Identifying critically ill children at high risk of acute kidney injury and renal replacement therapy.

Acute kidney injury (AKI), a common complication in paediatric intensive care units (PICU), is associated with increased morbidity and mortality. In this single centre, prospective, observational cohort study, neutrophil gelatinase-associated lipocalin in urine (uNGAL) and plasma (pNGAL) and renal angina index (RAI), and combinations of these markers, were assessed for their ability to predict severe (stage 2 or 3) AKI in children and young people admitted to PICU. In PICU children and young people had initial and serial uNGAL and pNGAL measurements, RAI calculation on day 1, and collection of clinical data, including serum creatinine measurements. Primary outcomes were severe AKI and renal replacement therapy (RRT). Secondary outcomes were length of stay, hospital acquired infection and mortality. The area under the Receiver Operating Characteristic (ROC) curves and Youden index was used to determine biomarker performance and identify optimum cut-off values. Of 657 children recruited, 104 met criteria for severe AKI (15∙8%) and 47 (7∙2%) required RRT. Severe AKI was associated with increased length of stay, hospital acquired infection, and mortality. The area under the curve (AUC) for severe AKI prediction for Day 1 uNGAL, Day 1 pNGAL and RAI were 0.75 (95% Confidence Interval [CI] 0∙69, 0∙81), 0∙64 (95% CI 0∙56, 0∙72), and 0.73 (95% CI 0∙65, 0∙80) respectively. The optimal combination of measures was RAI and day 1 uNGAL, giving an AUC of 0∙80 for severe AKI prediction (95% CI 0∙71, 0∙88). In this heterogenous PICU cohort, urine or plasma NGAL in isolation had poorer prediction accuracy for severe AKI than in previously reported homogeneous populations. However, when combined together with RAI, they produced good prediction for severe AKI.

Introduction

Acute kidney injury (AKI) is a serious and common complication in critical care and is identified by abrupt (<48 hours) increase in serum creatinine levels or reduction in urine output resulting from injury, or insult causing a functional or structural change in the kidney [1]. The AWARE study, a multi-centre prospective study in paediatric critical care, demonstrated a 11∙6% incidence of severe AKI and an absolute increase in mortality of 8∙5% for those patients [2]. Severe AKI also confers an increase in morbidity in children through increased length of stay and subsequent chronic kidney disease [3]. Renal replacement therapy (RRT) may be required to maintain electrolyte, acid-base, and fluid balance. Timing of RRT is remains controversial, but studies in children suggest earlier implementation improves outcomes [4]. Accurate biomarkers would help clinicians to make a diagnosis earlier of AKI and thus weigh the risks and benefits of RRT for the individual based on objective evidence.

The diagnosis of AKI using the Kidney Disease: Improving Global Outcomes (KDIGO) is validated in children [5, 6]. However, this relies on urine output and changes in creatinine measurements that occur in the late stages of acute kidney injury, and are markers of established damage as opposed to reversible injury. This impedes early detection and instigation of preventative measures. Moreover, there is a lag time of several hours from injury to rise in creatinine that results in a ‘sub-clinical’ phase of AKI, which may be more amenable to treatment [7]. Lack of progress in reducing mortality in AKI has been attributed to delays in diagnosis [8]. These concerns are supported by evidence from the AWARE study, that serum creatinine measurements alone missed 67∙2% of diagnoses of AKI, compared to urine output. However, in clinical settings, urine output measurements are variably recorded or monitored on wards and are dependent on favourable nursing staff ratios. Two suggested approaches to improve the early detection of AKI are the measurement of novel renal biomarkers, and use of a Renal Angina Index (RAI).

The ideal biomarker is outlined by the Wilson and Junger criteria, that identifies a pertinent condition during an early stage at which time effective, acceptable treatment can be instigated. A more sensitive, rapid, and renal related biomarker would allow further research into preventative strategies for AKI development and allow prompt initiation of personalised therapy to improve outcomes. Neutrophil Gelatinase Associated Lipocalin (NGAL) is a promising plasma and urinary biomarker that rises in the initial phase of AKI, but there is a wide variation of reported diagnostic accuracy in paediatric studies [9, 10]. Under conditions of stress and during proximal tubule insults, renal tubule epithelial cells secrete monomeric NGAL into the urine , whilst activated neutrophils release homodimers of NGAL [11].

The RAI has been developed as a screening tool that is validated in children to detect those at risk of AKI in intensive care units (ICU) and is calculated according to Table 1 [12]. In one study the RAI outperformed renal biomarkers for the prediction of severe AKI, and the combination of RAI with biomarker measurements further improved prediction [13]. This study aimed to evaluate the diagnostic and prognostic value of initial and serial urine (uNGAL), plasma NGAL (pNGAL) concentrations, and the RAI to predict severe (stage 2 or 3) AKI and RRT in a heterogenous cohort of critically ill children.

Table 1

Parameters to calculate renal angina index (adapted from Basu, 2017) [12].

