 8(1): e44 REV I EW ART I C L E Accuracy of Urine Kidney Injury Molecule-1 in Predicting Acute Kidney Injury in Children; a Systematic Review and Meta-Analysis Mojtaba Fazel1,2, Arash Sarveazad3,4, Kosar Mohamed Ali5, Mahmoud Yousefifard6∗, Mostafa Hosseini1,7 †, 1. Pediatric Chronic Kidney Disease Research Center, Tehran University of Medical Sciences, Tehran, Iran. 2. Department of Pediatrics, Valiasr Hospital, Imam Khomeini Medical Complex, Tehran University of Medical Sciences, Tehran, Iran. 3. Colorectal Research Center, Iran University of Medical Sciences, Tehran, Iran. 4. Nursing Care Research Center, Iran University of Medical Sciences, Tehran, Iran. 5. College of medicine, University of Sulaimani, Sulaimani, Iraq . 6. Physiology Research Center, Iran University of Medical Sciences, Tehran, Iran. 7. Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran. Received: February 2020; Accepted: March 2020; Published online: 5 April 2020 Abstract: Introduction: There is considerable controversy on the accuracy of Kidney Injury Molecule-1 (KIM-1) in predic- tion of acute kidney injury (AKI) in children. Therefore, the present study intends to provide a systematic review and meta-analysis of the value of this biomarker in predicting AKI in children. Methods: An extensive search was performed on the Medline, Embase, Scopus and Web of Science databases by the end of 2019. Cohort and case-control studies on children were included. Urinary KIM-1 levels were compared between AKI and non- AKI groups. Findings were reported as an overall standardized mean difference (SMD) with a 95% confidence interval (CI). Also, the overall area under the receiver operating characteristic (ROC) curve (AUC) of KIM-1 in predicting AKI in children was calculated. Results: Data from 13 articles were included. Urinary KIM-1 levels in children with stage 1 AKI were higher than the non-AKI group only when assessed within the first 12 hours after admission (SMD = 0.95; 95% CI: 0.07 to 1.84; p = 0.034). However, urinary KIM-1 levels in children with stage 2-3 AKI were significantly higher than non-AKI children (p <0.01) at all times. The AUC of urinary KIM-1 in predicting AKI in children was 0.69 (95% CI: 0.62 to 0.77). Conclusion: Based on the available evidence, KIM-1 seems to have moderate value in predicting AKI in children. Since previous meta-analyses have provided other urinary and serum biomarkers that have better discriminatory accuracy than KIM-1, so it had better not to use KIM-1 in predicting AKI in children. Keywords: Acute Kidney Injury; Renal Insufficiency; HAVCR1 protein, human; Hepatitis A Virus Cellular Receptor 1. Cite this article as: Fazel M, Sarveazad A, Mohamed Ali K, Yousefifard M, Hosseini M. Accuracy of Urine Kidney Injury Molecule-1 in Predict- ing Acute Kidney Injury in Children; a Systematic Review and Meta-Analysis. Arch Acad Emerg Mede. 2020; 8(1): e44. 1. Introduction Acute kidney injury (AKI) is one of the major public health problems worldwide, with a high incidence and many new ∗Corresponding Author: Mahmoud Yousefifard, Assistant Professor of Phys- iology, Physiology Research Center, Hemmat highway, Tehran, Iran. E-mail: yousefifard.m@iums.ac.ir † Corresponding Author: Mostafa Hosseini, Department of Epidemiology and Biostatistics School of Public Health, Tehran University of Medical Sciences, Poursina Ave, Tehran, Iran; Email: mhossein110@yahoo.com cases annually (1). There are many complications that can result from this condition, including metabolic acidosis, el- evated blood potassium levels, uremia, and changes in fluid balance. Long-term complications of AKI also include car- diovascular disease, stroke, and heart failure. Children with AKI mainly die from cardiovascular diseases and infections (2). Current research suggests that the use of preventive strategies and rapid diagnosis of AKI can lead to a signifi- cant reduction in the burden of AKI (3). Prompt diagnosis and treatment of the disease will enable slowing down the progression of the disease and prevent it from causing last- This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem M. Fazel et al. 2 ing complications, such as chronic kidney failure. Despite significant advances in medical knowledge, delayed