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Research Article
Assessment of NGAL, eGFR, and tubular injury markers for early detection of chemotherapy-induced nephrotoxicity in pediatric oncology patients
expand article infoPetya Markova§, Antoniya Yaneva|, Mariya Spasova§, Stoyan Markov#, Kostadin Kostadinov¤
‡ Depatrment of Pediatrics, Medical University of Plovdiv, Plovdiv, Bulgaria
§ Department of Pediatrics, St. George University Hospital, Plovdiv, Bulgaria
| Medical Informatics, Biostatistics and eLearning, Faculty of Public Health, Medical University of Plovdiv, Plovdiv, Bulgaria
¶ Department of Otorhinolaryngology, Faculty of Medicine, Medical University of Plovdiv, Plovdiv, Bulgaria
# Department of Otorhinolaryngology, St. George University Hospital, Plovdiv, Bulgaria
¤ Department of Social Medicine and Public Health, Faculty of Public Health, Medical University of Plovdiv, Plovdiv, Bulgaria
Open Access

Abstract

Introduction: Acute kidney injury is a common complication in children with oncological diseases. Its etiology is multifactorial, with key risk factors including drug-induced nephrotoxicity, tumor lysis syndrome, and infection—including sepsis during periods of aplasia. The classical diagnostic criteria for AKI rely on monitoring serum creatinine levels and detecting oliguria. In the context of drug-induced nephrotoxicity, the predominant pathophysiological mechanism is tubular injury, which is typically non-oliguric and may not cause changes in serum creatinine, even in cases of severe damage.

Materials and methods: We studied 40 children undergoing 116 chemotherapy cycles with nephrotoxic agents at the Pediatric Hematology-Oncology Department, St. George University Hospital. Urinary NGAL, estimated GFR (eGFR) using the Schwartz, Schwartz-adjusted, and Brandt formulas, and tubular markers—fractional excretion of phosphate (FeP) and tubular maximum reabsorption of phosphate per GFR (Tmp/GFR)—were measured before and 12 hours after each cycle.

Results: Urinary NGAL increased significantly after later chemotherapy cycles but remained within reference ranges and failed to detect two KDIGO-defined AKI episodes. The classical Schwartz formula identified only two AKI cases, while adjusted formulas revealed decreased eGFR in 36% of patients and a reduction in hyperfiltration prevalence from 50% to 28%. Tubular markers (FeP, Tmp/GFR) showed consistent and significant changes across all cycles.

Conclusion: NGAL is not a reliable early biomarker for chemotherapy-related AKI in children. Adjusted eGFR formulas and tubular injury markers provide greater sensitivity and should be incorporated into monitoring protocols to enable earlier detection and reduce the risk of chronic kidney damage.

Keywords

acute kidney injury, nephrotoxicity, NGAL, tubular injury markers

Introduction

Acute kidney injury (AKI) is a common issue in children with oncological diseases. The reported incident in literature ranges from 16.9% to 52%.[1,2] Its etiology is multifactorial, with key risk factors including drug-induced nephrotoxicity, tumor lysis syndrome, and infection, including sepsis emerging during periods of aplasia.[3] The occurrence of AKI during treatment often necessitates dose reduction of the implicated medication or its replacement with a less nephrotoxic alternative. This may have an adverse effect on the overall therapeutic outcome.

The classical diagnosis of AKI relies on monitoring serum creatinine levels and the presence of oliguria. However, these are late markers, changing more than 48 hours after the onset of kidney injury. Furthermore, they are unable to differentiate between prerenal and renal injuries. Serum creatinine levels rise only after more than 50% of nephron function has been lost. This means that mild and moderate impairments in kidney function may go undetected.

According to the KDIGO criteria, the diagnosis of acute kidney injury (AKI) is based on changes in serum creatinine levels and/or estimated glomerular filtration rate (eGFR), as well as the presence of oliguria. In pediatric practice, the Schwartz formula is the most widely applied method for estimating GFR, relying on serum creatinine concentration and patient height. Nevertheless, serum creatinine is strongly influenced by muscle mass, and in the context of cachexia, frequently observed in children with malignancies, its levels may remain deceptively low even in the presence of AKI. As a result, the use of the classical Schwartz formula, a simple and widely accessible method, tends to overestimate glomerular filtration. The literature provides only limited evidence regarding the optimal formula to apply in pediatric oncology patients; however, the available studies consistently emphasize formulas that incorporate body mass. These include the body surface area–adjusted Schwartz formula, as well as the Brandt and the Cockcroft–Gault formulas.