Risk		Injury: Serum Creatinine to baseline		Renal Angina Index
ICU admission	1	No change	1
Stem cell/ solid organ transplant	3	x 1–1∙49	2	Risk x Injury = (1–40)
Mechanical ventilation and/ or inotrope use	5	x 1∙5–1∙99	4	Positive if ≥8
		x ≥2	8

Materials and methods

Study population

A prospective, observational cohort study of 657 children aged from 0–16 years, consecutively admitted to a tertiary paediatric ICU (PICU), were recruited from October 2010 to June 2012 (Fig 1). Exclusion criteria were; preterm infant age <37 weeks corrected gestational age or ≥16 years of age; children admitted moribund and not expected to survive more than 24 hours; children who were non-intubated elective admissions with a predicted duration of stay of less than 24 hours; children not expected to survive at least 28 days because of pre-existing condition; presence of existing directive to withhold life-sustaining treatment; end-stage renal disease requiring chronic dialysis therapy; and end-stage cirrhosis with evidence of portal hypertension. Ethics approval was granted by the local ethics committee (NRES Committee North West—Greater Manchester West, REC reference: 10/H1014/52) and all procedures followed were in accordance with the Helsinki Declaration. Written, informed consent was gained from the primary care giver.

Fig 1

STARD flowchart.

Sample and data collection and analysis

Patient demographic and clinical data were collected prospectively. Plasma urea, creatinine, uNGAL, and pNGAL (pNGAL was only measured on 295 children) were determined daily for the first 7 days of admission (see Fig 1 for STARD flowchart). Renal angina index was calculated retrospectively. Primary outcomes measures were incidence and risk factors for AKI, and RRT. Secondary outcomes were length of stay, hospital acquired infection, and mortality.

Biomarker measurement

Serum creatinine was measured using an enzymatic (Creatininase/Creatinase) method developed by Abbott Diagnostics for use on the Abbott Architect Chemistry Analyser. uNGAL was measured using the ARCHITECT Urine NGAL assay (Abbott Diagnostics, Illinois, U.S.A.) and pNGAL was measured using a commercial ELISA kit (R&D Systems Inc., Minneapolis, USA), both according to the manufacturers’ instructions.

Definitions

Severe (stage 2 or 3) AKI within 72 hours of admission to ICU was defined using the serum creatinine criteria of the KDIGO guidelines [1]: a >2 times increase in the serum creatinine value from admission when compared to the value on day 3 (48–72 hours), or the need for renal replacement therapy. RRT was defined as the need for dialysis (haemodialysis or peritoneal dialysis), or haemofiltration. Baseline serum creatinine concentration was calculated as the lowest value in the preceding 3 months before admission to ICU if available. RAI was calculated retrospectively according to previously published protocols [14], using data obtained upon admission to PICU, and their first serum creatinine measurement on their first day in PICU.

A limiting factor in applying the RAI to this population was the number without baseline creatinine values. Calculation of baseline creatinine values using the Schwartz equation has been previously validated and widely used [2], but relies on height measurements which we did not have. We therefore conducted a secondary analysis using the Pottel method [15] to impute baseline creatinine values for those with absent data, following the method of Roy et al [16].

Statistical analysis

Statistical analysis was performed using IBM SPSS Statistics version 22. Data were initially summarized using standard measures of location (mean, median etc.) and variation, counts and percentages are reported to summarize categorical variables. Chi-square test were used to compare categorical variables, Mann Whitney test for continuous variables. Receiver Operating Characteristic (ROC) curves were calculated by plotting sensitivity against 1-specificity for a range of cut-off values for each biomarker. The area under the curves (AUC) and Youden index was used to determine biomarker performance and identify optimum cut-off values. The closer the area under the ROC is to one the better the screening tool is at discriminating between positive and negative cases. Sensitivity and specificity were calculated for a range of cut-off values for uNGAL and pNGAL. Sensitivity and specificity curves were then plotted on the same graph and the point of crossover is the cut-off value that optimises sensitivity and specificity. This cut-off value was then used to classify patients as being positive (above the cut-off) or negative (equal to or below the cut-off). Multivariate logistic regression and Cox proportional-hazards models were fitted to the outcome renal replacement therapy, using the independent variables where an association was found in the univariate analysis.

Results and discussion

There were 2468 admissions to the PICU between October 2010 and June 2012. Nonetheless, 1339 were not eligible to participate and 472 were eligible but not recruited, thus 657 participants were enrolled in the study (Fig 2). There were 359 males (54∙6%) and the median age at admission was 1∙01 years (IQR, 0∙30, 5∙01) and the median weight on admission was 8∙15kg (IQR, 4∙15, 16∙05). The most common reason for admission was for cardiac surgery (n = 350 53.3%) and suspected infection (n = 84 12∙8%). The median duration of stay in the PICU for all patients was 2∙92 days (IQR, 1∙63, 6∙00) and there were 15 deaths during the study (2∙30%), with 12 being within the first 28 days (1∙80%). Table 2 shows the characteristics of all patients stratified by the occurrence of severe AKI.