identifi- cation of AKI can occur in some cases, and this may lead to persistent damage (4-6). Therefore, researchers are looking for diagnostic methods for early AKI identification. In recent years, serum and urine biomarkers have been suggested as reliable methods for rapid diagnosis of renal diseases, they have been shown to have better prognostic value compared to other techniques (7-9). These factors include serum crea- tinine, cystatin C, neutrophil gelatinase-associated lipocalin (NGAL) protein, and Kidney Injury Molecule-1 (KIM-1) (10- 12). KIM-1 is a membrane protein, which is not detectable in serum/urine of healthy individuals. However, KIM-1 is widely expressed in proximal tubule cells after ischemia and toxic conditions, and has been reported to be an appropriate marker in the diagnosis of AKI (13, 14). Systematic reviews and meta-analyses of adult studies suggest that urinary KIM- 1 levels can be an appropriate marker for early detection of AKI (14, 15). As can be seen, these meta-analyses were mainly performed on adults, while the number of studies on chil- dren has increased in recent years. In addition, there is con- siderable controversy on the accuracy of KIM-1 in prediction of AKI in children. Therefore, the present study intends to provide a systematic review and meta-analysis of the value of this biomarker in predicting AKI in children. 2. Methods 2.1. Study design This meta-analysis was designed based on the guidelines for Meta-analysis of Epidemiology Statement, to evaluate the value of urinary KIM-1 level in predicting AKI in children (16). 2.2. Search strategy To achieve the objectives of the present study, extensive searches were conducted on Medline (via PubMed), Em- base, Scopus, and Web of Science by the end of 2019. The search strategy was based on words related to KIM-1 and AKI. Then, by combining these words with appropriate tags in the databases, searches were performed and relevant ar- ticles were screened. To find additional articles or unpub- lished data, manual search was performed in the bibliogra- phy of relevant studies as well as search in Google and Google Scholar search engines. The search query used in Medline database is reported in appendix 1. 2.3. Selection criteria PICO was defined as follows: P: paediatric patients with AKI, I: urinary KIM-1, C: compare with non-AKI children, and outcome: discriminatory accuracy of KIM-1. In the present study, cohort and case-control studies on the accuracy of KIM-1 in predicting AKI in children were included. Stud- ies were included if AKI was confirmed by a standard pro- cedure and urine samples were obtained from all partici- pants. Duplicate studies, review studies, studies without a non-AKI group, and adult studies were excluded from the present study. 2.4. Data extraction and risk of bias assessment The method of collecting and evaluating the data is described in detail in our previous meta-analyses (17-20). In summary, after searching and removing duplicates, two independent researchers reviewed the titles and abstracts of records and then full-texts of potentially eligible articles were assessed. Disagreements were resolved in consultation with a third re- viewer. The data collection checklist was designed based on the PRISMA statement guidelines (21). Extracted data in- cluded first author’s name, year of publication, sample size, age and sex distribution of patients, patients’ setting, AKI def- inition criteria, KIM-1 level assay method, time interval be- tween patient’s admission and KIM-1 level assessment, the mean and standard deviation of urinary KIM-1 level, area under the receiver operating characteristic curve (AUC), and sensitivity and specificity of KIM-1 in predicting AKI in chil- dren. The risk of bias was assessed using the proposed guide- lines in QUADAS-2: A Revised Tool for the Quality Assess- ment of Diagnostic Accuracy Studies (22). 