In the context of drug-induced nephrotoxicity, the primary pathogenetic mechanism of AKI is tubular injury, which is typically non-oliguric, and even in cases of severe kidney damage, serum creatinine levels may remain unchanged. This highlights the need for new markers for the early diagnosis of AKI in pediatric oncology. One of them is NGAL, which has been extensively discussed in the literature as a biomarker of acute kidney injury (AKI), with proven sensitivity and specificity in various conditions such as AKI following perinatal asphyxia or after cardiac surgery with cardiopulmonary bypass, among others. Its role in pediatric oncology, and particularly as a marker of nephrotoxicity, remains incompletely elucidated. Other potential nephrotoxicity biomarkers include tubular markers of injury, such as the fractional excretion of phosphate (FeP%), or the renal phosphate threshold.

Aim

To compare classical markers of AKI with tubular injury markers and NGAL as early markers of drug-induced nephrotoxicity in children undergoing chemotherapy.

Materials and methods

A cross-sectional study was conducted among 40 children undergoing chemotherapy with nephrotoxic medications at the Pediatric Hematology and Oncology Department of the Pediatrics Clinic at St. George University Hospital for the period 2021–2024.

Inclusion criteria: Children aged 0 to 18 years undergoing treatment with nephrotoxic medication (ifosfamide, cisplatin, or carboplatin), with no history of prior kidney injury or renal anomalies, and with signed informed consent.

Exclusion criteria: Presence of a comorbid condition—evidence of sepsis, or treatment with another nephrotoxic agent such as an antibiotic, antiviral, or antifungal medication, dehydration.

For all children, blood and urine samples were collected before the start of each chemotherapy cycle, as well as 12 hours after its completion. The following parameters were analyzed:

In blood, serum creatinine, urea, and electrolytes—including phosphorus and magnesium—were measured using an AU480 automated biochemical analyzer (Beckman Coulter) based on the turbidimetric principle. Estimated glomerular filtration rate (eGFR) was calculated using the original Schwartz formula, the body surface area–adjusted Schwartz formula (Equ. 1), and the Brandt equation (Equ. 2).

GFRESTmL/min=kHt(cm)SCR×BSA(m2)1.73m2 (Equ. 1)

where k=0.45 for children ≤1 year, 0.70 for pubertal males, and 0.55 for all other children, Scr is serum creatinine level (mg/dL), BSA: body surface area, Ht : the child’s height in cm.

GFRmL/min=k( agemos +6)×wtSCR (Equ. 2)

where k is 1.05 for males and 0.95 for females; agemos is the child’s age in months; wt is the child’s weight in kilograms; Scr is serum creatinine levels (mg/dL).

The KDIGO 2022 classification was used to stage acute kidney injury.[4] According to the KDIGO criteria, AKI is defined as an increase in serum creatinine levels or a decline in GFR of 25% for stage 1, 50% for stage 2, and 75% or an eGFR less than 35 mL/min/1.73 m2 for stage 3 (Table 1).

Table 1.

KDIGO classification of acute kidney injury (AKI)

Stage Serum creatinine Urine output
1 1.5–1.9 times above baseline or >26.5 µmol/L within 48 hours <0.5 mL/kg/h for 6-12 hours
2 1.0–2.9 times increase from baseline <0.5 mL/kg/h for >12 hours
3 3.0 times above baseline, or serum creatinine >353.6 µmol/L, or initiation of renal replacement therapy, or eGFR <35 mL/min/1.73 m2 (in patients <18 years of age). <0.3 mL/kg/h for >24 hours or anuria for >12 hours

In urine, neutrophil gelatinase-associated lipocalin (NGAL) was assessed for each chemotherapy cycle. Urine NGAL levels were measured 12 hours before and after treatment using a chemiluminescent microparticle immunoassay for quantitative determination of NGAL. NGAL was considered elevated if its value exceeded 150 ng/mL, the reference value provided by the manufacturer. The NGAL/creatinine ratio was calculated to account for the diluting effect of hyperhydration administered during each chemotherapy cycle. In addition, the fractional excretion of phosphate (FeP%) was measured (Equ. 3), and the renal phosphate threshold was calculated (Equ. 4).