Fig 2

CONSORT study inclusion and exclusion flowchart.

Table 2

Clinical and demographic characteristics as stratified by AKI status.

Characteristic	Severe (stage 2 or 3) AKI		P value
	No n = 553 (84∙17%)	Yes n = 104 (15∙83%)
Age in years, median (IQR),	1∙03 (0∙30, 5∙17)	0∙94 (0∙18, 4∙42)	0∙761
Range	0∙00–16∙43	0∙00–15∙91
Gender—male, n (%)	298 (53∙9)	61 (58∙7)	0∙370
Weight on Admission (kg), mean (SD)	13∙63 (±14∙98)	14∙10 (±17∙54)	0∙883
Range	1∙09–103∙00	2∙90–108∙00
Median (IQR)	8∙10 (4∙20, 16∙05)	8∙73 (3∙90, 16∙00)
Reason for Admission—n (%)
Cardiac Surgery	287 (51∙9)	63 (60∙6)
Congenital Heart Disease	13 (2∙4)	7 (6∙7)
SBI	61 (11∙0)	10 (9∙6)
Infection	73 (13∙2)	11 (10∙6)
Post-Op Other	48 (8∙7)	3 (2∙9)
Trauma	23 (4∙2)	1 (1∙0)
Other	48 (8∙7)	9 (8∙7)
Surgery—Yes n (%) (can be more than one)	391 (70∙7)	82 (788)	0∙09
(can be more than one)
Cardiac	300 (76∙7)	68 (82∙9)
Neurosurgery	24 (6∙1)	5 (6∙1)
General	14 (3∙6)	1 (1∙2)
Orthopaedic	7 (1∙8)	2 (2∙4)
Thoracic	12 (3∙1)	1 (1∙2)
Abdominal	35 (9∙0)	6 (14∙6)
Plastic	4 (1∙0)	1 (1∙2)
ENT	7 (1∙8)	1 (1∙2)
Other	9 (2∙3)	2 (2∙4)
Renal Replacement Therapy—Yes n (%)	-	47 (45∙2)
Peritoneal Dialysis	-	36 (76∙6)
Haemofiltration	-	10 (21∙3)
Haemodialysis	-	1 (2∙1)
Duration of Renal Replacement Therapy (days)
Mean (SD); range	-	3∙53 (±3∙20); 0∙21–15∙34
Median (IQR)	-	2∙58 (1∙21, 4∙75)
Duration of PICU stay (days)			<0∙0001
Mean (SD); range	4∙35 (±5∙63); 0∙25–52∙96	8∙78 (±9∙83); 0∙63–61∙50
Median (IQR)	2∙75 (1∙29, 4∙96)	6∙40 (3∙23, 9∙98)
MODS, n (%)			<0∙0001
No	485 (87∙7)	48 (46∙2)
Yes	68 (12∙3)	56 (53∙8)
PICU Mortality, n (%)			<0∙0001
Missing	4 (0.7)	0 (0.0)
Alive	542 (98∙0)	96 (92∙3)
Dead	7 (1∙3)	8 (7∙7)
28 Day mortality, n (%)			0∙358
Alive	542 (98∙4)	98 (97∙0)
Dead	9 (1∙6)	3 (3∙0)
Maximum PELOD			<0∙0001
Mean (SD); range	10∙94 (±5∙49); 0∙00–31∙00	18∙55 (±6∙61); 2∙00–43∙00
Median (IQR)	11∙00 (11∙00, 12∙00)	21∙00 (12∙00, 22∙00)
Hospital Acquired Infection n (%)			<0∙0001
Yes	138 (25∙0)	51 (49∙0)
No	415 (75∙0)	53 (51∙0)
Serious Bacterial Infection n (%)			0∙797
Yes	107 (19∙3)	19 (18∙3)
No	446 (80∙7)	85 (81∙7)
Renal angina index n (%)			<0∙0001
Positive (i.e ≥8)	113 (36∙6)	35 (11∙3)
Negative	156 (50∙5)	5 (1∙6)

Abbreviations: SD = Standard Deviation, IQR = Interquartile Range, MODS = Multiple Organ Dysfunction Syndrome and PELOD = Paediatric Logistic Organ Dysfunction Score.

AKI incidence and outcomes

Of the 657 patients, 104 (15∙83%) met criteria for severe AKI within the first 72 hours of admission. Age, weight, gender, and reason for admission did not differ between patients with severe AKI and those without (Table 2). The secondary outcome measures were length of stay and mortality, which were both different between the two groups. Patients who developed severe AKI had a median length of stay in the PICU over twice as long as those who did not; 6∙4 days (IQR; 3∙23–9∙98) versus, 2∙75 days (IQR; 1∙29–4∙96) (p<0∙0001). Mortality during admission in those who did develop severe AKI was higher than in those who did not develop severe AKI (7∙7% versus 1∙6% (p = <0∙0001)). Furthermore, the incidence of hospital acquired infection was 49% of patients with severe AKI but only 25% of those without (p = <0∙0001). Finally, the median maximum Paediatric Logistic Organ Dysfunction (PELOD) score was higher in those who developed severe AKI, when compared with those who did not (21 (IQR; 12–22) versus 11 (IQR; 11–12) (p<0∙0001).

