Utilization output (UOP)-based stratifications of AKI, is

Utilization of Renal
Angina Index for prediction of subsequent severe acute kidney injury in PICU
of  a developing country


Raina Kaur, Gurdeep Singh Dhooria, Puneet A.
Pooni, Deepak Bhat, Sidharth Bhargava, Shruti Kakkar, 

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Department of
Pediatrics, Dayanand Medical College and Hospital, Ludhiana, Punjab, India


Dr Gurdeep Singh

Department of
Pediatrics, Dayanand Medical College and Hospital, Ludhiana, Punjab, India

[email protected]



Acute kidney injury (AKI) is independently associated with worsened
morbidity and increased mortality in PICU. AKI risk stratification, termed
renal angina index (RAI) has been used in the west  to predict persistent severe AKI. Very few
studies have been done on application of renal angina index in PICU of a
developing country. Aim: To predict
severe subsequent AKI in children admitted in PICU using Renal Angina Index. Design: Prospective observational
study.  Methods: Children admitted
over one year in PICU between one month to 18 years of age with no previous
kidney disease were included. RAI assessment was done from 8-12 hours of
admission to PICU. RAI was calculated from product of Renal Risk  and Renal Injury score. Renal angina
positivity was defined as RAI  ? 8. On
day 4, serum creatinine is noted and GFR is calculated. RAI was correlated with
presence/absence of subsequent severe AKI. RAI positivity was also correlated
with duration of PICU stay, need for dialysis, mechanical ventilation and mortality.
Results: RAI positivity was seen in
16.7 % cases. Of the RAI positive cases, 36.2 % cases developed AKI at 4 days
compared to 2.3% in the RAI negative cases (p value <0.001). Mean duration of PICU stay in RAI positive group was 7.19 ± 5.13 days vs 4.72 ± 2.71 days in RAI negative group.  31% cases in RAI positive cases had poor outcome. Conclusions: Renal angina index could be used as a simple and important bedside tool to predict patients at risk of severe AKI. Keywords: Acute kidney injury, children, renal angina index.       Introduction: Acute kidney injury (AKI) is a significant problem in critical illness. Approximately 5–6% of all hospitalized adults and 10% of children suffer from varying degrees of AKI 1. The presence of AKI in critical illness occurs at a rate of 10–15% and carries a 50% mortality rate in children requiring dialysis 2,3,4. Increasing AKI severity, characterized by serum creatinine (SCr)- and urine output (UOP)-based stratifications of AKI, is associated with increased mortality in adults5 and children.6 Notably, small increases in SCr (0.3 mg/dl) may reflect significant kidney damage and is associated with poor patient outcomes 7,8. Consistently effective AKI therapy to prevent or limit the disease intensity is lacking, potentially due to delayed recognition of existing and/or ongoing injury. AKI diagnosis is traditionally dependent on changes in serum creatinine (SCr), a marker with limitations involving time, body habitus, sex, age, steady-state measurement, and patient condition. Primarily due to the lag in the rise of SCr, the diagnosis of AKI is often delayed, which creates a significant barrier to effective early intervention. 9 Treatment for acute myocardial infarction (MI) was transformed by the use of troponin I measurements in patients with signs and symptoms of a cardiac angina. Sensitivity and specificity of troponin elevations and electrocardiographic changes for MI have allowed practitioners to institute early and life-saving therapy. However, whereas the novel AKI biomarkers recently discovered may serve well as a renal troponin equivalent, AKI lacks an important parallel to MI. Simply put, AKI does not hurt.  In order to optimize the utility of AKI biomarkers, screening systems are needed to identify patients who are at high risk of developing AKI.9 Goldstein SL recently proposed the empiric clinical model of renal angina to identify which critically ill patients would be at the greatest risk of AKI. Using patient demographic factors and early signs of injury, renal angina is aimed to delineate patients at risk for subsequent severe AKI (AKI beyond the period of functional injury) versus those at low risk. 10 In this study, the concept of renal angina to improve prediction of subsequent severe AKI has been validated in Indian children admitted in PICU. Material And Methods: Subjects: All children between 1 months-18 years age group admitted in PICU Duration: one year. Setting: Pediatric Intensive Care Unit (PICU) of a tertiary care hospital in Ludhiana District, Punjab. Design: Prospective observational study Inclusion Criteria: All critically ill children between 1 months to 18 years of age admitted to PICU were included in the study. Exclusion criteria: Children with previously known kidney disease and children with hospital stay less than 72 hours were be excluded. The study was approved by the Institutional Ethics committee. Written consent was obtained from the attendants of the children. Data