FeP%=PO4(U)×Cr(S)PO4(S)×Cr(U)×100 (Equ. 3)

where PO4: phosphate (mmol/L), Cr: creatinine (µmol/L), U: urine concentration, S: serum concentration.

TmpGFR=Pp(Up×PcrUcr) (Equ. 4)

where P is the phosphate level, Pp is plasma phosphate concentration, Up is Urine phosphate concentration, Pcr is plasma creatinine concentration, and Ucr is the urine creatinine.

Under conditions of normal serum phosphate levels, the fractional excretion of phosphate (FeP) is normally up to 20%. In the presence of hypophosphatemia with proximal tubular dysfunction, FeP increases above 20%, whereas in intact tubular function it is expected to decrease, even approaching 0%.

Statistical analysis

The following statistical methods were used for data analysis:

- Descriptive and inferential statistics

Data for qualitative, non-metric variables are presented using absolute frequencies and relative proportions (count, %). Quantitative variables are described using key parameters of central tendency (mean ± standard error of the mean, SEM), statistical dispersion (standard deviation, SD), and the 95% confidence interval (95% CI). Quantitative indicators that do not follow a normal distribution are described using the median and interquartile range (IQR).

- To assess the normality of the distribution of the analyzed variables, the Kolmogorov–Smirnov test was used.

- When testing hypotheses for insignificant influence of factors, the χ² (chi-square) test for contingency tables and Fisher’s exact test for 2×2 tables were applied.

- For the analysis of quantitative variables in independent groups that do not follow a normal distribution, the Mann–Whitney U test was applied.

- To compare two or more relative proportions, the z-test was used. When comparing proportions across more than two groups, p-value correction was applied using the Bonferroni method.

- For comparison of more than two independent groups with variables that do not follow a normal distribution, the Kruskal–Wallis test was used.

- A significance level of p<0.05 was adopted for the null hypothesis.

The statistical analysis of the results was performed using the statistical software SPSS version 23.

Results

Patients

The analysis included 40 children who were followed up, with 18 being girls (45.0%) and 22 being boys (55.0%). The mean age of the participants was 10.59±5.26 years, with no statistically significant difference observed between the two sexes (p=0.376) (Table 2).

Table 2.

Distribution of children by sex

Sex N Min Max Average Median U z p
Girl 18 2.00 21.00 10.0122 10.45 (6.35–12.25) 165.5 −0.885 0.376
Boy 22 2.00 18.00 11.0595 13.45 (5.78–15.13)

The distribution of children by malignant disease is shown in Table 3.

Table 3.

Distribution of children by tumor type

Diagnosis Number of patients
Acute lymphoblastic leukemia (ALL) 3
Teratoma 2
Ewing sarcoma 8
Brainstem glioma 2
Medulloblastoma 4
Osteosarcoma 7
Astrocytoma 1
Neuroblastoma 3
Nephroblastoma 3
Non-Hodgkin lymphoma 5
Soft tissue sarcoma 1
Hodgkin’s disease (Hodgkin lymphoma) 1

Table 4 presents all parameters before and 12 hours after each chemotherapy cycle, by cycle number.

Table 4.