AKI biomarker evaluation

uNGAL, pNGAL and creatinine were measured daily. Table 3 shows the Day 1 values and Fig 3 demonstrates the longitudinal profiles of the biomarkers between the severe AKI and non-AKI groups. Over the initial 3 days after admission, pNGAL concentrations were higher in the severe AKI population than those who did not develop severe AKI. However, uNGAL and creatinine concentrations remained higher in patients with severe AKI throughout the 7 days included in the study. Longitudinal biomarker values were compared for their diagnostic accuracy using Receiver Operating Characteristic (ROC) curves (Table 4). The maximal AUC achieved was 0∙75 (95% CI 0∙69–0∙81) for day 1 uNGAL with a cut off value of 61∙15 ng/mL. pNGAL values showed poorer discriminative value over all time points.

Table 3

Day 1 median biomarker concentrations categorized by AKI status.

Day 1 values	No severe AKI (553)	Severe AKI (104)	P value
	Median (Interquartile range)
uNGAL (ng/mL)	30∙9 (11∙35, 127∙5)	186∙0 (73∙2, 785∙1)	<0∙0001
pNGAL (ng/mL)	120∙1 (70∙0, 245∙7)	174 (116∙7, 441∙8)	0∙013

Fig 3

Longitudinal profiles of mean biomarker levels over the first 7 days after PICU admission.

Blue bars no severe AKI, green bars severe AKI present.

Table 4

AUC values for biomarkers of severe AKI.

Biomarker	AUC (95% CI)	Cut Point	Sensitivity	Specificity	PPV	NPV
Day 1 uNGAL	0∙75 (0∙69, 0∙81)	61∙15	0∙80	0∙62	0.29	0.94
Day 2 uNGAL	0∙74 (0∙67, 0∙80)	29∙60	0∙73	0∙66	0.38	0.90
Day 3 uNGAL	0∙66 (0∙58, 0∙73)	30∙70	0∙63	0∙62	0.42	0.80
Day 1 pNGAL	0∙64 (0∙56, 0∙72)	114∙18	0∙78	0∙48	0.24	0.91
Day 2 pNGAL	0∙68 (0∙60, 0∙77)	123∙63	0∙78	0∙55	0.31	0.90
Day 3 pNGAL	0∙62 (0∙52, 0∙72)	127∙84	0∙70	0∙54	0.32	0.85
RAI alone	0∙73 (0∙65, 0∙80)	-	0∙88	0∙58	0.24	0.97
RAI	0.80 (0.71, 0.88)	-	0.80	0.79	0.34	0.97
and day 1 uNGAL
RAI	0.73 (0.61, 0.86)	-	0.54	0.93	0.64	0.90
and day 2 uNGAL
RAI	0.69 (0.56, 0.80)	-	0.59	0.80	0.38	0.90
and day 1 pNGAL
RAI	0.70 (0.56, 0.84)	-	0.55	0.84	0.46	0.88
and day 2 pNGAL
RAI	0.70 (0.54, 0.85)	-	0.50	0.89	0.50	0.89
and day 1 uNGAL
and day 1 pNGAL
RAI	0.75 (0.59, 0.92)	-	0.57	0.94	0.73	0.88
and day 2 uNGAL
and day 2 pNGAL
RAI	0.68 (0.59, 0.76)	-	0.97	0.38	0.17	0.99
or day 1 uNGAL
RAI	0.71 (0.63, 0.80)	-	1.00	0.43	0.28	1.00
or day 2 uNGAL
RAI	0.66 (0.56, 0.77)	-	1.00	0.33	0.24	1.00
or day 1 pNGAL
RAI	0.67 (0.56, 0.78)	-	1.00	0.34	0.19	1.00
or day 2 pNGAL
RAI	0.60 (0.48, 0.73)	-	1.00	0.21	0.22	1.00
or day 1 uNGAL
or day 1 pNGAL
RAI	0.61 (0.46, 0.76)	-	1.00	0.22	0.25	1.00
or day 2 uNGAL
or day 2 pNGAL

Cut point calculated using Youden’s index.

Renal angina index

Baseline creatinine measurements from the preceding 3 months were available in 309 patients (47%) and thus RAI was calculated using the formula in Table 1. RAI was defined as positive in 148 cases (i.e. score >7) with sensitivity of 0∙88, specificity 0∙58, positive predictive value 0∙24 and negative predictive value 0∙97 and AUC of the ROC curve of positive RAI for severe AKI was 0∙73 (Table 4). Positive Likelihood Ratio was 2∙08 and Negative Likelihood Ratio was 0∙22.

Combining biomarker profiles (using the calculated cut-off values, via Youden’s index to give binary high/ low values) with RAI, gave a maximal AUC of 0.80 (95% CI 0∙71–0.88) for a combination of RAI and day 1 uNGAL (Table 4). This combination demonstrated increased specificity of 0.79 compared to either measure alone, but the sensitivity of 0.80 was lower than for RAI alone.

