Archives of Academic Emergency Medicine. 2020; 8(1): e39 REV I EW ART I C L E Discriminatory Precision of Renal Angina Index in Pre- dicting Acute Kidney Injury in Children; a Systematic Re- view and Meta-Analysis Arash Abbasi1,2, Pardis Mehdipour Rabori3, Ramtin Farajollahi3, Kosar Mohammed Ali4, Nematollah Ataei1,2, Mahmoud Yousefifard5∗, Mostafa Hosseini1,6 † 1. Pediatric Chronic Kidney Disease Research Center, Tehran University of Medical Sciences, Tehran, Iran. 2. Department of Pediatrics, Division of Nephrology, Children’s Medical Center, Tehran University of Medical Sciences, Tehran, Iran. 3. Student Research Committee, Iran University of Medical Sciences, Tehran, Iran. 4. College of Medicine, University of Sulaimani, Sulaimani, Iraq. 5. Physiology Research Center, Iran University of Medical Sciences, Tehran, Iran. 6. Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran. Received: February 2020; Accepted: February 2020; Published online: 26 March 2020 Abstract: Introduction: There is still controversy over the value of renal angina index (RAI) in predicting acute renal failure (AKI) in children. Therefore, the present study aims to provide evidence by conducting a systematic review and meta-analysis on the value of RAI in this regard. Methods: An extensive search of Medline, Embase, Scopus and Web of Science databases was conducted by the end of January 2020 using words related to RAI and AKI. Two independent reviewers screened and summarized the related studies. Data were analysed using STATA 14.0 statistical program and discriminatory precision of RAI was assessed. Results: Data from 11 studies were included. These studies included data from 3701 children (60.41% boys). There were 752 children with AKI and 2949 non-AKI children. Pooled analysis showed that the area under the ROC curve of RAI in prediction of AKI was 0.88 [95% confidence interval (CI): 0.85 to 0.91]. Sensitivity and specificity of this tool in predicting AKI were 0.85% (95% CI: 0.74% to 0.92%) and 0.79% (95% CI: 0.69% to 0.89%), respectively. The diagnostic odds ratio of RAI was 20.40 (95% CI: 9.62 to 43.25). Conclusion: The findings of the present meta-analysis showed that RAI is a reliable tool in predicting AKI in children. Keywords: Acute Kidney Injuries; Renal Insufficiency; Severity of Illness Index; Child Cite this article as: Abbasi A, Mehdipour Rabori P, Farajollahi R, Mohamed Ali K, Ataei N, Yousefifard M, Hosseini M. Discriminatory Precision of Renal Angina Index in Predicting Acute Kidney Injury in Children; a Systematic Review and Meta-Analysis. Arch Acad Emerg Mede. 2020; 8(1): e39. 1. Introduction Acute kidney injury (AKI) is a serious problem in children and adolescents and can rapidly progress to chronic kidney dis- ease and result in the need for dialysis if not diagnosed in a timely manner. The prevalence of acute renal failure indi- ∗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: Department of Epidemiology and Biostatistics School of Public Health, Tehran University of Medical Sciences, Poursina Ave, Tehran, Iran; Email: mhossein110@yahoo.com cates that approximately 10% of children admitted to inten- sive care units develop AKI (1). The effect of this failure on mortality is significant (1, 2). Unfortunately, the onset and progression of AKI is often asymptomatic and its diagnosis is mainly based on functional biomarkers such as serum creati- nine. But in recent years, due to the limitations of creatinine, researchers are seeking an alternative method (1, 3, 8). Currently, several diagnostic methods for identifying chil- dren with kidney disease are available, but none of them pro- vide a correct picture in the early stages of the disease. Mean- while, the use of scoring systems such as the renal angina in- dex (RAI) has received much attention in recent years. RAI was