Utility of Renal Angina Index as Compared to Kdigo in Critically Ill Children at a Tertiary Care Hospital in a Low Middle-income Country

Utility of Renal Angina Index as Compared to Kdigo in Critically Ill Children at a Tertiary Care Hospital in a Low Middle-income Country | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Utility of Renal Angina Index as Compared to Kdigo in Critically Ill Children at a Tertiary Care Hospital in a Low Middle-income Country Muhammad Nasheet Sagri, Bushra Rehman, Abida Akbar, Yusra Jamshed, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6556627/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Jan, 2026 Read the published version in BMC Pediatrics → Version 1 posted 4 You are reading this latest preprint version Abstract Objective: To evaluate the diagnostic accuracy of the Renal Angina Index (RAI) compared to Kidney Disease Improving Global Outcomes (KDIGO) criteria for early prediction of severe acute kidney injury (AKI) in critically ill children admitted to a tertiary care hospital in a low-middle-income country. Methods: This cross-sectional study included critically ill children aged 1 month to 18 years admitted to the Pediatric Intensive Care Unit (PICU) at Aga Khan University Hospital, Karachi, from January to July 2021. Patients with chronic kidney disease or transferred after 24 hours were excluded. RAI was calculated within 24 hours of PICU admission, and its performance in predicting severe AKI (KDIGO stage 2 or 3) was compared with clinical outcomes such as need for renal replacement therapy (RRT), PICU length of stay, and mortality. Results: Among 278 patients, 148 (53.2%) were RAI positive. RAI demonstrated a sensitivity of 82% and specificity of 87% for predicting severe AKI, with an area under the ROC curve (AUC) of 0.90. RAI-positive patients had significantly higher requirements for diuretics (41.2% vs. 3.1%), RRT (10.8% vs. 0%), longer PICU stay (6.33 ± 5.28 vs. 3.71 ± 2.83 days), and higher mortality (23.6% vs. 1.5%) compared to RAI-negative patients. A threshold of RAI ≥8 provided the best balance between sensitivity and specificity. Conclusion: The Renal Angina Index is a highly sensitive and specific tool for early prediction of severe AKI and associated poor outcomes in critically ill pediatric patients. Its implementation could significantly enhance AKI detection and management in resource-limited settings, where conventional biomarkers are often inaccessible. Figures Figure 1 INTRODUCTION Globally, acute kidney injury (AKI) is considered as a critical health condition that leads to higher rates of mortality and morbidity amongst children with serious illnesses ( 1 , 2 ). Evidence from the relevant study indicated that 10% of hospitalized children develop AKI of varying degrees ( 3 ). Depending on various etiological risk factors and different mechanisms causing renal injury it is classified into three categories pre-renal AKI, renal AKI, and post-renal AKI. Out of the three categories, the causes of pre-renal AKI, and post-renal AKI are extrinsic that disrupt normal renal function. Over the years, a tremendous amount of work has been done to search for chemical biomarkers to diagnose AKI. In many experimental models and human subjects, different urinary chemicals and serological makers were evaluated and validated over time ( 4 , 5 , 6 ). Impending renal dysfunction can be predicted by several biomarkers, however, their availability and applicability combined with financial burden remain a hindrance for use in resource-limited countries ( 7 ). Basu et al have demonstrated an incidence of renal angina positivity for acute kidney injury between 15–68% on day 0 ( 7 ). A slight rise in serum creatinine 0.3mg/dl parallels with increased severity of AKI leading to undesirable outcomes. Unfortunately, the time interval needed for serum creatinine to change is wide, limiting necessary renal protective measures to be taken earlier ( 8 )( 9 ). AKI is one of the independent determinants of prolonged hospital stay, and continued support of mechanical ventilation that leads to the development of chronic kidney disease, and increased morbidity, and mortality of the affected individuals ( 10 ). The renal angina index is an important tool for identifying renal dysfunction earlier in the clinical course ( 11 ). The concept of Renal angina is derived from angina pectoris. It alarms for impending kidney dysfunction, providing the risk and injury combined effect. As with angina pectoris however, clinical and laboratory equivalents do not exist for AKI, hence RAI was conceptualized ( 3 ). Renal angina index is a product of risks like the use of mechanical ventilation, inotropic support, and post-transplant patients and injury that can be identified by measuring the change in baseline serum creatinine or percentage of fluid accumulation. Previously many scoring systems (APACHE, PRISM, PELOAD, PSOFA, TISS) have failed to predict AKI accurately, and many of these critical care scores include AKI when it is already developed or when it has progressed to severe stages where paucity of practical actions fail to reverse the damage ( 12 ). Additionally, RAI has helped identify early clinical signs of AKI ( Kidney diseases improving global outcomes KDIGO stages 2–3) in PICU-admitted patients in multiple multicentric studies, but as these studies were performed predominantly in developed countries, we decided to validate RAI in our resource-limited country ( 13 ). METHODOLOGY Outcomes Evaluating the renal angina index's diagnostic accuracy in predicting the occurrence of severe AKI (classified as KDIGO AKI 2 or 3) on the first day of a PICU stay was the primary outcome of the study. [A rise in serum creatinine to ≥ 200% from baseline, a drop in estimated CrCl (eCrCl) of ≥ 50% from baseline, or a drop in urine output to < 0.5 ml/kg/hr for ≥ 8 hours are all considered KDIGO 2 or 3]. It was followed by the secondary outcomes of mortality, length of PICU stay, and the diagnostic accuracy of RAI in predicting the need for RRT. Methods This cross-sectional study was conducted over a period of seven months, from January 2021 to July 2021, in the Pediatric Intensive Care Unit at Aga Khan University Hospital's main campus and Karachi, Pakistan. The study included all the critically ill children, aged between 1 month to 18 years who we admitted to the PICU. Children with a prior diagnosis of chronic kidney disease (CKD), defined as abnormalities in kidney structure or function persisting for more than three months with at least one of the following documented or inferred—either a glomerular filtration rate (GFR) below 60 ml/min/1.73 m² or markers of kidney damage such as albuminuria were excluded. Children transferred from another intensive care unit after 24 hours of illness were also excluded. This study employed a nonprobability consecutive sampling technique. The sample size was calculated based on the reported incidence of AKI among children admitted to critical care units, which range from 15–68% ( 14 ). Using OpenEpi version 2 (open-source calculator, SSpropor), a sample size of 277 was determined by selecting the highest incidence value (68%) to ensure adequate precision. This sample size was calculated to achieve a 95% confidence level with a 5% margin of error, assuming a total critically ill pediatric population of 500. Data collection and calculations Files of patients admitted to PICU were reviewed and the information was recorded on performance and kept