Using RAI or a biomarker value above the cut point (a positive result was returned if either test was positive, or both) led to increases in sensitivity and negative predictive value, with reduced specificity and positive predictive value. The maximal AUC achieved was 0.71 (95% CI 0.63–0.80) for a combination of RAI or day 2 uNGAL, with a sensitivity of 1.00, specificity of 0.43, positive predictive value of 0.28 and negative predictive value of 1.00 (Table 4).

Using the Pottel approach [15] to impute missing baseline creatinine values, RAI was positive in 352 of 657 cases with sensitivity of 0∙85, specificity 0∙52, positive predictive value 0∙25 and negative predictive value 0∙85 and AUC of the ROC curve of positive RAI for severe AKI was 0∙80 (95% CI: 0.76–0.84).

AKI following cardiac surgery

350 (53.3%) of the cohort were admitted to PICU following cardiac surgery. Of these 63 (18%) developed severe AKI. A secondary analysis of the predictive value of uNGAL, pNGAL and RAI for severe AKI was completed in this subgroup. Both uNGAL and pNGAL showed poorer diagnostic accuracy in this subgroup, than in the overall population (S1 and S2 Tables). Conversely, RAI demonstrated better diagnostic accuracy with an AUC of the ROC curve of 0.90 (95% CI: 0.86–0.94) (S2 Table).

Renal replacement therapy incidence and outcomes

RRT was required in 47 (45.2%) patients with severe AKI. Median onset was 4 days after admission (IQR 2–8) and median duration of RRT was 2∙58 days (IQR; 1∙21–4∙75). A total of 3 patients (6∙4%) who required RRT died and 12 (25%) developed HAI, compared to 14 (2∙3%) and 83 (13∙6%) respectively, in the population who did not require RRT. The duration of ventilation and ICU stay was greater in the group undergoing renal replacement therapy 7∙7 days (IQR 5∙1–11∙8) and 9∙2 days (IQR 6∙9–20∙8) which compared to those without RRT 2∙8 days (IQR 1∙4–5∙2) and 3∙0 days (IQR 1∙7–6∙3). A model for the prediction of RRT was developed using logistic regression and Cox proportional-hazards models and is presented in the S1 Appendix.

Discussion

In our large, prospective single centre cohort of critically ill children, we demonstrate that severe AKI presents a significant disease burden in morbidity and mortality. The incidence of severe AKI was 15∙8% with an absolute increase in mortality of 6∙1%. Both severe AKI and RRT are associated with increased duration of PICU stay, and increased risk of HAI. Serial biomarker values are higher in children who develop severe AKI compared to those who do not. RAI, uNGAL or pNGAL alone all demonstrated moderate diagnostic accuracy for severe AKI. The combination of RAI with initial uNGAL values provided good prediction for severe AKI, and demonstrates the potential to improve the early identification of children who will develop severe AKI in in a general PICU population These approaches could help identify patients earlier that might benefit from RRT, and could potentially be used to define entry criteria for future clinical trials of pre-emptive RRT. Our study is the first to our knowledge that reports a significant increase in hospital acquired infections in the severe AKI population with a doubling of relative risk (25% to 49%).

Results from our study are comparable to the multicentre AWARE study which reported a 11∙6% AKI incidence and an absolute increase in mortality of 8∙5% (from 11% mortality in patients with AKI compared to 2∙5% in the general cohort) [2]. In our study, severe AKI developed in 18% of children post-cardiac surgery, which is lower than other studies reporting rates between 25–42% [17, 18]. Other studies have shown the development of AKI whilst in PICU confers a 4-fold increase in total length of hospitalization, and in our cohort length of PICU stay was doubled in those with severe AKI [19].

In a recent systematic review and meta-analysis of NGAL evaluation in children by Filho et al, uNGAL demonstrated an AUC of 0∙94 from 13 studies and for pNGAL an AUC of 0∙90 [20]. However, in these studies NGAL measurements occurred 2–6 hours after surgery and predominantly in ICU admissions after cardiopulmonary bypass or contrast- induced nephropathy. In our clinically heterogenous population, day 1 uNGAL demonstrated only fair diagnostic accuracy (AUC = 0∙75). In our post-cardiac surgery subgroup it performed worse, with an AUC of 0.53. Other less selective ICU studies have also found NGAL to be a poor biomarker of AKI and RRT in adult ICUs [21]. There are various possible explanations for this as timing of NGAL sampling is known to be critical in its diagnostic accuracy due to a rapid initial peak, and this may have been missed due to sampling occurring at variable times after ICU admission in our study where the initial peak may have been missed [22]. Secondly, heterogeneity of aetiological factors in renal insults may impact the utility of NGAL as a biomarker for general ICU populations. Thirdly, although the reference gold standard of paediatric AKI diagnosis uses KDIGO criteria including creatinine, it may not be adequately sensitive thus missing a proportion of diagnoses and may introduce diagnostic bias [2, 21].