Collection: Day 0 was considered first day of PICU admission. Day 3 consisted of the time period between 72 and 96 hours after PICU admission. Baseline data at admission included demographic information including age, sex, primary diagnosis, system involved, Pediatric Risk of Mortality (PRISM-II) scores 11 within 24 hours of hospital admission, renal dysfunction using KDIGO (Kidney Disease Improving Global Outcomes) staging 12, and baseline serum creatinine measurement (SCr). Calculation of Renal angina index (RAI): The index calculation for the fulfilment of renal angina is assessed 8-12 h after a patient is admitted to an intensive care unit and used for prediction of severe acute kidney injury 72 hours (3 days) later. Risk factors are determined as shown and assigned a point value (1, 3, and 5, where 1 denotes the lowest risk and 5 denotes the highest risk). Mechanical ventilation and vasoactive support should be used within the 12-h timepoint but are not required to be simultaneous for a patient to be scored 5 points. Injury strata was assigned as depicted to a patient as appropriate.  Percentage fluid overload (FO %) was calculated by {Fluid in(ml)-fluid out (ml) ÷ Patient Weight (gm)} x 100). GFR was based on estimated creatinine clearance (eCrCl) calculated by the Schwartz equation 13, for determination of the RAI.  Calculation of Renal Angina Index (Range 1 to 40) is a multiplication of the risk and injury scores assigned (Risk score x FO% score OR Risk score x GFR score), whichever is worse of the two is chosen. The index RAI ?8 was considered fulfillment of renal angina 14. Fulfillment or the absence of renal angina was denoted 'RA positive' or 'RA negative'.     Outcomes: The primary outcome was the presence of severe AKI on Day3 (Day3-AKI). Severe AKI was defined by the Kidney Disease Improving Global Outcomes (KDIGO) AKI classification stage ?2: serum creatinine of 200% baseline (a decrease in eCrCl of ?50% from baseline) (26). Day3 was chosen since most PICU patients develop AKI within this timeframe and is a clinically relevant time frame for AKI management. Secondary outcomes included use of renal replacement therapy (RRT), need and duration of mechanical ventilation, length of PICU stay (LOS), and incidence of mortality. Statistical Analyses All statistical analyses were performed using STATA version 12 (StataCorp, College Station, TX), SAS version 9.3 (SAS Institute, Cary, NC), and R version 2.14.1 (R Development Core Team, Vienna, Austria). An a priori study sample size of 5250 patients was expected. The continuous data were summarized using descriptive statistics (mean ± standard deviation). Statistical differences between the mean values were compared using Student's t-test. A difference between the two values was considered to be significant if the P < 0.05. Categorical variables were summarized using frequency and proportion and compared by chi-squared or Fisher's exact tests. An RAI cut-off of ?8 was used to define renal angina fulfillment ANG(+) and this cut-off was used for operative characteristics (20). Simple and multivariable logistic models were used to predict day 3 AKI using RAI. Area under the curve (AUC) values were calculated for the prediction model (RAI) and compared using DeLong's method (27). In all analyses, a P value <0.05 was considered statistically significant. Results:  Of the total ____________ patients admitted during the one year period, 413 (    %) patients were enrolled for study. Number______ could not enrolled for different reasons CKD (n=   ), age less than 2 months (n=______), hospital stay less than 72 hours(n=_______). Approximately one third of patients i.e. 69/413(16.7 %) were RA + on day day 0 of PICU admission. Mean age in RA +ve group was 5.92 ± 5.30 years as compared to RA -ve group 5.88 ± 5.32 years. Age, gender, admitting diagnosis or primary system did not affect RA positivity. Sepsis was the diagnosis in 21 % cases and did not affect RA status. Patients in RA +ve group had higher mean PRISM-II scores (18.62 ± 6.49) compared to RA -ve group (12.74 ± 6.49) (p value < 0.001). Additionally, patients in RA+ group had longer duration of mechanical ventilation (mean 4.94 ± 4.10 days vs mean 1.08 ± 2.68 days) (p value < 0.001), PICU stay (mean  7.19 ± 5.13 days vs mean  4.72 ± 2.71) (p value < 0.001), need for dialysis ( 23.2 % vs 0.6% %, p value < 0.001)  and higher mortality( 31.09 % vs 2 % , p value < 0.001). Prediction of subsequent, severe AKI by Renal Angina Index on admission.  Of the total 413 patients enrolled, 33 patients developed Day3-AKI (8%).  The incidence of Day 3 AKI was significantly higher in patients with RAI ?8 (RA + group) 25 of 69 (36.2 %) versus 8 of 344 (2.3 %); P= <0.001. Day 0 RAI positivity (RAI > 8) predicted Day-3 AKI with an
AUC of 0.883 95% confidence interval (CI) = 0.823-0.943. RAI > 8
positivity had a
high negative predictive value (NPV)
of 97.67% % (95% CI = 95.84-98.7
%), with sensitivity and specificity of  75% and 88.42 % , respectively, and positive
predictive value (PPV) of 35.29% (95% CI = 27.92-43.44 %).