confidential. Day 0 was the 1st day of PICU admission. At admission baseline information comprising demographic information, including age, sex, primary diagnosis, and system involved [CNS, respiratory, cardiovascular, Gastrointestinal, Sepsis, and others (trauma or surgery)] was recorded. The renal angina index was calculated within 24 hours after admission and was used for the determination of acute kidney injury on the day of admission. Risk factors were given a score: 1, 3, or 5 (where 1 demonstrates the lowest risk and 5 the highest risk). Inotropic support and mechanical ventilation use were also included in the final score. Estimated glomerular filtration rate (eGFR) was assessed using creatinine clearance estimation (eCrCl), calculated by the Schwartz Eq. (15), for the determination of the Renal Angina Index (RAI). The RAI (ranging from 1 to 40) was derived by multiplying the risk score by the injury score, using either the FO% score or GFR score—whichever indicated the greater severity. An RAI score of ≥ 8 was considered renal angina positive (RA+), while a score of < 8 was classified as renal angina negative (RA−). RESULTS 278 patients were admitted to the PICU and included in the study during the period. 58.27% (n 162) were males. Sepsis was the most common diagnosis (28.1%) in patients admitted to PICU, followed by respiratory pathology constituting 26.3% of patients. Other pathologies reported were CNS at 17.26%, GI at 12.2%, and CVS and Trauma/GS contributing to 8.63% and 7.6%. 44.6% of patients were on mechanical ventilation, and 37.4% were administered inotropes. Out of these, 78.2% and 82.7% respectively were RAI positive. While only 4 patients (1.4%) underwent hematopoietic stem cell transplantation. 161 (57.9%) patients had AKI. Among these 278 patients, 148 were RAI positive while 130 (46.76%) were RAI negative. The diagnostic performance of the Renal Angina Index (RAI) in predicting AKI was evaluated using a confusion matrix and standard diagnostic metrics. RAI demonstrated strong predictive capability, with a sensitivity of 82% and specificity of 87%, indicating its ability to correctly identify patients at risk for AKI while minimizing false positives. The positive predictive value (PPV) of 90% suggests that the majority of RAI-positive patients developed AKI, while the negative predictive value (NPV) of 78% implies that a negative RAI score, although less effective at ruling out AKI, still provides reasonable predictive value. As depicted in Fig. 1 , the ROC curve demonstrates excellent diagnostic accuracy, with an area under the curve (AUC) of 0.90 (95% CI: 0.86–0.93). This result indicates that RAI provides a reliable measure for distinguishing between patients with and without AKI. The high AUC value reflects the strong overall sensitivity and specificity of the RAI in this population. Overall, 65 patients (23.4%) required diuretic therapy. Of these, 4 patients were RAI negative while 61 (41.2%) were RAI positive. The ROC analysis yielded an AUC of 0.87 (95% CI: 0.83–0.91), confirming a strong association between the prediction and worse outcome. 10.8% of RAI-positive individuals required RRT, compared to 0% in RAI-negative individuals. The AUC of 0.85 (95% Ci: 0.79–0.90) indicated RAI’s utility in predicting severe renal dysfunction. The length of stay in PICU was significantly longer in patients with RAI-positive profile, 6.33 \(\:\pm\:\) 5.28 days as compared to RAI-negative patients (3.71 \(\:\pm\:\) 2.83 days) (p=0.004). Mortality in RAI-positive patients was notably higher, 23.6% as compared to 1.5% in RAI-negative patients. The AUC for mortality prediction was 0.85 (95% CI: 0.80–0.89). It can be confidently inferred that RAI is a good and reliable predictor of worst outcomes in patients with AKI. RAI \(\:\ge\:\:\) 8 was the optimal threshold, balancing sensitivity and specificity, 82% and 87% respectively making it ideal for early AKI detection. Higher thresholds (RAI ≥ 12, RAI ≥ 20, RAI ≥ 40) improved specificity (98–99%), making them useful for confirming severe AKI cases rather than early detection as sensitivity decreased to 63%, 46%, and 46% respectively. Table 2 outlines the predictability of RAI at different thresholds. DISCUSSION Our study demonstrated that RAI is a strong reliable predictor of renal injury in critically ill children, with high sensitivity (82%) and specificity (87%). RAI outperformed KDIGO in all stages. With a threshold of ≥ 8 a strong predictive diagnostic accuracy was effectively seen (AUC = 0.90). Gawadia et al also reported sensitivity and specificity at RAI ≥ 8 96.9% and 75.5% respectively at day 0 ( 16 ). Whereas Basu et al., reported an AUC of 0.89 aligning with our results ( 17 ). Zulu et al., on the contrary, reported sensitivity and specificity at RAI ≥ 8 being 55.6% and 85.6% respectively, and at ≥ 12 being 33.6% and 94.2% ( 18 ). RAI had a poor positive predictive value being 40% and 50% respectively thresholds of 8 and 12. Our results also indicate that RAI thresholds at the lower end, i.e. 3–5, are useful for early risk stratification. The findings of our study also correspond with the existing evidence ( 19 ). They reported RAI ≥ 8 demonstrating a sensitivity of 82.8% and lower thresholds ( 3 – 5 ) being 100% sensitive. However, it is noteworthy to highlight here the specificity at lower thresholds being 21.5 and 23.8% respectively in their study. In parallel, our study demonstrated specificities to be 56% and 58%. The high negative predictive value (NPV = 97%) at RAI ≥ 12 or ≥ 20 suggests that these thresholds may be better suited for confirming severe AKI cases, whereas lower thresholds like RAI ≥ 3 (100% sensitivity, 21.5% specificity) are better for early screening. Additionally, RAI-positive patients had worse outcomes like diuretics use, longer PICU stay, need for RRT, and higher mortality rates. Our findings suggested that RAI detects AKI earlier as compared to conventional screening markers like serum creatinine, which often shows delayed elevation. Given that AKI is one of the major morbidities developing in critically ill patients, it is of utmost importance that detection of AKI should be confirmed as early in the clinical course as possible. In resource-limited settings, including leading centers in our country, we lack testing of biomarkers to detect AKI early in the course. Hence, a need for reliable bedside indicators is the need for time. RAI is one such indicator that has not only proven its reliability over the period, as evident by multiple study findings. Its reasonable positive predictive value (PPV) further supports its utility in clinical decision-making. Incorporating RAI as a part of regular assessment will not only help detect RAI earlier but also aid in better management of AKI. To our knowledge, no previous studies evaluating RAI in critically ill pediatric patients have been conducted in Pakistan, highlighting the novelty and importance of our findings. Our study does have limitations, as we conducted it in a single center and for a relatively short period. Moreover, we did not calculate and check RAI's reliability on Day 3 as compared to other previous studies. This is because of a lack of resources and data availability that we were unable to do. Nevertheless, we have put forward a foundation for further research to be conducted and a way to address these gaps. In our opinion, a larger, multi-center observational study being conducted over a longer period and having RAI tested on day 3 as well, will