2.5. Statistical analysis Data were recorded as mean and standard deviation, AUC, sensitivity, and specificity of KIM-1 in predicting AKI in children. Most studies reported median and interquartile range instead of mean and standard deviation. Therefore, Cochrane’s proposed method was used to estimate the mean and standard deviation (23). All analyses were performed in STATA 14.0 statistical program and "metan" command was used. Findings were presented as standardized mean differ- ence (SMD) with a 95% confidence interval (95% CI) to com- pare the mean urinary KIM-1 level in AKI group with non-AKI group. Since the time interval between admission and KIM- 1 assessment varied between 0 and 96 hours in the included studies, analyses were performed in three time subgroups in- cluding 0-12 hours, 12-24 hours, and 24-96 hours. Also, in the eligible studies, patients were divided into three groups based on AKI severity, including stage 1 (or high-risk) AKI, stages 2-3 (or injury and failure) and all severities (stages 1- 3). For this reason, the analysis was also performed based on these subgroups. For this purpose, being high risk was con- sidered as stage 1 AKI, injury was deemed equivalent to stage 2 AKI, and failure was deemed equivalent to stage 3 AKI. An additional analysis was performed to pool the AUCs reported for urinary KIM-1 level in predicting AKI in children. In this section, the AUCs of urinary KIM-1 with their 95% CI were recorded in the statistical program and an overall AUC was This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem 3 Archives of Academic Emergency Medicine. 2020; 8(1): e44 reported. Heterogeneity between studies was assessed using I2 test and p value less than 0.1 were considered significant (indicating heterogeneity). In addition, publication bias was assessed using the funnel Plot (Egger’s tests) (24). 3. Results 3.1. Characteristics of included studies A search of databases yielded 2011 non-duplicated studies. During screening, 29 articles were reviewed in detail, and finally the data of 13 articles were included in the present meta-analysis (25-37) (Figure 1). There were 8 cohort and 5 case-control studies. The sample sizes ranged from 33 to 252 children. The total sample size was 1620 children (825 of whom were boys). Identification of AKI in 8 stud- ies were based on Kidney Disease Improvement Global Out- comes (KDIGO) criteria, based on Pediatric Risk, Injury, Fail- ure, and End-stage kidney disease (pRIFLE) criteria in 4 stud- ies and based on AKI network definition in 1 study. The in- terval between admission of patients and assessment of uri- nary KIM-1 levels ranged from 0 to 96 hours. All studies used the ELISA method to check the urine level of KIM-1 and all of them had frozen the specimens at -80 ◦C prior to examina- tion. Table 1 shows the characteristics of the included stud- ies. 3.2. Risk of bias and publication bias assessment The quality control of studies was performed based on QUADAS-2 criteria. Since the design of 5 studies was case- control, patient selection was associated with bias in these 5 studies and therefore, they were marked as having high-risk of bias. In other cases, the risk of bias and applicability were low risk (Table 2 and Figure 2). The analysis also revealed no evidence of publication bias in the present study (p = 0.576) (Figure 2). 3.3. Comparison of mean urinary KIM-1 levels in children with and without AKI Urinary KIM-1 levels were significantly higher in children with AKI compared to non-AKI children, regardless of sever- ity of AKI. As figure 3 shows, the urinary level of KIM-1 in children with all intensities of AKI (stage 1-3) was higher than non-AKI children during first 12 hours after admission (SMD = 0.84; 95% CI: 0.35 to 1.33; p = 0.001), 24-12 hours (SMD = 0.48; 95% CI: 0.14 to 0.82; p = 0.006) and between 96-24 hours (SMD = 1.08; 95% CI: 0.14 to 2.02; p = 0.024). 3.4. Comparison of mean urinary KIM1 levels between stage 1 AKI and non-AKI patients The urinary level of KIM-1 in children with stage 1 AKI was higher than the non-AKI group only when examined within the first 12 hours of admission (SMD = 0.95; 95% CI: 0.07 to 1.84; p = 0.034). However, 12-24 hours (SMD = -0.05; 95% CI: -0.62 to 0.51; p = 0.859) and 24-96 hours (SMD = -0.45; 95% CI: -1.17 to 0.28; p = 0.226) after admission, there was no difference between stage 1 AKI and non-AKI groups (Figure 4). 