Investigated parameters distributed by chemotherapy cycles

Parameter Chemotherapy cycle sequence N Before chemotherapy 12 hours after chemotherapy
Mean SD SEM Mean SD SEM
Serum creatinine, (µmol/l) First cycle 27 46.222 11.666 2.245 47.074 11.812 2.273
Second cycle 24 49.292 11.719 2.392 48.913 14.497 3.023
Third cycle 17 46.529 11.801 2.862 46.471 10.199 2.474
Cycles from 4 to 10 48 54.396 14.623 2.111 54.042 16.089 2.322
eGFR, ml/min/1.73 m2 First cycle 27 155.947 30.861 5.939 159.581 42.528 8.185
Second cycle 24 147.115 29.595 6.171 156.737 40.228 8.388
Third cycle 17 163.258 35.344 8.572 161.481 36.839 8.935
Cycles from 4 to 10 48 155.478 39.007 5.751 157.137 44.254 6.525
Mg, mmol/L First cycle 27 0.846 0.218 0.044 1.059 0.421 0.081
Second cycle 24 0.827 0.237 0.048 1.091 0.350 0.073
Third cycle 17 0.862 0.058 0.014 1.209 0.431 0.104
Cycles from 4 to 10 48 0.836 0.100 0.015 1.050 0.326 0.047
Phosphorus, mmol/L First cycle 27 1.443 0.232 0.047 1.324 0.237 0.046
Second cycle 24 1.383 0.291 0.061 1.236 0.229 0.048
Third cycle 17 1.504 0.201 0.050 1.300 0.193 0.047
Cycles from 4 to 10 48 1.405 0.467 0.070 1.132 0.354 0.051
NGAL (urine), ng/ml First cycle 27 13.012 24.145 4.735 11.081 27.816 5.353
Second cycle 24 14.821 18.532 3.783 7.443 8.482 1.769
Third cycle 17 19.553 23.372 5.669 7.552 14.428 3.499
Cycles from 4 to 10 48 92.117 308.326 45.460 34.647 92.396 13.477
Parameter Chemotherapy cycle sequence N Before chemotherapy 12 hours after chemotherapy
Mean SD SEM Mean SD SEM
NGAL/creatinine, (urine) First cycle 27 2.527 2.936 0.565 3.821 5.797 1.116
Second cycle 24 4.106 4.634 0.946 7.639 12.480 2.548
Third cycle 17 4.255 6.906 1.675 5.023 7.404 1.796
Cycles from 4 to 10 48 11.800 28.964 4.181 16.139 34.532 4.984
FeP% First cycle 27 12.627 23.038 4.518 27.098 63.489 12.451
Second cycle 24 17.547 35.750 7.454 22.692 19.152 3.994
Third cycle 17 7.390 5.777 1.444 15.707 7.714 1.871
Cycles from 4 to 10 48 13.606 23.239 3.390 25.837 19.140 2.763
Tmp/GFR First cycle 27 1.242 0.399 0.081 0.933 0.875 0.179
Second cycle 24 1.194 0.431 0.090 0.945 0.310 0.065
Third cycle 17 1.405 0.210 0.054 1.096 0.228 0.055
Cycles from 4 to 10 48 1.238 0.604 0.090 0.858 0.389 0.056

NGAL

The analysis included 116 cycles, which were divided into four categories based on their cycle sequence: “first,” “second,” “third,” and “from the fourth to the tenth cycle.” These categories include all of the analysis’s first, second, and third cycles, respectively, while the final category includes all of the fourth to tenth cycles.

The largest percentage of children were included in the “4–10 cycles” group. This is because, during the study period, in addition to newly diagnosed patients, children with disease relapse and those admitted for a new course of chemotherapy involving nephrotoxic medications were also included. Regarding the use of chemotherapy agents with high (ifosfamide, cisplatin) and low (carboplatin) nephrotoxicity, the children were divided into two main groups: a high nephrotoxicity group and a low nephrotoxicity group (Fig. 1).

Figure 1.

Distribution of children by medication type.

A higher percentage of children were treated with highly nephrotoxic medications—92 out of 116 cycles, or 79.3% of all cycles, involved a high-toxicity drug. Fig. 2 presents a summary of the NGAL values at baseline (0 hour) and at 12 hours post-treatment, distributed by cycles.

Figure 2.

Distribution of NGAL levels by chemotherapy cycles.

The figure demonstrates that NGAL levels remain within the reference range and that, even at 12 hours, lower values are observed compared with those measured before the initiation of chemotherapy.

To determine whether there was a statistically significant difference between NGAL levels before each chemotherapy cycle and 12 hours after, the Wilcoxon signed ranks test was used (Table 5).

Table 5.