first introduced by Basu et al. in 2014 to improve the 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 A. Abbasi et al. 2 prediction of AKI in critically ill children (9). This study has two phases of derivation and validation. The findings of this study suggest that RAI is an acceptable and simple criterion for identifying children at risk for AKI. Recent research sug- gests that RAI can diagnose AKI and predict the patient’s out- come. But there is still disagreement between studies and there is no consensus (10, 11). Therefore, the present study aims to provide evidence by conducting a systematic review and meta-analysis on the value of RAI in predicting AKI in children. 2. Methods 2.1. Study design The present study is a systematic review and meta-analysis on the diagnostic value of RAI in predicting AKI in children. The study was designed based on meta-analysis of observa- tional studies in epidemiology (MOOSE) statement (12). 2.2. Search strategy In the first step, the keywords associated with AKI and RAI were identified. Then by combining the related keywords us- ing standard tags and Boolean operators for each database, a systematic search was performed on Medline, Embase, Sco- pus, and Web of Science electronic databases until the end of January 2020. The search query used in Medline database is reported in Appendix 1. To find additional articles or un- published data, a manual-search was performed in the bibli- ography of the relevant studies, Google and Google Scholar. Applying this strategy resulted in the addition of two articles to the present study. 2.3. Selection criteria In the present study, diagnostic accuracy studies performed on renal angina index in diagnosis of acute renal failure in children were included. Inclusion criteria were confirmation of AKI via one of the standard methods, sensitivity and speci- ficity (from the article or by contacting the authors) being provided or true positive (TP), true negative (TN)), false pos- itive (FP) and false negative (FN) being provided. Both ret- rospective and prospective studies were included. Exclusion criteria were as follows: review studies, studies on adults, du- plicate studies (use of same dataset in two studies), and lack of non-AKI group. 2.4. Data collection and quality assessment After combining the search records and eliminating dupli- cates, two independent reviewers screened the abstracts and selected potentially relevant studies. Then, they assessed and summarized the full-text of eligible studies. In case of dis- agreement, a third reviewer evaluated the findings and ex- isting disagreement was resolved through discussion. Ex- tracted data included first author’s name, year of publication, country, demographic data of patients (age, sex), sample size, standard criterion for defining AKI, severity of AKI, RAI evalu- ation time, cut-offs used for RAI and finally sensitivity, speci- ficity, TP, TN, FP, and FN. The risk of bias was assessed using guidelines proposed in Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) (14). 2.5. Statistical analysis Statistical analyses were performed using STATA version 14.0 (Stata Corporation, College Station, TX). All studies were summarized and categorized based on sensitivity and speci- ficity or TP, TN, FP, and FN. Then, the discriminatory power of RAI in predicting children’s AKI was calculated using the "midas" command, which is a bivariate mixed-effects binary regression modelling framework. Results were reported as area under the summary receiver operating characteristics (SROC) curve (AUC) with 95% confidence interval (CI). In addition, sensitivity, specificity, negative and positive likeli- hood ratios and diagnostic odds ratio were calculated. Het- erogeneity between studies was assessed using I2 test and p value less than 0.1 was considered significant (indicating Heterogeneity). Publication bias across studies was also as- sessed using Deek’s funnel plot asymmetry test. 3. Results 3.1. Study characteristics The literature search yielded 2223 records, 2097 of which were non-duplicates. After the initial screening, full texts of 28 articles were studied in detail and finally, the data of 11 articles were included in the present study (15–25) (Fig- ure 1). All studies were cohorts. One article contained data from four separate cohorts (15). Therefore, the data of each cohort were reported separately. Three retrospec- tive cohorts and 11 prospective cohorts were included in the present study. The studied patients were ICU admitted in 12 cohorts. These studies included data from 3701 children (60.41% boys). There were 752 children with AKI and 2949 non-AKI children. The severity of AKI was severe in 12 co- horts. All studies had assessed RAI status at the time of ad- mission and used a cut-off point of 8 to predict AKI. Patients were followed up for 3 days in 13 cohorts and until discharge in one cohort. Table 1 shows the characteristics of the in- cluded studies. 3.2. Risk of bias and publication bias across studies Risk of bias assessment based on the QUADAS-2 guidelines showed that the risk of bias in patient selections and flow and timing was unclear in three cohorts. The risk of bias in refer- 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): e39 ence index was unclear in one cohort. Also, applicability of patient selection and reference standard was unclear in one cohort. Finally, analysis showed that there is no evidence for publication bias in the present study (p = 0.074) (Figure 2). 3.3. Discriminatory power of RAI in AKI Pooled analysis showed that the AUC of RAI in paediatric AKI prediction was 0.88 (95% CI: 0.85 to 0.91) (Figure 3). Sensi- tivity and specificity of this tool in predicting AKI were 0.85% (95% CI: 0.74% to 0.92%) and 0.79% (95% CI: 0.69% to 0.89%), respectively (Figure 4). Positive and negative likelihood ratio of RAI in predicting AKI in children were 3.96 (95% CI: 2.69 to 5.83) and 0.19 (95% CI: 0.11 to 0.34), respectively (Figure 5). Finally, the diagnostic odds ratio of RAI was 20.40 (95% CI: 9.62 to 43.25) (Figure 6). 4. Discussion Early detection of AKI in children can prevent persistent kid- ney damage such as chronic kidney failure and end-stage re- nal disease. For this purpose, the present meta-analysis ex- amined the discriminatory power of RAI in predicting AKI in children on admission. The findings showed that RAI is a re- liable tool in predicting AKI in children. Diagnostic odds ratio of RAI in predicting AKI was about 20, indicating its high applicability in management of the patients. However, there were 7.94% false negatives for RAI. Although RAI has high discriminatory precision, we should consider a proper solutions to reduce its false neg- ative rate. One of these solutions is to add other biomark- ers to the RAI model. In this regard, a study has shown that adding Syndecan-1 to RAI increases its predictive value (26). In another study, Basu et al. showed that the ad- dition of any of plasma neutrophil gelatinase-associated lipocalin (NGAL), matrix metalloproteinase-8 (MMP-8), and neutrophil elastase-2 (Ela-2) biomarkers increased the dis- criminatory power of RAI (27). However, further studies are still needed to investigate the cost-effectiveness of adding a new biomarker to RAI. The lowest sensitivity for RAI was reported in the study by the Basu et al. This multicenter study had the largest sample size among the included studies. The sensitivity and specificity of RAI in this study were 33% and 86%, respectively (16). The findings of this study may be outliers. We performed an ad- ditional analysis after excluding the Basu et al. article. The findings showed that