We found that RAI in this heterogeneous cohort had moderate sensitivity and specificity, but excellent negative predictive value. Our findings are comparable to those in two previous publications which also demonstrated the high negative predictive value of RAI [13, 23]. In these studies, when RAI was combined with urinary NGAL values the AUC was improved to between 0∙85 and 0∙97 [13, 23]. In our study, the optimal combination was day 1 uNGAL and RAI, giving an AUC of 0∙80. This approach improved specificity over either measure alone, and maintained a high negative predictive value.

Combining measures using ‘or’ (returning an overall positive result when any or all of the selected measures were positive) maximized sensitivity and negative predictive value at the expense of AUC, specificity and positive predictive value. For instance, the combination of RAI or day 2 uNGAL had a sensitivity and negative predictive value of 1.00. It may be important to consider such combinations in clinical practice as they would allow early exclusion of patients who will not develop AKI (based on a negative result for all measures) and closer monitoring of those with a positive result, confident that this group will include all patients who will develop AKI.

A total of 7∙2% of children admitted to PICU required RRT. This is consistent with adult studies estimating RRT requirement in 5–10% of ICU patients [24]. A total of 45∙2% of patients who developed severe AKI required RRT, predominantly via peritoneal dialysis. This is similar to an overall 2∙9–13∙4% incidence of RRT in a multicenter PICU study and other reports of 77–82% RRT initiation due to AKI related reasons in PICU [2, 25]. In our study, RRT was associated with increased incidence of adverse outcomes including absolute increases in duration of ventilation and ICU stay, 4∙9 days and 6∙2 days respectively. Studies from low resource settings reported mortality from RRT at 25–28%, which is higher than the 6.4% in our study. However, there is a paucity of general RRT related mortality in PICU [26].

Meta-analysis of RRT timing suggests differential efficacies of early RRT in adults compared to children [4, 27]. The literature suggests a survival benefit of early RRT in children with sepsis, AKI, and fluid overload [28]. This gap in clinical knowledge regarding optimal timing of RRT in PICU and accurate risk stratification, is an important area for further research [1, 29]. Given that there is currently no specific treatment to reverse AKI, it is vital that it is recognized promptly and managed aggressively to prevent further deterioration in renal function. More sensitive and rapid biomarkers, possibly combined with clinical risk scores such as the RAI could potentially be used to identify children who would benefit from early pre-emptive RRT.

The limitations of this study include the fact that this was a single centre study, and without data regarding hourly urine output some diagnoses of AKI may have been missed. Variation in times when blood and urine samples were collected after admission may also mean that peak concentrations were missed. Furthermore, specific indications for modality of RRT were not documented. RAI could not be calculated in 348 patients due to no previous creatinine measurement in the previous 3 months. This may have led to selection bias towards patients undergoing surgery with planned pre-operative bloods.

Conclusions

This study supports the evidence base regarding the disease burden and adverse outcomes associated with severe AKI and RRT in a heterogeneous PICU population. The combination of RAI with initial uNGAL values provides good prediction for severe AKI in a general PICU population. Young age and cardiac surgery are associated with increased risk for severe AKI and RRT. The development of both severe AKI and RRT are associated with increased resource utilisation, namely PICU stay and nosocomial infections. Our RRT prediction model may allow further studies to stratify populations for early pre-emptive RRT timing in PICU.

Day 1 median biomarker concentrations categorized by AKI status in post-cardiac surgery subgroup.

(DOCX)

Click here for additional data file.

AUC values for biomarkers of severe AKI in post-cardiac surgery subgroup.

(DOCX)

Click here for additional data file.

Development of a model for prediction of renal replacement therapy.

(DOCX)

Click here for additional data file.

12 Jun 2020

PONE-D-20-09458

Identifying critically ill children at high risk of acute kidney injury and renal replacement therapy

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: No

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: No

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: No

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: No

Reviewer #2: Yes

Reviewer #3: No

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: McGalliard and colleagues perform a prospective analysis of the ability of uNGAL and pNGAl to predict SCr based KDIGO Stage 2 AKI in critically ill children. They then assessed the ability of the RAI to do the same, and then combined the RAI and NGAL levels to determine if the performance improved. Then then undertook an extensive assessment of multiple factors to predict RRT provision in the cohort. The authors found that combination of the RAI and Day uNGAL provided the best predictive performance. The following issues require attention:

1) The section developing the model for RRT prediction can be shortened signficantly. I find it to be distracting from the overall message, and in fact, is not even mentioned in the abstract. As the authors note, RRT provision is a soft outcome measure, as indications for RRT provision were not provided in the protocol. I would recommend significant shorterning of this part of the manuscript, or removing it altogether.

2) There are two distinct cohorts in this population -- a group of heterogenous PICU patients and post-cardiac surgery patients. The RAI has never been validated in the post-cardiac surgery patients. It would be helpful for the authors to do a sensitivity analysis of RAI and NGAL performance in these two cohorts separately.