NGAL levels before and after each consecutive chemotherapy cycle

NGAL ng/ml. N Median z p
(25th - 75th percentile)
Before chemotherapy cycle 12 hours after chemotherapy cycle
First cycle 26 5.8 (1.175–14.575) 2.7 (0.3–8.1) −1.472 0.141
Second cycle 24 6.15 (3.225–23.4) 4.2 (1.3- 12.8) −3.011 0.003
Third cycle 17 9.2 (1.55–33.6) 2 (0.35–7.195) −2.344 0.019
Cycles from 4 to 10 46 10.25(3.575–29.375) 6.5(2.3-12.6) −2.856 0.004

A statistically significant difference in NGAL levels before and 12 hours after the chemotherapy cycle was observed in patients who completed their second cycle (Z=−3.11, p=0.003) and third cycle (Z=−2.344, p=0.019), as well as in patients who underwent four or more chemotherapy cycles (Z=−0.856, p=0.004). However, NGAL levels remained within their reference values. Using KDIGO criteria, two episodes of AKI were diagnosed following treatment with a nephrotoxic agent. In both episodes, NGAL levels remained unchanged.

To avoid the dilution effect resulting from hyperhydration, which is part of the chemotherapy protocol for these children, NGAL levels were compared to creatinine levels in a urine sample collected 12 hours after the chemotherapy cycle. The results are presented in Table 6.

Table 6.

Urine NGAL/creatinine levels distributed by cycles

NGAL/creatinine N Median (IQR z p
Before chemotherapy cycle 12 hours after chemotherapy cycle
First cycle 27 1.5 (0.54–3.09) 1.66 (0–4.9) −0.000 0.317
Second cycle 24 2.145 (1.105–5.842) 2.9 (0.855–9.562) −0.600 0.549
Third cycle 17 1.39 (0.15–5.39) 1.21 (0.53–5.95) −0.355 0.723
Cycles from 4 to 10 48 1.735 (0.89–5.3) 3.22 (1.517–9.684) −2.066 0.039

A statistically significant marker increase was found only in the “4–10 cycles” group, that is, in patients who underwent multiple courses of nephrotoxic chemotherapy.

eGFR

One of the main KDIGO criteria for diagnosing acute kidney injury is the patient’s GFR. Using the original Schwartz formula, GFR values were calculated before and after each chemotherapy cycle. Normal values were considered between 90 and 160 mL/min/1.73 m2, hyperfiltration was defined as values above 160 mL/min/1.73 m2 [5], and reduced GFR as values below 90 mL/min/1.73 m2 (Fig. 3).

Figure 3.

Presentation of eGFR values at 0 and 12 hours.

What stands out is the high percentage of hyperfiltration—almost 50% of all chemotherapy cycles—as well as the diagnosis of only one child with AKI before and two children after chemotherapy.

To determine whether there was a statistically significant difference in eGFR before and 12 hours after chemotherapy for each cycle, the Wilcoxon signed ranks test was used (Table 7).

Table 7.

eGFR values before and after each chemotherapy cycle

eGFR mL/min/1.73 m2 N Median (IQR z p
Before chemotherapy cycle 12 hours after chemotherapy cycle
First cycle 27 157 (133.38–176.57) 164 (128.94–198) −0.148 0.88
Second cycle 23 149.69 (120–169) 149.57 (128.62–182) −0.654 0.51
Third cycle 17 168.30 (141.165–185.215) 165.58 (131.1–189) −0.157 0.88
Cycles from 4 to 10 46 161.63 (116.68–182.94) 162.96 (112.705–191.252) −0.119 0.91

The data show no statistically significant difference in eGFR before and after the chemotherapy cycle.

Given the fact that creatinine levels depend on muscle mass, in pediatric oncology their values are often pseudonormal, even in the presence of kidney injury. The standard Schwartz formula relies primarily on serum creatinine levels and the child’s height, which can lead to an overestimation of eGFR. The literature discusses the use of alternative formulas adapted for pediatric oncology patients[6], two of which are the Schwartz formula corrected for the child’s body surface area, and the formula proposed by Brandt et al., which incorporates the child’s weight and age. The values obtained from these formulas have been compared with those derived using a steady-state methodology involving non-radioactive iothalamate (with a renal clearance similar to that of inulin), measured by high-performance liquid chromatography.[6] Table 8 presents the comparison of GFR values calculated using the classical Schwartz formula and the body surface area – the adjusted Schwartz formula.

Table 8.