3.5. Evaluation of mean urinary KIM-1 levels in stage 2-3 of AKI and non-AKI patients Urinary KIM-1 level in children with stage 2-3 AKI was sig- nificantly higher than non-AKI children. As figure 5 shows, the urinary KIM-1 level in children with stage 2-3 AKI were higher than non-AKI children within the first 12 hours (SMD = 0.84; 95% CI: 0.35 to 1.33; p = 0.001), 24-12 hours (SMD = 1.02; 95% CI: 0.31 to 1.73; p = 0.005) and 96-24 hours (SMD = 0.75; 95% CI: 0.27 to 1.22; p = 0.002) after admission. 4. Discrimination 4.1. The AUC of urinary KIM-1 level in diagnosis of pediatric AKI In four studies, AUC of urinary KIM-1 was reported with a 95% CI (26, 28, 33, 37). Pooled analysis showed that AUC of urinary KIM-1 in prediction of AKI was 0.69 (95% CI: 0.62 to 0.77). 4.2. Sensitivity and specificity of urinary KIM-1 level in diagnosis of pediatric acute kidney injury In the beginning of the present study, it was decided to evalu- ate the discriminatory power of urinary KIM-1 based on sen- sitivity, specificity and diagnostic odds ratio. To achieve this goal, we needed data of true positive, false positive, true neg- ative and false negative. But such information was not re- ported in the studies. Only three studies reported the sensi- tivity and specificity of urinary KIM-1 level in prediction of AKI. In the first study, Carvalho et al. showed that the best cut-off point for KIM-1 in predicting chemotherapy-induced AKI was 6.2 ng / mg of creatinine. In this cut-off, urinary KIM-1 had a sensitivity and specificity of 73.1% and 92.1%, respectively (26). Another study by Sarafidis et al. showed that based on the best cut-off point (cut-off = 569.8 pg / ml), KIM-1 had a sensitivity and specificity of 40% and 86%, re- spectively (36). Finally, Westhoff et al. reported sensitivity and specificity of 54.5% and 96.5%, respectively, for KIM-1; indicating that the best cut-off point for this urine biomarker was 2235 pg / ml (37). 5. Discussion The findings of the present study showed that mean urinary KIM-1 level in children with stage 2-3 AKI was significantly higher than the non-AKI group. It was also found that the level of this biomarker in stage 1 AKI patients was only higher This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem M. Fazel et al. 4 than the non-AKI group when assessed within the first 12 hours of admission. However, the AUC of urinary KIM-1 in prediction of AKI was 0.69, which is in the poor to fair range. Pooled analysis in the present study showed that the urinary level of KIM-1 was significantly higher in children with AKI compared to non-AKI cases. However, with a closer look at the findings, we will find that the obtained SMD is often below 1, which is in the poor to moderate effect size range. Therefore, KIM-1 may not be a good biomarker for the prediction of AKI in children. In addition, the AUC of this biomarker being poor to fair generally indicates that the dis- criminatory accuracy of KIM-1 is moderate at best. Several methods have been suggested for assessing the discrimina- tory power of a biomarker. Although AUC calculation is the most common method in this field, it should be kept in mind that this test is a primary test and we require additional as- sessment such as sensitivity and specificity. However, sen- sitivity and specificity of KIM-1 were only reported in three studies. The sensitivity of KIM-1 to predict AKI in children was between 40% and 73.1% and its specificity was between 86% and 96.5%. This sensitivity and specificity were reported based on a wide range cut-off points. Therefore, we could not pool the data in this section. However, it seems that uri- nary KIM-1’s sensitivity to predict pediatric AKI is poor to fair. Other findings of the present study indicate the weakness of KIM-1 in differentiating patients at risk of AKI (stage 1 AKI) from non-AKI patients. It seems that the level of KIM-1 in the urine would increase significantly only when the patient is in the advanced stages of the AKI (injury or failure phase). This is a major limitation for KIM-1 in predicting AKI in children. In a systematic review with the aim of examining the value of KIM-1 in diagnosis of AKI in children and adults, Wang et al. showed that the AUC, sensitivity and specificity of KIM-1 in prediction of AKI after cardiac surgery were 0.71, 76% and 0.84%, respectively (15). Although the findings of the study by Wang et al. are in line with the