provide good quality results. In conclusion, our study has given a way forward for upcoming studies to be conducted and laid down the idea of introducing RAI as a regular indicator for predicting acute kidney injury in critically ill patients, specifically the pediatric population. Declarations Ethics Approval Statement: This study was approved by the Ethical Review Committee of Aga Khan University Hospital Pakistan, with the reference number 2021-5913-17386. Data was collected after ethical review committee exemption and all records were kept confidential and without identifiers Author Contribution All authors have made substantial contributions to the conception, design, data acquisition, analysis, and interpretation of the work. All authors were involved in drafting and revising the manuscript critically for important intellectual content. All authors have read and approved the final version of the manuscript and agree to be accountable for all aspects of the work, ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.Author Contributions:MNS: Conceptualization, study design, data collection, data analysis, manuscript writing, critical revision, and final approval.BR: Study design, data collection, manuscript drafting, and critical revision.AA: Data acquisition, statistical analysis support, manuscript editing.YJ: Data analysis, methodology development, manuscript revision.SZ: Data management, technical support, statistical consultation.SI: Literature review, critical revision, and final editing support.All authors agree to be listed as authors and to the order of authorship. References Mehta RL, Burdmann EA, Cerdá J, Feehally J, Finkelstein F, García-García G, et al. Recognition and management of acute kidney injury in the International Society of Nephrology 0by25 Global Snapshot: a multinational cross-sectional study. Lancet . 2016 May 14;387(10032):2017-25. Susantitaphong P, Cruz DN, Cerdá J, Abulfaraj M, Alqahtani F, Koulouridis I, et al. World incidence of AKI: a meta-analysis. Clin J Am Soc Nephrol . 2013 Sep 6;8(9):1482-93. Kaur R, Dhooria GS, Pooni PA, Bhat D, Bhargava S, Kakkar S, et al. Utilization of the renal angina index in PICU of a developing country for prediction of subsequent severe acute kidney injury. Pediatr Nephrol . 2018 Nov;33(11):2185-91. Miller TR, Anderson RJ, Linas SL, Henrich WL, Berns AS, Gabow PA, et al. Urinary diagnostic indices in acute renal failure: a prospective study. Ann Intern Med . 1978 Jul;89(1):47-50. Carvounis CP, Nisar S, Guro-Razuman S. Significance of the fractional excretion of urea in the differential diagnosis of acute renal failure. Kidney Int . 2002 Dec;62(6):2223-9. Esson ML, Schrier RW. Diagnosis and treatment of acute tubular necrosis. Ann Intern Med . 2002 Nov 5;137(9):744-52. Basu RK, Kaddourah A, Terrell T, Mottes T, Arnold P, Jacobs J, et al. Assessment of worldwide acute kidney injury, renal angina and epidemiology in critically ill children (AWARE): study protocol for a prospective observational study. BMC Nephrol . 2015 Dec 1;16(1):24. Hoste EA, Kellum JA. Incidence, classification, and outcomes of acute kidney injury. In: Ronco C, Bellomo R, Kellum JA, editors. Acute Kidney Injury . Basel: Karger; 2007. p. 32-8. (Contrib Nephrol; vol. 156). Zappitelli M, Bernier PL, Saczkowski RS, Tchervenkov CI, Gottesman R, Dancea A, et al. A small post-operative rise in serum creatinine predicts acute kidney injury in children undergoing cardiac surgery. Kidney Int . 2009 Oct;76(8):885-92. Rady HI, Mohamed SA, Mohssen NA, ElBaz M. Application of different scoring systems and their value in pediatric intensive care unit. Egypt Pediatr Assoc Gaz . 2014 Sep;62(3-4):59-64. Menon S, Goldstein SL, Mottes T, Fei L, Kaddourah A, Terrell T, et al. Urinary biomarker incorporation into the renal angina index early in intensive care unit admission optimizes acute kidney injury prediction in critically ill children: a prospective cohort study. Nephrol Dial Transplant . 2016 Apr 1;31(4):586-94. Goldstein SL, Chawla LS. Renal angina. Clin J Am Soc Nephrol . 2010 May 1;5(5):943-9. Lee HA, Seo YS. Current knowledge about biomarkers of acute kidney injury in liver cirrhosis. Clin Mol Hepatol . 2022 Jan;28(1):31-44. Smith LH Jr, Post RS, Teschan PE, Abernathy RS, Davis JH, Gray DM, et al. Post-traumatic renal insufficiency in military casualties: II. Management, use of an artificial kidney, prognosis. Am J Med . 1955 Feb 1;18(2):187-98. Teschan PE, Post RS, Smith LH Jr, Abernathy RS, Davis JH, Gray DM, et al. Post-traumatic renal insufficiency in military casualties: I. Clinical characteristics. Am J Med . 1955 Feb 1;18(2):172-86. Ricci Z, Ronco C. New insights in acute kidney failure in the critically ill. Swiss Med Wkly . 2012 Aug 13;142:w13662. Gawadia J, Mishra K, Kumar M, Saikia D. Prediction of severe acute kidney injury using renal angina index in a pediatric intensive care unit. Indian Pediatr . 2019 Aug;56(8):647-52. Zulu C, Mwaba C, wa Somwe S. The renal angina index accurately predicts low risk of developing severe acute kidney injury among children admitted to a low-resource pediatric intensive care unit. Ren Fail . 2023 Dec 22;45(2):2252095. Sundararaju S, Sinha A, Hari P, Lodha R, Bagga A. Renal angina index in the prediction of acute kidney injury in critically ill children. Asian J Pediatr Nephrol . 2019 Jan 1;2(1):25-3 Tables Table 1- Basic demographics and risk factors Characteristic Overall n (%) RA - n (%) RA + n (%) Gender Male 162 (58.27%) 82 (63.07%) 80 (54.05%) Admission Reason CNS 48 (17.266%) 29 (22.3%) 19 (12.8%) CVS 24 (8.633%) 5 (3.8%) 19 (12.8%) GI 34 (12.2%) 13 (10.0%) 21 (14.2%) Respiratory 73 (26.3%) 43 (33.1%) 30 (20.3%) Sepsis 78 (28.1%) 27 (20.8%) 51 (34.5%) Trauma/Surgery (T/S) 21 (7.6%) 13 (10.0%) 8 (5.4%) Risk Factors Inotropes 104 (37.4%) 18 (13.8%) 86 (58.1%) Mechanical Ventilation (MV) 124 (44.6%) 27 (20.8%) 97 (65.5%) HSCT 4 (1.4%) 2 (1.5%) 2 (1.4%) Table 2- Age distribution and outcomes Age Group Overall n (%) RA - n (%) RA + n (%) Chi-Square Value P-Value 0-12 months 73 (26.3%) 27 (20.8%) 46 (31.1%) 2.774 0.096 1-5 years 93 (33.5%) 51 (39.2%) 42 (28.4%) 2.429 0.119 6-10 years 49 (17.6%) 23 (17.7%) 26 (17.6%) 0.0008 0.977 11-15 years 46 (16.5%) 19 (14.6%) 27 (18.2%) 0.546 0.459 16-18 years 17 (6.1%) 10 (7.7%) 7 (4.7%) 1.043 0.307 O utcomes Diuretics 65 (23.4%) 4 (3.1%) 61 (41.2%) 43.069 <0.00001 Renal Replacement Therapy (RRT) 16 (5.8%) 0 (0%) 16 (10.8%) 14.118 0.00017 PICU Length of Stay (days) 5.10 ± 4.503 3.71 ± 2.827 6.33 ± 5.288 41.847 0.004 Mortality 37 (13.3%) 2 (1.5%) 35 (23.6%) 25.414 <0.00001 Table 3- RAI threshold variance RAI Threshold Sensitivity Specificity PPV NPV AUC RAI ≥ 3 100% 56% 76% 100% — RAI ≥ 5 82% 58% 73% 70% — RAI ≥ 6 82% 87% 90% 78% 0.90 RAI ≥ 8 82% 87% 90% 78% 0.90 RAI ≥ 10 63% 88% 88% 63% — RAI ≥ 12 63% 98% 98% 66% 0.92 RAI ≥ 20 46% 99% 99% 57% 0.94 RAI ≥ 40 46% 99% 99% 57% — Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 10 Jan, 2026 Read the published version in BMC Pediatrics → Version 1 posted Editorial decision: Revision requested 19 May, 2025 Editor assigned by journal 15 May, 2025 Submission checks completed at journal 15 May, 2025 First submitted to journal 29 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6556627","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":451987452,"identity":"12aa5b21-2fea-45f8-80fa-1f584e03e2ac","order_by":0,"name":"Muhammad Nasheet