prediction by GFR criteria and Fluid overload (FO %) criteria

predictive value of  RAI was broken down by composite factors
of kidney injury. The predictive value for Day-3 AKI by GFR score alone by AUC values was consistently
superior when compared to
fluid overload score (FO %) AUC 0.877 (95% CI = 0.817-0.936) vs
0.774 (95% CI = 0.685- 0.864).
The AUC for
RAI for Day-3 AKI improved when
RAI incorporated worse of the two scores (GFR score/FO score). (AUC
0.883(95% CI= 0.823-0.943). 

versus KDIGO stage and PRISM score

Prediction of RA for Day-3 AKI was superior to KDIGO
stage 1 injury at admission;
fulfillment of renal angina demonstrated higher sensitivity (27.27%), PPV of 25%, NPV of 93.63%
and a higher
Youden’s index ( ____________) than KDIGO stage 1,
although specificity was found to be higher with KDIGO stage 1 (92.89%). Similar results were seen when RA was compared to KDIGO stages 2–3 (Youden’s index=_____). When compared directly, RAI outperformed PRISM-II for the
prediction of day 3 AKI. (AUC=0.764)
(95% confidence interval (CI) = 0.672-0.856).

Discussion:  Renal angina index was developed by Goldstein to identify critically
ill patients at greatest risk of AKI.10  In the current study, we operationalize renal
angina index in a tertiary care hospital of a developing country and show that
renal angina index improves prediction of subsequent severe AKI and also
outperforms currently used clinical thresholds for early signs of kidney
injury, or severity of illness scores.

RAI was derived as a composite of risk factors and clinical
signs of AKI. The logic behind the equation dictates that as a patient achieves
higher risk they require less “clinical sign of AKI” early on to fulfill renal
angina. Similarly, if a patient has less risk but shows more overt signs of clinical
AKI signs, renal angina would also be fulfilled.15 RAI derivation was based on available AKI epidemiology
reported in select pediatric populations: children admitted to the ICU carry
increased risk over the general population (4.5–10%),16,17 children receiving bone marrow
transplantation have ~3× risk (11–21%)18, and those who are intubated and on vasopressor support
carry nearly 5× risk versus the general ICU population (51%). 3 The ‘signs of injury’ (i.e.,
kidney pain) in the RAI include GFR and fluid overload.