findings of the present study, there are major differences between the two studies. First, Wang et al.’s study pooled the findings of studies on adults and children; and second, out of the 15 included studies, only 3 were studies on children. Therefore, the findings of the study by Wang et al. could not be generalized to the pediatric community. Along with KIM-1, there are other biomarkers such as cystatin C and NGAL that studies have cited as reli- able indicators of AKI. Three previous meta-analyses showed that urinary and serum levels of cystatin C and NGAL had good to excellent discriminatory accuracy in predicting AKI. In the first study, Nakhjavan-Shahraki et al. showed that sen- sitivity and specificity of serum cystatin C in predicting AKI in children were 85% and 61%, respectively. The AUC of serum and urine cystatin C in predicting AKI were 0.83 and 0.85, respectively (4). In the other two meta-analyses, Izadi et al. showed that the serum level of NGAL in predicting AKI was 87% sensitive and 88% specific, and its urinary level had a sensitivity and specificity of 92% in predicting AKI. The AUC of serum and urinary NGAL in AKI prediction was 0.94 and 0.97, respectively (5, 6). Therefore, it seems that the use of NGAL and cystatin C biomarkers in predicting AKI is supe- rior to KIM-1 in children. 6. Limitations In the beginning of the present study, it was decided to cal- culate the overall sensitivity, specificity, and diagnostic odds ratio of KIM-1 in predicting AKI in children, but after search- ing and entering studies it became clear that such analysis was not possible. On the other hand, out of the 13 included studies, 5 were case-controls. In this type of design, the re- search team is aware of the existence of AKI in patients from the beginning, and this may lead to some degree of bias. Also, since a wide range of cut-off points were reported for KIM-1 in the studies, we were unable to reach a unique cut-off point for urinary KIM-1 in predicting AKI in children. 7. Conclusion Based on available evidence, KIM-1 appears to have a mod- erate value in predicting childhood AKI. Since previous meta- analyses have shown urinary and serum biomarkers that have better discriminatory accuracy than KIM-1, it is better not to use urinary KIM-1 in predicting AKI in children. 8. Declarations 8.1. Acknowledgment We are grateful to Dr. Behnaz Bazargani and Prof. Nematol- lah Ataei for their valuable consultations 8.2. Author contributions Study design: Mahmoud Yousefifard, Mostafa Hosseini Data gathering: Mojtaba Fazel, Arash Sarveazad, Mahmoud Yousefifard Analysis and interpreting the results: Mahmoud Yousefifard, Mostafa Hosseini, Kosar Mohammad Ali Drafting the manuscript: Mahmoud Yousefifard Critically revising the paper: All authors All authors approved the final version of the manuscript and are accountable for all aspects of the work. Authors ORCID Mojtaba Fazel: 0000-0003-0463-4257 Arash Sarveazad: 0000-0001-9273-1940 Kosar Mohammad Ali: 0000-0001-5533-2924 Mahmoud Yousefifard: 0000-0001-5181-4985 Mostafa Hosseini: 0000-0002-1334-246X This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem 5 Archives of Academic Emergency Medicine. 2020; 8(1): e44 8.3. Funding This study was funded and supported by Tehran University of Medical Sciences (TUMS); Grant no. 95-04-184-33088. 8.4. Conflict of interest There is no conflict of interest. References 1. Coresh J, Selvin E, Stevens LA, Manzi J, Kusek JW, Eggers P, et al. Prevalence of chronic kidney disease in the United States. JAMA. 2007;298(17):2038-47. 2. Anavekar NS, McMurray JJ, Velazquez EJ, Solomon SD, Kober L, Rouleau J-L, et al. Relation between renal dys- function and cardiovascular outcomes after myocardial infarction. N Engl J Med. 2004;351(13):1285-95. 3. Devarajan P. Biomarkers for the early detection of acute kidney injury. Curr Opin Pediatr. 2011;23(2):194. 4. Nakhjavan-Shahraki B, Yousefifard M, Ataei N, Baikpour M, Ataei F, Bazargani B, et al. Accuracy of cystatin C in prediction of acute kidney injury in children; serum or urine levels: which one works better? A systematic review and meta-analysis. BMC Nephrol. 2017;18(1):120. 