Sagri","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYHACNgQzoYLBAERLEKHFAKrlDMlaGNuI0GLOfvzZgw8Vfxjk249f+/Bwnp2xwQHmg7d58Gix7MkxN5xxxoDB4ExO8YzEbclmBgfYkq3xaTE4kMMmzdsG1MKQk8yQuI3ZxuAAj5k0Xi3nnz+T/vvPgEG+/w1Qy5x6oBb+b/i13Egwk2ZsAHr6RvphhsSGw0CH8bAR0PLGTLLnmDEPkMHMkHDsuLHkYTZjyzl4HZb+TOJHjZycfH/6Y8YfNdWGfcebH954g0cLDABdwgONHGYilEMB+wPi1Y6CUTAKRsGIAgBOfUmkHpmnfgAAAABJRU5ErkJggg==","orcid":"","institution":"Aga Khan University Hospital","correspondingAuthor":true,"prefix":"","firstName":"Muhammad","middleName":"Nasheet","lastName":"Sagri","suffix":""},{"id":451987453,"identity":"6c5eaece-b4d8-4a8c-82ef-85f98fe5b294","order_by":1,"name":"Bushra Rehman","email":"","orcid":"","institution":"Aga Khan University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Bushra","middleName":"","lastName":"Rehman","suffix":""},{"id":451987454,"identity":"661a44e4-7b3f-431c-8cb6-41c75ab1d6d2","order_by":2,"name":"Abida Akbar","email":"","orcid":"","institution":"Aga Khan University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Abida","middleName":"","lastName":"Akbar","suffix":""},{"id":451987457,"identity":"f6085e1b-630c-4a08-a5da-d17a71f43208","order_by":3,"name":"Yusra Jamshed","email":"","orcid":"","institution":"Aga Khan University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yusra","middleName":"","lastName":"Jamshed","suffix":""},{"id":451987458,"identity":"42bc4d1b-ffb9-47fc-ae9d-e21793aa735e","order_by":4,"name":"Sumaiyah Zahid","email":"","orcid":"","institution":"National University of Computer and Emerging Sciences, FAST-NUCES","correspondingAuthor":false,"prefix":"","firstName":"Sumaiyah","middleName":"","lastName":"Zahid","suffix":""},{"id":451987459,"identity":"469735cc-077b-4767-8df5-bdbdd420362d","order_by":5,"name":"Sidra Ishaque","email":"","orcid":"","institution":"Aga Khan University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Sidra","middleName":"","lastName":"Ishaque","suffix":""}],"badges":[],"createdAt":"2025-04-29 12:53:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6556627/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6556627/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12887-025-06106-5","type":"published","date":"2026-01-10T15:58:03+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82168747,"identity":"be0fed91-e391-40a2-ba7e-3470d037a8ba","added_by":"auto","created_at":"2025-05-07 09:33:33","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":34125,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve illustrating the diagnostic performance of the Renal Angina Index (RAI) for AKI prediction,compared against the kidney disease: Improving Global Outcomes (KDIGO) criteria.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6556627/v1/d71df6b9d46681a9f79baca0.jpeg"},{"id":100069549,"identity":"5bf52387-0d68-46b0-9e2c-e337d529dbb9","added_by":"auto","created_at":"2026-01-12 16:14:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":589702,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6556627/v1/6d4453a7-7d46-4196-9f5a-6db3eddc486a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eUtility of Renal Angina Index as Compared to Kdigo in Critically Ill Children at a Tertiary Care Hospital in a Low Middle-income Country\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eGlobally, acute kidney injury (AKI) is considered as a critical health condition that leads to higher rates of mortality and morbidity amongst children with serious illnesses (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Evidence from the relevant study indicated that 10% of hospitalized children develop AKI of varying degrees (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Depending on various etiological risk factors and different mechanisms causing renal injury it is classified into three categories pre-renal AKI, renal AKI, and post-renal AKI. Out of the three categories, the causes of pre-renal AKI, and post-renal AKI are extrinsic that disrupt normal renal function. Over the years, a tremendous amount of work has been done to search for chemical biomarkers to diagnose AKI. In many experimental models and human subjects, different urinary chemicals and serological makers were evaluated and validated over time (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Impending renal dysfunction can be predicted by several biomarkers, however, their availability and applicability combined with financial burden remain a hindrance for use in resource-limited countries (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBasu et al have demonstrated an incidence of renal angina positivity for acute kidney injury between 15\u0026ndash;68% on day 0 (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). A slight rise in serum creatinine 0.3mg/dl parallels with increased severity of AKI leading to undesirable outcomes. Unfortunately, the time interval needed for serum creatinine to change is wide, limiting necessary renal protective measures to be taken earlier (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e)(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). AKI is one of the independent determinants of prolonged hospital stay, and continued support of mechanical ventilation that leads to the development of chronic kidney disease, and increased morbidity, and mortality of the affected individuals (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe renal angina index is an important tool for identifying renal dysfunction earlier in the clinical course (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). The concept of Renal angina is derived from angina pectoris. It alarms for impending kidney dysfunction, providing the risk and injury combined effect. As with angina pectoris however, clinical and laboratory equivalents do not exist for AKI, hence RAI was conceptualized (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Renal angina index is a product of risks like the use of mechanical ventilation, inotropic support, and post-transplant patients and injury that can be identified by measuring the change in baseline serum creatinine or percentage of fluid accumulation.\u003c/p\u003e \u003cp\u003ePreviously many scoring systems (APACHE, PRISM, PELOAD, PSOFA, TISS) have failed to predict AKI accurately, and many of these critical care scores include AKI when it is already developed or when it has progressed to severe stages where paucity of practical actions fail to reverse the damage (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Additionally, RAI has helped identify early clinical signs of AKI ( Kidney diseases improving global outcomes KDIGO stages 2\u0026ndash;3) in PICU-admitted patients in multiple multicentric studies, but as these studies were performed predominantly in developed countries, we decided to validate RAI in our resource-limited country (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e"},{"header":"METHODOLOGY","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eOutcomes\u003c/h2\u003e \u003cp\u003eEvaluating the renal angina index's diagnostic accuracy in predicting the occurrence of severe AKI (classified as KDIGO AKI 2 or 3) on the first day of a PICU stay was the primary outcome of the study. [A rise in serum creatinine to \u0026ge;\u0026thinsp;200% from baseline, a drop in estimated CrCl (eCrCl) of \u0026ge;\u0026thinsp;50% from baseline, or a drop in urine output to \u0026lt;\u0026thinsp;0.5 ml/kg/hr for \u0026ge;\u0026thinsp;8 hours are all considered KDIGO 2 or 3]. It was followed by the secondary outcomes of mortality, length of PICU stay, and the diagnostic accuracy of RAI in predicting the need for RRT.