Troponin measured in patients
who exhibit cardiac angina, a combination of clinical signs and known coronary
disease risk factors, allows practitioners to rule in myocardial infarction. In
this select, risk-stratified population, troponin has great specificity and
PPV. When measured in patients without cardiac angina, troponin loses
performance. Unfortunately, unlike a heart attack, AKI does not carry an easily
identifiable physical prodrome such as cardiac angina. Simply put, a kidney
attack does not ‘hurt’. So clinicians tried to find a novel renal equivalent of
“cardiac angina” so that a suitable biomarker can be applied to select patients
having high risk of AKI.

Renal angina fulfillment identifies children at the highest
risk of suffering subsequent severe AKI. For a clinician, the ability to
predict the presence of severe AKI 3 days in advance carries obvious benefit.

Fluids are the second most
common intervention in acutely ill patients (after oxygen). The benefits of
early fluid resuscitation in patients with shock and acute kidney injury (AKI)
are already accepted. There is evidence that fluid administration beyond the
correction of hypovolaemia is associated with increased morbidity, a longer
hospital stay and mortality. In a recent article in Critical Care, Wang et al.
analysed the data of 2526 patients admitted to 30 intensive care units (ICUs)
in China and showed that even relatively small degrees of fluid overload were
independently associated with an increased risk of AKI and mortality 19.

In the Rajit
Basu etal study,
based on the most optimal Youden’s index (0.49) and highest negative predictive
value (to safely rule out development of subsequent AKI), an RAI > 8 was
taken as cutoff to label Renal Angina positivity.15 Only day 3 AKI was chosen to define outcome as most PICU
patients develop AKI within this time frame and it surpasses the time frame of
functional AKI (prerenal AKI). Also, time frame of 8 h was kept to assess fluid
overload as it was beyond the generally accepted window of ‘early goal-directed
therapy’ (EGDT) of resuscitation. 20