omitting this article did not have a sig- nificant effect on the reported sensitivity (0.87 vs. 0.85) and specificity (0.78 vs. 0.79) of RAI in detection of AKI. In the present meta-analysis, 14 cohort studies (from 11 ar- ticles) were included, 3 of which were retrospective and 11 were prospective. The retrospective nature of these studies partly influenced the quality of the published articles and led to an unclear risk of bias in patient selection. In addition, one study (21) did not indicate the reference index used for classi- fication of children to AKI and non-AKI. Therefore, the status of this study in the reference index section was unclear. How- ever, the risk of bias and applicability of most studies were low, which is a strong point for the present study. 5. Conclusion The present meta-analysis summarized evidence on the dis- criminatory power of RAI at the time of admission in pre- dicting AKI in children and adolescents. The findings of this study showed that RAI is a reliable tool in predicting AKI in children. 6. Declarations 6.1. Acknowledgements We are grateful to Dr. Mastaneh Moghtaderi and Dr. Mojtaba Fazel for their valuable helps. 6.2. Authors Contributions Study design: Mahmoud Yousefifard, Mostafa Hosseini Data gathering: Arash Abbasi, Pardis Mehdipour Rabori, Ramtin Farajollahi Analysis and interpreting the result: Mahmoud Yousefifard, Mostafa Hosseini, Kosar Mohammad Ali Drafting the manuscript: Mahmoud Yousefifard, Kosar Mohammad Ali Critically revised the paper: All authors All authors approved the final version of manuscript and are accountable for all aspects of the work. Authors ORCIDs Arash Abbasi: 0000-0002-8859- Pardis Mehdipour Rabori: 0000-0001-5268-2267 Ramtin Farajollahi: 0000-0001-6728-3613 Kosar Mohammad Ali: 0000-0001-5533-2924 Nematollah Ataei: 0000-0001-9682-4394 Mahmoud Yousefifard: 0000-0001-5181-4985 Mostafa Hosseini: 0000-0002-1334-246X 6.3. Funding Support This study was funded and supported by Tehran University of Medical Sciences (TUMS); Grant no. 98-01-184-42136. 6.4. Conflict of Interest There is no conflict of interest. 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 A. Abbasi et al. 4 References 1. Schneider J, Khemani R, Grushkin C, Bart R. Serum cre- atinine as stratified in the RIFLE score for acute kidney injury is associated with mortality and length of stay for children in the pediatric intensive care unit. Critical care medicine. 2010;38(3):933-9. 2. Goldstein SL, Devarajan P. 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Appendix 1: Search query in Med- line via PubMed (Renal angina index[all field] OR Renal Angina[all field]) AND ("Acute Kidney Injury"[mh] OR "Renal Insuffi- ciency"[mh] OR "Kidney Tubular Necrosis, Acute"[mh] OR Acute Kidney Injuries [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 kidney cortex necrosis[tiab] OR Renal Insufficien- cies[tiab] OR Kidney Insufficiency[tiab] OR Insufficiency, Kidney[tiab] OR Kidney Insufficiencies[tiab] OR Kid- ney Failure[tiab] OR Failure, Kidney[tiab] OR Fail- ures, Kidney[tiab] OR Kidney Failures[tiab] OR Renal Failure[tiab] OR Failure, Renal[tiab] OR Failures, Re- nal[tiab] OR Renal Failures[tiab] OR Lower Nephron Nephrosis[tiab] OR Lower Nephron Nephroses[tiab] OR Nephron Nephroses, Lower[tiab] OR Nephron Nephro- sis, Lower[tiab] OR Nephroses, Lower Nephron[tiab] OR Nephrosis, Lower Nephron[tiab] OR Acute Kid- ney Tubular Necrosis[tiab] OR kidney cortical necro- sis[tiab] OR necrosis, kidney cortex[tiab] OR renal cor- tex necrosis[tiab] OR renal cortical necrosis[tiab] OR kidney tubule necrosis[tiab] OR kidney tubular epithe- lium necrosis[tiab] OR kidney tubular necrosis[tiab] OR kidney tubular necrosis, acute[tiab] OR kidney tubu- lus necrosis[tiab] OR necrosis, kidney tubule[tiab] OR necrotic tubulonephrosis[tiab] OR renal tubular cell necrosis[tiab] OR