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMethods\u003c/h3\u003e\n\u003cp\u003e This cross-sectional study was conducted over a period of seven months, from January 2021 to July 2021, in the Pediatric Intensive Care Unit at Aga Khan University Hospital's main campus and Karachi, Pakistan. The study included all the critically ill children, aged between 1 month to 18 years who we admitted to the PICU.\u003c/p\u003e \u003cp\u003eChildren with a prior diagnosis of chronic kidney disease (CKD), defined as abnormalities in kidney structure or function persisting for more than three months with at least one of the following documented or inferred\u0026mdash;either a glomerular filtration rate (GFR) below 60 ml/min/1.73 m\u0026sup2; or markers of kidney damage such as albuminuria were excluded. Children transferred from another intensive care unit after 24 hours of illness were also excluded.\u003c/p\u003e \u003cp\u003eThis study employed a nonprobability consecutive sampling technique. The sample size was calculated based on the reported incidence of AKI among children admitted to critical care units, which range from 15\u0026ndash;68% (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Using OpenEpi version 2 (open-source calculator, SSpropor), a sample size of 277 was determined by selecting the highest incidence value (68%) to ensure adequate precision. This sample size was calculated to achieve a 95% confidence level with a 5% margin of error, assuming a total critically ill pediatric population of 500.\u003c/p\u003e\n\u003ch3\u003eData collection and calculations\u003c/h3\u003e\n\u003cp\u003eFiles of patients admitted to PICU were reviewed and the information was recorded on performance and kept confidential. Day 0 was the 1st day of PICU admission. At admission baseline information comprising demographic information, including age, sex, primary diagnosis, and system involved [CNS, respiratory, cardiovascular, Gastrointestinal, Sepsis, and others (trauma or surgery)] was recorded. The renal angina index was calculated within 24 hours after admission and was used for the determination of acute kidney injury on the day of admission. Risk factors were given a score: 1, 3, or 5 (where 1 demonstrates the lowest risk and 5 the highest risk). Inotropic support and mechanical ventilation use were also included in the final score.\u003c/p\u003e \u003cp\u003eEstimated glomerular filtration rate (eGFR) was assessed using creatinine clearance estimation (eCrCl), calculated by the Schwartz Eq.\u0026nbsp;(15), for the determination of the Renal Angina Index (RAI). The RAI (ranging from 1 to 40) was derived by multiplying the risk score by the injury score, using either the FO% score or GFR score\u0026mdash;whichever indicated the greater severity. An RAI score of \u0026ge;\u0026thinsp;8 was considered renal angina positive (RA+), while a score of \u0026lt;\u0026thinsp;8 was classified as renal angina negative (RA\u0026minus;).\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e278 patients were admitted to the PICU and included in the study during the period. 58.27% (n 162) were males. Sepsis was the most common diagnosis (28.1%) in patients admitted to PICU, followed by respiratory pathology constituting 26.3% of patients. Other pathologies reported were CNS at 17.26%, GI at 12.2%, and CVS and Trauma/GS contributing to 8.63% and 7.6%. 44.6% of patients were on mechanical ventilation, and 37.4% were administered inotropes. Out of these, 78.2% and 82.7% respectively were RAI positive. While only 4 patients (1.4%) underwent hematopoietic stem cell transplantation.\u003c/p\u003e\n\u003cp\u003e161 (57.9%) patients had AKI. Among these 278 patients, 148 were RAI positive while 130 (46.76%) were RAI negative. The diagnostic performance of the Renal Angina Index (RAI) in predicting AKI was evaluated using a confusion matrix and standard diagnostic metrics. RAI demonstrated strong predictive capability, with a sensitivity of 82% and specificity of 87%, indicating its ability to correctly identify patients at risk for AKI while minimizing false positives. The positive predictive value (PPV) of 90% suggests that the majority of RAI-positive patients developed AKI, while the negative predictive value (NPV) of 78% implies that a negative RAI score, although less effective at ruling out AKI, still provides reasonable predictive value.\u003c/p\u003e\n\u003cp\u003eAs depicted in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, the ROC curve demonstrates excellent diagnostic accuracy, with an area under the curve (AUC) of 0.90 (95% CI: 0.86\u0026ndash;0.93). This result indicates that RAI provides a reliable measure for distinguishing between patients with and without AKI. The high AUC value reflects the strong overall sensitivity and specificity of the RAI in this population.\u003c/p\u003e\n\u003cp\u003eOverall, 65 patients (23.4%) required diuretic therapy. Of these, 4 patients were RAI negative while 61 (41.2%) were RAI positive. The ROC analysis yielded an AUC of 0.87 (95% CI: 0.83\u0026ndash;0.91), confirming a strong association between the prediction and worse outcome. 10.8% of RAI-positive individuals required RRT, compared to 0% in RAI-negative individuals. The AUC of 0.85 (95% Ci: 0.79\u0026ndash;0.90) indicated RAI\u0026rsquo;s utility in predicting severe renal dysfunction.\u003c/p\u003e\n\u003cp\u003eThe length of stay in PICU was significantly longer in patients with RAI-positive profile, 6.33\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e5.28 days as compared to RAI-negative patients (3.71\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\pm\\:\\)\u003c/span\u003e\u003c/span\u003e2.83 days) (p=0.004). Mortality in RAI-positive patients was notably higher, 23.6% as compared to 1.5% in RAI-negative patients. The AUC for mortality prediction was 0.85 (95% CI: 0.80\u0026ndash;0.89). It can be confidently inferred that RAI is a good and reliable predictor of worst outcomes in patients with AKI.\u003c/p\u003e\n\u003cp\u003eRAI \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\ge\\:\\:\\)\u003c/span\u003e\u003c/span\u003e8 was the optimal threshold, balancing sensitivity and specificity, 82% and 87% respectively making it ideal for early AKI detection. Higher thresholds (RAI \u0026ge; 12, RAI \u0026ge; 20, RAI \u0026ge; 40) improved specificity (98\u0026ndash;99%), making them useful for confirming severe AKI cases rather than early detection as sensitivity decreased to 63%, 46%, and 46% respectively. Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e outlines the predictability of RAI at different thresholds.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eOur study demonstrated that RAI is a strong reliable predictor of renal injury in critically ill children, with high sensitivity (82%) and specificity (87%). RAI outperformed KDIGO in all stages. With a threshold of \u0026ge;\u0026thinsp;8 a strong predictive diagnostic accuracy was effectively seen (AUC\u0026thinsp;=\u0026thinsp;0.90).\u003c/p\u003e \u003cp\u003eGawadia et al also reported sensitivity and specificity at RAI\u0026thinsp;\u0026ge;\u0026thinsp;8 96.9% and 75.5% respectively at day 0 (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Whereas Basu et al., reported an AUC of 0.89 aligning with our results (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Zulu et al., on the contrary, reported sensitivity and specificity at RAI\u0026thinsp;\u0026ge;\u0026thinsp;8 being 55.6% and 85.6% respectively, and at \u0026ge;\u0026thinsp;12 being 33.6% and 94.2% (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). RAI had a poor positive predictive value being 40% and 50% respectively thresholds of 8 and 12.