5. Izadi A, Yousefifard M, Nakhjavan-Shahraki B, Baikpour M, Mirzay Razaz J, Ataei N, et al. Value of plasma/serum neutrophil gelatinase-associated lipocalin in detection of pediatric acute kidney injury; a systematic review and meta-analysis. Int J Pediatr. 2016;4(11):3815-36. 6. Izadi A, Yousefifard M, Nakhjavan-Shahraki B, Baikpour M, Mirzay Razaz J, Hosseini M. Diagnostic value of Uri- nary Neutrophil Gelatinase-Associated Lipocalin (NGAL) in detection of pediatric acute kidney injury; a systematic review and meta-analysis. Int J Pediatr. 2016;4(11):3875- 95. 7. Mitsnefes MM, Kathman TS, Mishra J, Kartal J, Khoury PR, Nickolas TL, et al. Serum neutrophil gelatinase- associated lipocalin as a marker of renal function in children with chronic kidney disease. Pediatr Nephrol. 2007;22(1):101-8. 8. Mishra J, Dent C, Tarabishi R, Mitsnefes MM, Ma Q, Kelly C, et al. Neutrophil gelatinase-associated lipocalin (NGAL) as a biomarker for acute renal injury after cardiac surgery. The Lancet. 2005;365(9466):1231-8. 9. Nickolas TL, Barasch J, Devarajan P. Biomarkers in acute and chronic kidney disease. Curr Opin Nephrol Hyper- tens. 2008;17(2):127-32. 10. Han WK, Bailly V, Abichandani R, Thadhani R, Bon- ventre JV. Kidney Injury Molecule-1 (KIM-1): a novel biomarker for human renal proximal tubule injury. Kid- ney Int. 2002;62(1):237-44. 11. Waanders F, van Timmeren MM, Stegeman CA, Bakker SJ, van Goor H. Kidney injury molecule-1 in renal disease. J Pathol. 2010;220(1):7-16. 12. Sabbisetti VS, Waikar SS, Antoine DJ, Smiles A, Wang C, Ravisankar A, et al. Blood kidney injury molecule-1 is a biomarker of acute and chronic kidney injury and pre- dicts progression to ESRD in type I diabetes. J Am Soc Nephrol. 2014;25(10):2177-86. 13. Ucakturk A, Avci B, Genc G, Ozkaya O, Aydin M. Kidney injury molecule-1 and neutrophil gelatinase associated lipocalin in normoalbuminuric diabetic children. J Pedi- atr Endocrinol Metab. 2016;29(2):145-51. 14. Shao X, Tian L, Xu W, Zhang Z, Wang C, Qi C, et al. Diagnostic Value of Urinary Kidney Injury Molecule 1 for Acute Kidney Injury: A Meta-Analysis. PLoS One. 2014;9(1):e84131. 15. Wang H, Wang WJ, Zhou CC, Cen D. Accuracy of uri- nary kidney injury molecule-1 in predicting acute kid- ney injuries associated with cardiac surgery: a sys- tematic review and meta-analysis. Int J Clin Exp Med. 2019;12(6):6570-8. 16. Stroup DF, Berlin JA, Morton SC, Olkin I, Williamson GD, Rennie D, et al. Meta-analysis of observational studies in epidemiology. JAMA: the journal of the American Medi- cal Association. 2000;283(15):2008-12. 17. Yousefifard M, Baikpour M, Ghelichkhani P, Asady H, Darafarin A, Esfahani MRA, et al. Comparison of Ultra- sonography and Radiography in Detection of Thoracic Bone Fractures; a Systematic Review and Meta-Analysis. Emergency. 2016;4(2):55. 18. Yousefifard M, Baikpour M, Ghelichkhani P, Asady H, Nia KS, Jafari AM, et al. Screening Performance Char- acteristic of Ultrasonography and Radiography in De- tection of Pleural Effusion; a Meta-Analysis. Emergency. 2016;4(1):1. 19. Ebrahimi A, Yousefifard M, Kazemi HM, Rasouli HR, Asady H, Jafari AM, et al. Diagnostic accuracy of chest ultrasonography versus chest radiography for identifica- tion of pneumothorax: a systematic review and meta- analysis. Tanaffos. 2014;13(4):29-40. 20. Hosseini M, Yousefifard M, Aziznejad H, Nasirinezhad F. The Effect of Bone Marrow–Derived Mesenchymal Stem Cell Transplantation on Allodynia and Hyperalgesia in Neuropathic Animals: A Systematic Review with Meta- Analysis. Biol Blood Marrow Transplant. 2015;21(9):1537- 44. 21. Moher D, Liberati A, Tetzlaff J, Altman DG. Pre- ferred reporting items for systematic reviews and meta- analyses: the PRISMA statement. Ann Intern Med. 2009;151(4):264-9. 22. Whiting PF, Rutjes AWS, Westwood ME, Mallett S, Deeks JJ, Reitsma JB, et al. QUADAS-2: A Revised Tool for the Quality Assessment of Diagnostic Accuracy Studies. Ann Intern Med. 2011;155(8):529-36. This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem M. Fazel et al. 6 23. Higgins JP, Green S. Cochrane handbook for systematic reviews of interventions: John Wiley & Sons; 2011. 24. Egger M, Smith GD, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997;315(7109):629-34. 25. Askenazi DJ, Koralkar R, Hundley HE, Montesanti A, Par- war P, Sonjara S, et al. Urine biomarkers predict acute kid- ney injury in newborns. J Pediatr. 