    In our study a
total of 413 patients were included. Day 0 Renal Angina positive was seen in
16.7% patients. Of  renal angina positive
patients 36.2 %  developed subsequent
severe AKI compared to 2.3 % of the other group, which was highly significant
(p<0.001). Performance of the test was also calculated. Sensitivity came to be 75%. Specificity came to be 88.42%. Positive predictive value was low (35.29%) whereas a high negative predictive value of 97.67% was present. AUC for the same came to be 0.877. When the RAI was derived based on either GFR score or Fluid overload alone, it was still able to predict Day-3 AKI. Although FO score did not perform as well as GFR score, FO score was similar to PRISM-II scores for prediction of Day-3 AKI. More importantly, the prediction for Day-3 AKI improved when the worse value of FO or GFR score was used compared to using  GFR score alone. Also, Renal angina outperformed early signs of injury i.e. KDIGO stages I. Our results were similar to those seen in the Rajit Basu et al. study.15 Several AKI biomarkers have demonstrated promising results for the identification and prediction of AKI in children.21 Identifying patients at risk for severe and long-lasting AKI in the PICU is crucial as risk stratification could allow more judicious AKI biomarker assessment to drive therapeutic intervention, thereby increasing their predictive performance and cost-effectiveness. 22 Limitation of the study was that baseline creatinine was calculated from admission serum creatinine and patient height using the Schwartz correction. This was done, as most patients did not have their lowest creatinine value (up to 3 months before PICU admission) to establish a reference value. In our study, to define severe subsequent AKI, estimated creatinine clearance criteria of KDIGO (SCr of 200% baseline or  decrease in eCCl of ?50% from baseline) was take to define primary outcome and urine output criteria was not taken.  Thus, renal angina index could be used as a simple and important bedside tool without the need of any expensive equipment to detect patients at risk of severe AKI. This can allow us to use novel AKI biomarker or therapy trial, which could ultimately guide treatment strategy in critically ill children.                         Financial support and sponsorship: Nil. Conflicts of interest: There are no conflicts of interest.   References:   1. Uchino S, Kellum JA, Bellomo R, Doig GS, Morimatsu H, Morgera S, Schetz M, Tan I, Bouman C, Macedo E, Gibney N, Tolwani A, Ronco C, Beginning and Ending Supportive Therapy for the Kidney (BEST Kidney) Investigators. Acute renal failure in critically ill patients: a multinational, multicenter study. JAMA. 2005 Aug 17; 294(7):813-8. 2.  Hui-Stickle S, Brewer ED, Goldstein SL. Pediatric ARF epidemiology at a tertiary care center from 1999 to 2001. Am J Kidney Dis. 2005;45:96–101. 3. Akcan-Arikan A, Zappitelli M, Loftis LL, Washburn KK, Jefferson LS, Goldstein SL. Modified RIFLE criteria in critically ill children with acute kidney injury. Kidney Int. 2007;71:1028–1035. 4. Symons JM, Chua AN, Somers MJ, Baum MA, Bunchman TE, Benfield MR, et al. Demographic characteristics of pediatric continuous renal replacement therapy: a report of the prospective pediatric continuous renal replacement therapy registry. Clin J Am Soc Nephrol. 2007;2:732–738. 5. Srisawat N, Hoste EE, Kellum JA. Modern classification of acute kidney injury. Blood Purif. 2010;29:300–307. 6. Slater MB, Anand V, Uleryk EM, et al. A systematic review of RIFLE criteria in children, and its application and association with measures of mortality and morbidity. Kidney Int. 2012;81:791–798. 7. Hoste EA, Kellum JA. Incidence, classification, and outcomes of acute kidney injury. Contrib Nephrol. 2007;156:32–38. 8. Zappitelli M, Bernier PL, Saczkowski RS, Tchervenkov CI, Gottesman R, Dancea A, Hyder A, Alkandari O. A small post-operative rise in serum creatinine predicts acute kidney injury in children undergoing cardiac surgery. Kidney Int. 2009;76:885–892. 9. Basu RK, Chawla LS, Wheeler DS, Goldstein SL. Renal angina: an emerging paradigm to identify children at risk for acute kidney injury. Pediatric Nephrology 2012;27:1067-1078. 10.  Goldstein SL, Chawla LS. Renal angina. Clin J Am Soc Nephrol. 2010;5:943-9. 11.  Pollack MM, Ruttimann UE, Getson PR: Pediatric risk of mortality (PRISM) score. Crit Care Med 16: 1110–1116, 1988 12.       Kellum JA,  Lameire N,  Aspelin P, et al. KDIGO Clinical Practice Guideline for Acute Kidney Injury 2012, Kidney Int Suppl , 2012, vol. 2 (pg. 1-138) 13.       Schwartz GJ, Work DF: Measurement and estimation of GFR in children and adolescents. Clin J Am Soc Nephrol 2009;4: 1832–1843. 14.       Goldstein SL, Currier H, Graf C et al. Outcome in children receiving continuous venovenous hemofiltration. Pediatrics 2001; 107: 1309–1312. 15.       Basu RK , Zappitelli M, Brunner L et al.  . Derivation and validation of the renal angina index to improve the prediction of acute kidney injury in critically ill children. Kidney Int 2014; 85: 659–667. 16. Schneider J, Khemani R, Grushkin C, Bart R. Serum creatinine 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. Crit Care Med. 2010;38(3):933-9. 17. Bailey D, Phan V, Litalien C, Ducruet T, Mérouani A, Lacroix J, Gauvin F. Risk factors of acute renal failure in critically ill children: A prospective descriptive epidemiological study. Pediatr Crit Care Med. 2007 Jan; 8(1):29-35. 18. Michael M, Kuehnle I, Goldstein SL. 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