\u003c/p\u003e \u003cp\u003eOur results also indicate that RAI thresholds at the lower end, i.e. 3\u0026ndash;5, are useful for early risk stratification. The findings of our study also correspond with the existing evidence (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). They reported RAI\u0026thinsp;\u0026ge;\u0026thinsp;8 demonstrating a sensitivity of 82.8% and lower thresholds (\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) being 100% sensitive. However, it is noteworthy to highlight here the specificity at lower thresholds being 21.5 and 23.8% respectively in their study. In parallel, our study demonstrated specificities to be 56% and 58%. The high negative predictive value (NPV\u0026thinsp;=\u0026thinsp;97%) at RAI\u0026thinsp;\u0026ge;\u0026thinsp;12 or \u0026ge;\u0026thinsp;20 suggests that these thresholds may be better suited for confirming severe AKI cases, whereas lower thresholds like RAI\u0026thinsp;\u0026ge;\u0026thinsp;3 (100% sensitivity, 21.5% specificity) are better for early screening.\u003c/p\u003e \u003cp\u003eAdditionally, RAI-positive patients had worse outcomes like diuretics use, longer PICU stay, need for RRT, and higher mortality rates. Our findings suggested that RAI detects AKI earlier as compared to conventional screening markers like serum creatinine, which often shows delayed elevation.\u003c/p\u003e \u003cp\u003eGiven that AKI is one of the major morbidities developing in critically ill patients, it is of utmost importance that detection of AKI should be confirmed as early in the clinical course as possible. In resource-limited settings, including leading centers in our country, we lack testing of biomarkers to detect AKI early in the course. Hence, a need for reliable bedside indicators is the need for time. RAI is one such indicator that has not only proven its reliability over the period, as evident by multiple study findings. Its reasonable positive predictive value (PPV) further supports its utility in clinical decision-making. Incorporating RAI as a part of regular assessment will not only help detect RAI earlier but also aid in better management of AKI.\u003c/p\u003e \u003cp\u003eTo our knowledge, no previous studies evaluating RAI in critically ill pediatric patients have been conducted in Pakistan, highlighting the novelty and importance of our findings. Our study does have limitations, as we conducted it in a single center and for a relatively short period. Moreover, we did not calculate and check RAI's reliability on Day 3 as compared to other previous studies. This is because of a lack of resources and data availability that we were unable to do. Nevertheless, we have put forward a foundation for further research to be conducted and a way to address these gaps. In our opinion, a larger, multi-center observational study being conducted over a longer period and having RAI tested on day 3 as well, will provide good quality results.\u003c/p\u003e \u003cp\u003eIn conclusion, our study has given a way forward for upcoming studies to be conducted and laid down the idea of introducing RAI as a regular indicator for predicting acute kidney injury in critically ill patients, specifically the pediatric population.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003e\u003cspan\u003eEthics Approval Statement:\u0026nbsp;\u003c/span\u003e\u003c/h2\u003e\n\u003cp\u003e\u003cspan\u003eThis study was approved by the Ethical Review Committee of Aga Khan University Hospital Pakistan, with the reference number 2021-5913-17386. Data was collected after ethical review committee exemption and all records were kept confidential and without identifiers\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors have made substantial contributions to the conception, design, data acquisition, analysis, and interpretation of the work. All authors were involved in drafting and revising the manuscript critically for important intellectual content. All authors have read and approved the final version of the manuscript and agree to be accountable for all aspects of the work, ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.Author Contributions:MNS: Conceptualization, study design, data collection, data analysis, manuscript writing, critical revision, and final approval.BR: Study design, data collection, manuscript drafting, and critical revision.AA: Data acquisition, statistical analysis support, manuscript editing.YJ: Data analysis, methodology development, manuscript revision.SZ: Data management, technical support, statistical consultation.SI: Literature review, critical revision, and final editing support.All authors agree to be listed as authors and to the order\u0026nbsp;of\u0026nbsp;authorship.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eMehta RL, Burdmann EA, Cerd\u0026aacute; J, Feehally J, Finkelstein F, Garc\u0026iacute;a-Garc\u0026iacute;a G, et al. Recognition and management of acute kidney injury in the International Society of Nephrology 0by25 Global Snapshot: a multinational cross-sectional study. \u003cem\u003eLancet\u003c/em\u003e. 2016 May 14;387(10032):2017-25.\u003c/li\u003e\n \u003cli\u003eSusantitaphong P, Cruz DN, Cerd\u0026aacute; J, Abulfaraj M, Alqahtani F, Koulouridis I, et al. World incidence of AKI: a meta-analysis. \u003cem\u003eClin J Am Soc Nephrol\u003c/em\u003e. 2013 Sep 6;8(9):1482-93.\u003c/li\u003e\n \u003cli\u003eKaur R, Dhooria GS, Pooni PA, Bhat D, Bhargava S, Kakkar S, et al. Utilization of the renal angina index in PICU of a developing country for prediction of subsequent severe acute kidney injury. \u003cem\u003ePediatr Nephrol\u003c/em\u003e. 2018 Nov;33(11):2185-91.\u003c/li\u003e\n \u003cli\u003eMiller TR, Anderson RJ, Linas SL, Henrich WL, Berns AS, Gabow PA, et al. Urinary diagnostic indices in acute renal failure: a prospective study. \u003cem\u003eAnn Intern Med\u003c/em\u003e. 1978 Jul;89(1):47-50.\u003c/li\u003e\n \u003cli\u003eCarvounis CP, Nisar S, Guro-Razuman S. Significance of the fractional excretion of urea in the differential diagnosis of acute renal failure. \u003cem\u003eKidney Int\u003c/em\u003e. 2002 Dec;62(6):2223-9.\u003c/li\u003e\n \u003cli\u003eEsson ML, Schrier RW. Diagnosis and treatment of acute tubular necrosis. \u003cem\u003eAnn Intern Med\u003c/em\u003e. 2002 Nov 5;137(9):744-52.\u003c/li\u003e\n \u003cli\u003eBasu RK, Kaddourah A, Terrell T, Mottes T, Arnold P, Jacobs J, et al. Assessment of worldwide acute kidney injury, renal angina and epidemiology in critically ill children (AWARE): study protocol for a prospective observational study. \u003cem\u003eBMC Nephrol\u003c/em\u003e. 2015 Dec 1;16(1):24.\u003c/li\u003e\n \u003cli\u003eHoste EA, Kellum JA. Incidence, classification, and outcomes of acute kidney injury. In: Ronco C, Bellomo R, Kellum JA, editors. \u003cem\u003eAcute Kidney Injury\u003c/em\u003e. Basel: Karger; 2007. p. 32-8. (Contrib Nephrol; vol. 156).\u003c/li\u003e\n \u003cli\u003eZappitelli M, Bernier PL, Saczkowski RS, Tchervenkov CI, Gottesman R, Dancea A, et al. A small post-operative rise in serum creatinine predicts acute kidney injury in children undergoing cardiac surgery. \u003cem\u003eKidney Int\u003c/em\u003e. 2009 Oct;76(8):885-92.