2012;161(2):270-5.e1. 26. Carvalho Pedrosa D, Macedo de Oliveira Neves F, Caval- cante Meneses G, Pinheiro Gomes Wirtzbiki G, da Costa Moraes CA, Costa Martins AM, et al. Urinary KIM-1 in children undergoing nephrotoxic antineoplastic treat- ment: a prospective cohort study. Pediatr Nephrol. 2015;30(12):2207-13. 27. Dong L, Ma Q, Bennett M, Devarajan P. Urinary biomark- ers of cell cycle arrest are delayed predictors of acute kid- ney injury after pediatric cardiopulmonary bypass. Pedi- atr Nephrol. 2017;32(12):2351-60. 28. Du Y, Zappitelli M, Mian A, Bennett M, Ma Q, Devara- jan P, et al. Urinary biomarkers to detect acute kidney in- jury in the pediatric emergency center. Pediatr Nephrol. 2011;26(2):267-74. 29. Gist KM, Goldstein SL, Wrona J, Alten JA, Basu RK, Cooper DS, et al. Kinetics of the cell cycle arrest biomark- ers (TIMP-2*IGFBP-7) for prediction of acute kidney in- jury in infants after cardiac surgery. Pediatr Nephrol. 2017;32(9):1611-9. 30. Kandur Y, Gonen S, Fidan K, Soylemezoglu O. Evaluation of urinary KIM-1, NGAL, and IL-18 levels in determining early renal injury in pediatric cases with hypercalciuria and/or renal calculi. Clin Nephrol. 2016;86(2):62-9. 31. Krawczeski CD, Goldstein SL, Woo JG, Wang Y, Piyapha- nee N, Ma Q, et al. Temporal relationship and predic- tive value of urinary acute kidney injury biomarkers af- ter pediatric cardiopulmonary bypass. J Am Coll Cardiol. 2011;58(22):2301-9. 32. Lagos-Arevalo P, Palijan A, Vertullo L, Devarajan P, Ben- nett MR, Sabbisetti V, et al. Cystatin C in acute kidney in- jury diagnosis: early biomarker or alternative to serum creatinine? Pediatr Nephrol. 2015;30(4):665-76. 33. McCaffrey J, Coupes B, Chaloner C, Webb NJ, Barber R, Lennon R. Towards a biomarker panel for the assess- ment of AKI in children receiving intensive care. Pediatr Nephrol. 2015;30(10):1861-71. 34. Parikh CR, Thiessen-Philbrook H, Garg AX, Kadiyala D, Shlipak MG, Koyner JL, et al. Performance of kidney in- jury molecule-1 and liver fatty acid-binding protein and combined biomarkers of AKI after cardiac surgery. Clin J Am Soc Nephrol. 2013;8(7):1079-88. 35. Peco-Antic A, Ivanisevic I, Vulicevic I, Kotur-Stevuljevic J, Ilic S, Ivanisevic J, et al. Biomarkers of acute kidney injury in pediatric cardiac surgery. Clin Biochem. 2013;46(13- 14):1244-51. 36. Sarafidis K, Tsepkentzi E, Agakidou E, Diamanti E, Taparkou A, Soubasi V, et al. Serum and urine acute kid- ney injury biomarkers in asphyxiated neonates. Pediatr Nephrol. 2012;27(9):1575-82. 37. Westhoff JH, Fichtner A, Waldherr S, Pagonas N, Seibert FS, Babel N, et al. Urinary biomarkers for the differenti- ation of prerenal and intrinsic pediatric acute kidney in- jury. Pediatr Nephrol. 2016;31(12):2353-63. 9. Appendix 1: Search query in PubMed "Acute Kidney Injury"[mh] OR "Renal Insufficiency"[mh] OR Acute Kidney Injury[tiab] OR Kidney Injuries, Acute[tiab] OR Kidney Injury, Acute[tiab] OR Acute Re- nal Injury[tiab] OR Acute Renal Injuries[tiab] OR Re- nal Injuries, Acute[tiab] OR Renal Injury, Acute[tiab] OR Renal Insufficiency, Acute[tiab] OR Acute Renal In- sufficiencies[tiab] OR Renal Insufficiencies, Acute[tiab] OR Acute Renal Insufficiency[tiab] OR Kidney Insuffi- ciency, Acute[tiab] OR Acute Kidney Insufficiencies[tiab] OR Kidney Insufficiencies, Acute[tiab] OR Acute Kid- ney Insufficiency[tiab] OR Kidney Failure, Acute[tiab] OR Acute Kidney Failures[tiab] OR Kidney Failures, Acute[tiab] OR Acute Renal Failure[tiab] OR Acute Re- nal Failures[tiab] OR Renal Failures, Acute[tiab] OR Re- nal Failure, Acute[tiab] OR Acute Kidney Failure[tiab] OR Acute Kidney Tubule Necrosis[TIAB])) AND ("Hepatitis A Virus Cellular Receptor 1"[mh] OR CD365 Antigen[tiab] OR Kidney Injury Molecule 1[tiab]) This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem 7 Archives of Academic Emergency Medicine. 