3) The authors define AKI as Stage 2 AKI--this should be called severe AKI or Stage 2 AKI throughout.

4) The authors need to state when the RAI was calculated. In the studies cited in there references, it was calcuated at 12 hours of PICU admission.

5) I am confused by the UOP criteria---were they used or not? In the Methods, the authros state they were (although the criteria they cite is Stage 1 AKI by KDIGO, this needs to be reconciled), but in the Discussion they state they don't have data on UOP? If they did use UOP, they need to use the correct KDIGO staging for Stage 2, and in the results, theshould note how manay patients had Stage 2 AKI by SCr, UOP or both.

6) A substantial percentage of patients were excluded because they did not have a reference SCr in the 3 months prior to PICU admission. Numerous pediatric studies, including AWARE have imputed baseline SCr in such cases based on the method of Zappitelli. The authors could also use the Pottel method. In any case, these patients should be assessed.

Minor

1) Data is the plural form of datum.

2) The authors do not need to state a difference is "significant", that is implied by the methods. If the AKI group has a higher value, then just state, "Patients with AKI has a longer duration of PICU stay", for example.

3) Please note the first mortality variable in the AKI comparision table is PICU mortality.

Reviewer #2: A question to the authors: What are the costs for the tests especially if they were point of care tests which could determine if the child was in established AKI and if so to commence renal replacement therapy sooner rater than later.i.e. would it be cost effective done for the right indication?

Reviewer #3: A prospective observational study was conducted to assess the ability of biomarkers, neutrophil gelatin-aseassociated lipocalin in urine (uNGAL) and plasma (pNGAL), and renal angina index (RAI) to predict acute kidney injury (AKI). The AUC of ROC curves were used to identify the best cut-points in biomarkers. The manuscript requires further clarification of the methods and results before drawing conclusions.

Major revision:

1- The manuscript lacks clarity and organization. The methods and results have not been clearly explained.

2- Clearly indicate how RAI and uNGAL/pNGAL values were used to calculate the sensitivity, specificity, PPV and NPV shown in table 4.

3- Further clarify the statistical analysis section by providing details of the receiver operating characteristic curve.

Minor revisions:

1- Abstract: More fully develop the abstract to help clarify the purpose, statistical methods and results of the study.

2- Line 135: Chi-square tests were used to COMPARE categorical variables.

3- Line 138: Clarify the sentence, “Logistic regression and Cox proportional-hazards models were fitted to the outcome renal replacement therapy, using the independent variables where an association was found in the univariate analysis.” Were MULTIVARIATE logistic and Cox proportional-hazard models fitted?

4- Check the line spacing of results in Table 1, specifically for Renal Replacement Therapy.

5- Table 5: Clarify if standard error refers to the standard error of the mean.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Stuart L Goldstein

Reviewer #2: No

Reviewer #3: No

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22 Jul 2020

Please see the 'Response to Reviewers' document for full details.

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: McGalliard and colleagues perform a prospective analysis of the ability of uNGAL and pNGAl to predict SCr based KDIGO Stage 2 AKI in critically ill children. They then assessed the ability of the RAI to do the same, and then combined the RAI and NGAL levels to determine if the performance improved. Then then undertook an extensive assessment of multiple factors to predict RRT provision in the cohort. The authors found that combination of the RAI and Day uNGAL provided the best predictive performance. The following issues require attention:

1) The section developing the model for RRT prediction can be shortened signficantly. I find it to be distracting from the overall message, and in fact, is not even mentioned in the abstract. As the authors note, RRT provision is a soft outcome measure, as indications for RRT provision were not provided in the protocol. I would recommend significant shorterning of this part of the manuscript, or removing it altogether.

The section of the Results describing the development of the RRT prediction model and associated tables and figures has been largely removed and will be provided as supplementary material. A paragraph describing the incidence and outcomes of RRT is retained in the main manuscript.

2) There are two distinct cohorts in this population -- a group of heterogenous PICU patients and post-cardiac surgery patients. The RAI has never been validated in the post-cardiac surgery patients. It would be helpful for the authors to do a sensitivity analysis of RAI and NGAL performance in these two cohorts separately.

Thank you for your comment. We have completed an additional analysis in the sub-population of 360 post-cardiac surgery patients. The results section has been updated to include these results.

3) The authors define AKI as Stage 2 AKI--this should be called severe AKI or Stage 2 AKI throughout.

This has been amended throughout.

4) The authors need to state when the RAI was calculated. In the studies cited in there references, it was calcuated at 12 hours of PICU admission.

The RAI was calculated retrospectively using data obtained upon admission to PICU, and their first serum creatinine measurement on their first day in PICU. The text has been updated to include this detail.

5) I am confused by the UOP criteria---were they used or not? In the Methods, the authros state they were (although the criteria they cite is Stage 1 AKI by KDIGO, this needs to be reconciled), but in the Discussion they state they don't have data on UOP? If they did use UOP, they need to use the correct KDIGO staging for Stage 2, and in the results, theshould note how manay patients had Stage 2 AKI by SCr, UOP or both.