renal tubular necrosis[tiab] OR renal tubule necrosis[tiab] OR renal tubulus necrosis[tiab] OR tubular necrosis[tiab] OR tubule necrosis[tiab] OR tubule necrosis, kidney[tiab] OR tubulus necrosis[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 A. Abbasi et al. 6 Table 1: Characteristics of included studies Author; year; Country Study type Setting of patients Age No. AKI; non- AKI No. boys AKI defini- tion AKI sever- ity RAI time; cut- offs Follow up TP FP TN FN Basu, Cohort 1; 2014; USA RCS Sepsis 0.2 to 12.5 28; 116 83 KDIGO Severe 0; 8 3 21 31 85 7 Basu, Cohort 2; 2014; Canada RCS ICU admitted 0.2 to 12.5 12; 106 74 KDIGO Severe 0; 8 3 7 11 95 5 Basu, Cohort 3; 2014; Canada PCS ICU admitted 0.2 to 12.5 11; 97 64 KDIGO Severe 0; 8 3 10 27 70 1 Basu, Cohort 4; 2014; USA PCS ICU admitted 0.2 to 12.5 29; 185 134 KDIGO Severe 0; 8 3 27 118 67 2 Basu; 2018; Multicenter PCS ICU admitted 2 to 14.5 553; 1037 882 KDIGO Severe 0; 8 3 121 165 1057 247 Gawada; 2019; India PCS ICU admitted 0.1 to 12 114; 48 95 KDIGO Severe 0; 8 3 62 24 74 2 Hanson; 2020; USA PCS ICU admitted 0.1 to 25 17; 64 38 KDIGO Any AKI 0; 8 in- hospital 16 10 54 1 Kaur; 2018; India PCS ICU admitted 0.1 to 18 53; 360 301 KDIGO Severe 0; 8 3 25 44 336 8 Menon; 2016; USA PCS ICU admitted 0.2 to 25 15; 141 98 KDIGO Severe 0; 8 3 12 40 101 3 Perez; 2018; Philippine RCS Sepsis <19 90; 132 130 NR NR 0; 8 3 87 8 124 3 Sethi; 2018; India PCS ICU admitted 6.5±5.9 months 33; 69 69 KDIGO Severe 0; 8 3 27 21 48 6 Sundararaju; 2019; India PCS ICU admitted 0.1 to 18 29/256 189 KDIGO Severe 0; 8 3 24 117 139 5 Youssef; 2019; Egypt PCS ICU admitted 0.2 to 14 13; 40 34 pRIFLE Severe 0; 8 3 10 1 39 3 Zeid; 2019; Egypt PCS ICU admitted 0.2 to 7 10; 43 45 pRIFLE Severe 0; 8 3 9 16 27 1 AKI: Acute kidney injury; FN: False negative; FP: False positive; KDIGO: Kidney Disease Improving Global Outcomes; NR: Not reported; PCS: Prospective cohort study; pRIFLE: Pediatric Risk, Injury, Failure, Loss, End Stage Renal Disease; RCS: Retrospective cohort study; TN: True negative; TP: True positive; ICU: Intensive care unit. Table 2: Risk of bias assessment Risk of bias Applicability Patient selection Index test Reference standard Flow and timing Patient selection Index test Reference standard Basu; 2014; Cohort 1 A © © A © © © Basu; 2014; Cohort 2 A © © A © © © Basu; 2014; Cohort 3 © © © © © © © Basu; 2014; Cohort 4 © © © © © © © Basu; 2018 © © © © © © © Gawada; 2019 © © © © © © © Hanson; 2020 © © © © © © © Kaur; 2018 © © © © © © © Menon; 2016 © © © © © © © Perez; 2018 A © A A A © A Sethi; 2018 © © © © © © © Youssef; 2019 © © © © © © © Zeid; 2019 © © © © © © © ©: Low Risk; A: Unclear 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): e39 Figure 1: PRISMA flow diagram of present meta-analysis. 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 A. Abbasi et al. 8 Figure 2: Risk of bias and publication bias assessments. There is no evidence. 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): e39 Figure 3: Area under the summary receiver operative characteristics (SROC) curve (AUC). SENS: Sensitivity; SPEC: Specificity. 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 A. Abbasi et al. 10 Figure 4: Sensitivity and specificity of renal angina index in prediction of acute kidney injury. 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): e39 Figure 5: Positive and negative diagnostic likelihood ratios (DLR) of renal angina index in prediction of acute kidney injury. 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 A. Abbasi et al. 12 Figure 6: Diagnostic score and diagnostic odds ratio of renal angina index in prediction of acute kidney injury. 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 Introduction Methods Results Discussion Conclusion Declarations References Appendix 1: Search query in Medline via PubMed