\u003c/li\u003e\n \u003cli\u003eRady HI, Mohamed SA, Mohssen NA, ElBaz M. Application of different scoring systems and their value in pediatric intensive care unit. \u003cem\u003eEgypt Pediatr Assoc Gaz\u003c/em\u003e. 2014 Sep;62(3-4):59-64.\u003c/li\u003e\n \u003cli\u003eMenon S, Goldstein SL, Mottes T, Fei L, Kaddourah A, Terrell T, et al. Urinary biomarker incorporation into the renal angina index early in intensive care unit admission optimizes acute kidney injury prediction in critically ill children: a prospective cohort study. \u003cem\u003eNephrol Dial Transplant\u003c/em\u003e. 2016 Apr 1;31(4):586-94.\u003c/li\u003e\n \u003cli\u003eGoldstein SL, Chawla LS. Renal angina. \u003cem\u003eClin J Am Soc Nephrol\u003c/em\u003e. 2010 May 1;5(5):943-9.\u003c/li\u003e\n \u003cli\u003eLee HA, Seo YS. Current knowledge about biomarkers of acute kidney injury in liver cirrhosis. \u003cem\u003eClin Mol Hepatol\u003c/em\u003e. 2022 Jan;28(1):31-44.\u003c/li\u003e\n \u003cli\u003eSmith LH Jr, Post RS, Teschan PE, Abernathy RS, Davis JH, Gray DM, et al. Post-traumatic renal insufficiency in military casualties: II. Management, use of an artificial kidney, prognosis. \u003cem\u003eAm J Med\u003c/em\u003e. 1955 Feb 1;18(2):187-98.\u003c/li\u003e\n \u003cli\u003eTeschan PE, Post RS, Smith LH Jr, Abernathy RS, Davis JH, Gray DM, et al. Post-traumatic renal insufficiency in military casualties: I. Clinical characteristics. \u003cem\u003eAm J Med\u003c/em\u003e. 1955 Feb 1;18(2):172-86.\u003c/li\u003e\n \u003cli\u003eRicci Z, Ronco C. New insights in acute kidney failure in the critically ill. \u003cem\u003eSwiss Med Wkly\u003c/em\u003e. 2012 Aug 13;142:w13662.\u003c/li\u003e\n \u003cli\u003eGawadia J, Mishra K, Kumar M, Saikia D. Prediction of severe acute kidney injury using renal angina index in a pediatric intensive care unit. \u003cem\u003eIndian Pediatr\u003c/em\u003e. 2019 Aug;56(8):647-52.\u003c/li\u003e\n \u003cli\u003eZulu C, Mwaba C, wa Somwe S. The renal angina index accurately predicts low risk of developing severe acute kidney injury among children admitted to a low-resource pediatric intensive care unit. \u003cem\u003eRen Fail\u003c/em\u003e. 2023 Dec 22;45(2):2252095.\u003c/li\u003e\n \u003cli\u003eSundararaju S, Sinha A, Hari P, Lodha R, Bagga A. Renal angina index in the prediction of acute kidney injury in critically ill children. \u003cem\u003eAsian J Pediatr Nephrol\u003c/em\u003e. 2019 Jan 1;2(1):25-3\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1- Basic demographics and risk factors\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"656\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRA - n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRA + n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e162 (58.27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e82 (63.07%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e80 (54.05%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003eAdmission Reason\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003eCNS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e48 (17.266%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e29 (22.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e19 (12.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003eCVS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e24 (8.633%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e5 (3.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e19 (12.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003eGI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e34 (12.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e13 (10.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e21 (14.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003eRespiratory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e73 (26.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e43 (33.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e30 (20.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003eSepsis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e78 (28.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e27 (20.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e51 (34.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003eTrauma/Surgery (T/S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e21 (7.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e13 (10.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e8 (5.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003eRisk Factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003eInotropes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e104 (37.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e18 (13.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e86 (58.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003eMechanical Ventilation (MV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e124 (44.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e27 (20.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e97 (65.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003eHSCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e4 (1.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e2 (1.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 161px;\"\u003e\n \u003cp\u003e2 (1.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Table 2- Age distribution and outcomes\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"686\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge Group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRA - n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRA + n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChi-Square Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e0-12 months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e73 (26.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e27 (20.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e46 (31.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e2.774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e1-5 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e93 (33.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e51 (39.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e42 (28.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e2.429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e6-10 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e49 (17.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e23 (17.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e26 (17.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.0008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.977\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e11-15 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e46 (16.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e19 (14.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e27 (18.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.459\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e16-18 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e17 (6.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e10 (7.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e7 (4.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e1.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.307\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eO\u003c/strong\u003e\u003cstrong\u003eutcomes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eDiuretics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e65 (23.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e4 (3.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e61 (41.