Apologies for the confusion. Urine output data was not collected, and was therefore not used for defining AKI. The text has been amended to clarify this.

6) A substantial percentage of patients were excluded because they did not have a reference SCr in the 3 months prior to PICU admission. Numerous pediatric studies, including AWARE have imputed baseline SCr in such cases based on the method of Zappitelli. The authors could also use the Pottel method. In any case, these patients should be assessed.

We have added a paragraph to the Results section to address this issue. The Pottel method has been applied as height data were not collected.

Minor

1) Data is the plural form of datum.

This has been amended throughout.

2) The authors do not need to state a difference is "significant", that is implied by the methods. If the AKI group has a higher value, then just state, "Patients with AKI has a longer duration of PICU stay", for example.

This has been amended throughout.

3) Please note the first mortality variable in the AKI comparision table is PICU mortality.

This has been amended.

Reviewer #2: A question to the authors: What are the costs for the tests especially if they were point of care tests which could determine if the child was in established AKI and if so to commence renal replacement therapy sooner rater than later.i.e. would it be cost effective done for the right indication?

Thank you for your question. We are not in a position to perform a health economic analysis of this approach at present. Costs for the ARCHITECT NGAL test used are available in a recent NICE publication at https://www.nice.org.uk/guidance/dg39/chapter/3-Evidence

Reviewer #3: A prospective observational study was conducted to assess the ability of biomarkers, neutrophil gelatin-aseassociated lipocalin in urine (uNGAL) and plasma (pNGAL), and renal angina index (RAI) to predict acute kidney injury (AKI). The AUC of ROC curves were used to identify the best cut-points in biomarkers. The manuscript requires further clarification of the methods and results before drawing conclusions.

Major revision:

1- The manuscript lacks clarity and organization. The methods and results have not been clearly explained.

Thank you for your comments. We have taken the opportunity to significantly review the manuscript, taking into account this and your other comments. We hope you will find the clarity and organisation of the manuscript improved.

2- Clearly indicate how RAI and uNGAL/pNGAL values were used to calculate the sensitivity, specificity, PPV and NPV shown in table 4.

Sensitivity and specificity was calculated for a range of cut-off values for uNGAL and pNGAL. Sensitivity and specificity curves were then plotted on the same graph and the point of crossover is the cut-off value that optimises sensitivity and specificity. This cut-off value was then used to classify patients as being positive above the cut-off or negative equal to or below the cut-off. The cut-offs, sensitivity etc. are reported in table 4.

3- Further clarify the statistical analysis section by providing details of the receiver operating characteristic curve.

The ROC curve is calculated by plotting sensitivity against 1-specificity for a range of cut-off values. The closer the area under the ROC is to one the better the screening tool is at discriminating between positive and negative cases.

Minor revisions:

1- Abstract: More fully develop the abstract to help clarify the purpose, statistical methods and results of the study.

The abstract has been reviewed in order to address these aspects.

2- Line 135: Chi-square tests were used to COMPARE categorical variables.

This has been amended.

3- Line 138: Clarify the sentence, “Logistic regression and Cox proportional-hazards models were fitted to the outcome renal replacement therapy, using the independent variables where an association was found in the univariate analysis.” Were MULTIVARIATE logistic and Cox proportional-hazard models fitted?

Multivariate logistic regression and Cox proportional-hazard models were fitted.

4- Check the line spacing of results in Table 1, specifically for Renal Replacement Therapy.

This has been amended.

5- Table 5: Clarify if standard error refers to the standard error of the mean.

The standard errors do refer to the standard errors of the mean. The relevant sections (now moved to supplementary) have been updated to clarify this.

Submitted filename: McGalliard PLOS One response to reviewers comments Final.docx

Click here for additional data file.

25 Sep 2020

Identifying critically ill children at high risk of acute kidney injury and renal replacement therapy

PONE-D-20-09458R1

Dear Dr. McWilliam,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Brenda M. Morrow, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed

Reviewer #3: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: (No Response)

Reviewer #2: Yes

Reviewer #3: (No Response)

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: (No Response)

Reviewer #2: I Don't Know

Reviewer #3: (No Response)

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: (No Response)

Reviewer #2: Yes

Reviewer #3: (No Response)

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: (No Response)

Reviewer #2: Yes

Reviewer #3: (No Response)

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: (No Response)

Reviewer #2: The revision reads much better. More logically set out. All the tables and flow charts are more easily interpreted. The combination of UGal and RAI having a 80% accuracy is better than no criteria but further research to tease out the variables in different causes of AKI and how they are assessed and compared is needed for prediction of AKI and the timing of commencement of renal replacement therapy e.g. post surgical cases compared to general cases.

Reviewer #3: (No Response)

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

1 Oct 2020

PONE-D-20-09458R1

Identifying critically ill children at high risk of acute kidney injury and renal replacement therapy

Dear Dr. McWilliam:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

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on behalf of

Professor Brenda M. Morrow

Academic Editor

PLOS ONE