2020; 8(1): e44 Table 1: Characteristics of the included studies Author; year; country Study type Setting Age Sample size No. of boys AKI definition Timing (hrs) Askenazi; 2012; USA Case-Control AKI suspected Neonates 33 17 AKI Network 0 to 96 Carvalho Pedrosa; 2015; Brazil Cohort Chemotherapy in- duced AKI <18 64 26 KDIGO 24, 48, 72, 96 Dong; 2017; USA Case-Control Post-cardiopulmonary surgery AKI <18 150 77 KDIGO 2, 6, 12 Du; 2010; USA Cohort AKI suspected 11.4 252 126 KDIGO 0 Gist; 2017; USA Cohort Post-cardiopulmonary surgery AKI <1 94 63 KDIGO 6 Kandur; 2016; Turkey Case-Control ICU admitted AKI 1 to 17 60 33 KDIGO 24 Krawczeski; 2011; USA Case-Control Post-cardiopulmonary surgery AKI <18 220 110 KDIGO 0, 6, 12, 24 Lagos-Arevalo; 2014; Canada Cohort AKI suspected < 18 160 58 KDIGO 0 to 24 McCaffrey; 2015; UK Cohort AKI suspected <18 49 26 pRIFLE criteria 0 Parikh; 2013; USA Cohort Post-cardiopulmonary surgery AKI <18 311 171 pRIFLE criteria 6, 12, 24, 48, 72, 96 Peco-Antic; 2013; Ser- bia Cohort Post-cardiopulmonary surgery AKI 1.6 112 65 pRIFLE criteria 2, 6, 24, 48 Sarafidis; 2012; Greece Case-Control Asphyxia-associated AKI Neonates 35 21 KDIGO 24, 72 Westhoff; 2016; Ger- many Cohort AKI suspected <10 80 32 pRIFLE criteria 0 AKI: Acute kidney injury; KDIGO: Kidney Disease Improving Global Outcomes; pRIFLE: Pediatric Risk, Injury, Failure, Loss of kidney function, and End-stage kidney disease; Timing: Time interval between admission and kidney injury molecule-1 assessment. Table 2: Quality assessment of included studies based on QUADAS-2 recommendations Author; year Risk of bias Applicability Patient Index test Reference Flow and Patient Index test Reference selection standard timing selection standard Askenazi; 2012 § © © © © © © Carvalho Pedrosa; 2015 © © § © © © © Dong; 2017 § © © © © © © Du; 2010 © © © © © © © Gist; 2017 © © § © © © © Kandur; 2016 § © © © © © © Lagos-Arevalo; 2014 © © © © © © © McCaffrey; 2015 © © © © © © © Peco-Antic; 2013 © © © © © © © Sarafidis; 2012 § © © © © © © Westhoff; 2016 © © © © © © © ©: Low risk pf bias, §: High risk of bias This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem M. Fazel et al. 8 Figure 1: Flow diagram of screening and selection of eligible studies. AKI: Acute kidney injury. This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem 9 Archives of Academic Emergency Medicine. 2020; 8(1): e44 Figure 2: Risk of bias and publication bias assessment of the included studies. There is no evidence of publication bias (p = 0.576). This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem M. Fazel et al. 10 Figure 3: Forest plot for standardized mean difference (SMD) of urine kidney injury molecule-1 (KIM-1) between acute kidney injury (AKI) patients with all severities (stage 1/risk, stage 2/injury, and stage 3/failure) and non-AKI patients at different time cut offs. The urinary level of KIM-1 in AKI-patients is higher than non-AKI patients. CI: Confidence interval. This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem 11 Archives of Academic Emergency Medicine. 2020; 8(1): e44 Figure 4: Forest plot for standardized mean difference (SMD) of urine kidney injury molecule-1 (KIM-1) between acute kidney injury (AKI) patients in stage 1/risk and non-AKI patients at different time cut offs. The urinary level of KIM-1 in AKI-patients with a severity of stage 1/risk is slightly higher than non-AKI patients only when assessed during the first 12-hours after admission. CI: Confidence interval. This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem M. Fazel et al. 12 Figure 5: Forest plot for standardized mean difference (SMD) of urine kidney injury molecule-1 (KIM-1) between acute kidney injury (AKI) patients with stage 2-3/injury-failure severity and non-AKI patients at different time cut offs. The urinary level of KIM-1 in AKI-patients with a severity of stage 2-3/risk is higher than non-AKI patients in all assessed time points. CI: Confidence interval. This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem 13 Archives of Academic Emergency Medicine. 2020; 8(1): e44 Figure 6: Area under the curve (AUC) of kidney injury molecule-1 (KIM-1) in diagnosis of acute kidney injury in children. The discriminatory power of KIM-1 in detection of acute kidney injury is poor to fair (AUC = 0.69; 95% confidence interval: 0.62 to 0.77). This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0). Downloaded from: http://journals.sbmu.ac.ir/aaem Introduction Methods Results Discrimination Discussion Limitations Conclusion Declarations References Appendix 1: Search query in PubMed