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e43.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026lt;0.00001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eRenal Replacement Therapy (RRT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e16 (5.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e16 (10.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e14.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.00017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003ePICU Length of Stay (days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e5.10 \u0026plusmn; 4.503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e3.71 \u0026plusmn; 2.827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e6.33 \u0026plusmn; 5.288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e41.847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eMortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e37 (13.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 102px;\"\u003e\n \u003cp\u003e2 (1.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e35 (23.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e25.414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026lt;0.00001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Table 3- RAI threshold variance\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"620\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRAI Threshold\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecificity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAUC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRAI \u0026ge; 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e56%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e76%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e100%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRAI \u0026ge; 5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e82%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e58%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e73%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRAI \u0026ge; 6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e82%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e87%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e78%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRAI \u0026ge; 8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e82%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e87%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e78%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRAI \u0026ge; 10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e63%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e88%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e88%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e63%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRAI \u0026ge; 12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e63%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e98%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e98%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e66%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRAI \u0026ge; 20\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e46%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e99%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e99%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e57%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRAI \u0026ge; 40\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e46%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e99%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e99%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 99px;\"\u003e\n \u003cp\u003e57%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bped","sideBox":"Learn more about [BMC Pediatrics](http://bmcpediatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bped/default.aspx","title":"BMC Pediatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6556627/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6556627/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e\u003cbr\u003e\nTo evaluate the diagnostic accuracy of the Renal Angina Index (RAI) compared to Kidney Disease Improving Global Outcomes (KDIGO) criteria for early prediction of severe acute kidney injury (AKI) in critically ill children admitted to a tertiary care hospital in a low-middle-income country.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003cbr\u003e\nThis cross-sectional study included critically ill children aged 1 month to 18 years admitted to the Pediatric Intensive Care Unit (PICU) at Aga Khan University Hospital, Karachi, from January to July 2021. Patients with chronic kidney disease or transferred after 24 hours were excluded. RAI was calculated within 24 hours of PICU admission, and its performance in predicting severe AKI (KDIGO stage 2 or 3) was compared with clinical outcomes such as need for renal replacement therapy (RRT), PICU length of stay, and mortality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003cbr\u003e\nAmong 278 patients, 148 (53.2%) were RAI positive. RAI demonstrated a sensitivity of 82% and specificity of 87% for predicting severe AKI, with an area under the ROC curve (AUC) of 0.90. RAI-positive patients had significantly higher requirements for diuretics (41.2% vs. 3.1%), RRT (10.8% vs. 0%), longer PICU stay (6.33 ± 5.28 vs. 3.71 ± 2.83 days), and higher mortality (23.6% vs. 1.5%) compared to RAI-negative patients. A threshold of RAI ≥8 provided the best balance between sensitivity and specificity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e\u003cbr\u003e\nThe Renal Angina Index is a highly sensitive and specific tool for early prediction of severe AKI and associated poor outcomes in critically ill pediatric patients. Its implementation could significantly enhance AKI detection and management in resource-limited settings, where conventional biomarkers are often inaccessible.\u003c/p\u003e","manuscriptTitle":"Utility of Renal Angina Index as Compared to Kdigo in Critically Ill Children at a Tertiary Care Hospital in a Low Middle-income Country","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 09:17:28","doi":"10.21203/rs.3.rs-6556627/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-19T12:24:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-15T10:31:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-15T10:29:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pediatrics","date":"2025-04-29T12:45:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bped","sideBox":"Learn more about [BMC Pediatrics](http://bmcpediatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bped/default.aspx","title":"BMC Pediatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6881ed0f-4898-41d9-b656-a0ed16b8d5b8","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-12T16:07:48+00:00","versionOfRecord":{"articleIdentity":"rs-6556627","link":"https://doi.org/10.1186/s12887-025-06106-5","journal":{"identity":"bmc-pediatrics","isVorOnly":false,"title":"BMC Pediatrics"},"publishedOn":"2026-01-10 15:58:03","publishedOnDateReadable":"January 10th, 2026"},"versionCreatedAt":"2025-05-07 09:17:28","video":"","vorDoi":"10.1186/s12887-025-06106-5","vorDoiUrl":"https://doi.org/10.1186/s12887-025-06106-5","workflowStages":[]},"version":"v1","identity":"rs-6556627","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6556627","identity":"rs-6556627","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}