Comparison of GFR values calculated using the classical Schwartz formula and the body surface area−adjusted Schwartz formula

eGFR 0 hour At 12 hours
Schwartz Schwartz - corrected Schwartz Schwartz - corrected
Normal GFR (90–160 mL/min/m2) 61 40 55 38
Hyperfiltration GFR (>160 mL/min/m2) 53 30 58 31
Low GFR (<90 mL/min/m2) 1 40 2 41

As demonstrated in Table 8, the data indicates a substantial increase in the number of chemotherapy cycles with reduced eGFR: from 2 at 12 hours using the standard Schwartz formula to 41 cycles when using the corrected formula. Furthermore, there is a decline in the percentage of cycles with hyperfiltration – to 31, which corresponds to 28% of all cycles, a percentage comparable to the results obtained using the radioisotope method for determining eGFR.

Using the adjusted Schwartz formula and the Brandt formula, a statistically significant number of children (p<0.001) were found to have reduced GFR before and after chemotherapy cycles. The number of affected children was nearly the same for both formulas, while the cycles with hyperfiltration were significantly fewer compared to the original formula. Table 9 presents a comparative overview of the differences in eGFR calculation using the three formulas.

Table 9.

Presentation of the differences in eGFR calculation using the three formulas

%
eGFR Test 1 vs Test 2 Difference z-score 95% CI 95% CI p value (corrected)
Normal Schwartz Brandt −6.00% −1.06 −0.17 0.05 0.58
Normal Schwartz Schwartz-corrected 10.00% 2 0 0.2 0.14
Normal Brandt Schwartz-corrected 16.00% 3.08 0.06 0.26 0.01
Lowered Schwartz Brandt −46.00% −8.14 −0.58 −0.35 <0.001
Lowered Schwartz Schwartz-corrected −46.00% −8.14 −0.58 −0.35 <0.001
Lowered Brandt Schwartz-corrected 0.00% 0.00% −0.15 0.15 >0.99
Hyperfiltration Schwartz Brandt 49.00% 8.48 0.38 0.6 <0.001
Hyperfiltration Schwartz Schwartz-corrected 26.00% 3.71 0.12 0.39 <0.001
Hyperfiltration Brandt Schwartz-corrected −23.00% −4.22 −0.34 −0.13 <0.001

A comparison was made between the proposed formulas to determine which formula detects the most deviations (reduced eGFR as well as hyperfiltration), with the adjusted Schwartz formula standing out as the one identifying the most pathology (Table 10).

Table 10.

Comparison of the three formulas for determining eGFR

Formula for calculating GFR Normal n (%) Reduced GFR or hyperfiltration n (%) Difference in relative proportions (Δ) z statistics 95% CI 95% CI p-value
(L) (U)
Schwartz 54 (47.79%) 59 (52.21%) −4.42% −0.67 −0.17 0.09 0.51
Brandt 63 (55.75%) 50 (44.25%) 11.50% 1.74 −0.01 0.24 0.16
Schwartz (Corrected) 38 (34.23%) 73 (65.77%) −31.53% −4.95 −0.44 −0.19 <0.01

Since the corrected Schwartz formula identified the highest number of pathological cases, it was selected for use in subsequent analyses.

In the course of the study, the elevated rate of hyperfiltration raised the question of whether this finding could be attributed to tubular creatinine loss in the context of tubular injury, which is recognized as the principal pathogenic mechanism of drug-induced nephrotoxicity.

The main markers for tubular injury are the fractional excretion of phosphate and the more accurate indicator relative to the patient’s GFR — the renal phosphate threshold. To clarify the etiology of hyperfiltration, these two indicators — eGFR and fractional excretion of phosphate/renal phosphate threshold — were compared. Using the classical Schwartz formula for calculating eGFR, children with reduced eGFR were detected only in the “4–10 cycles” group. The comparison between eGFR and fractional phosphate excretion data is presented at Table 11.

Table 11.

Comparison between eGFR and fractional phosphate excretion

Filtration First cycle First cycle Second cycle Second cycle Third cycle Third cycle Cycles 4–10 Cycles 4–10
FeP<20% FeP>20% FeP<20% FeP>20% FeP<20% FeP>20% FeP<20% FeP>20%
Hyperfiltration
eGFR>160 mL/min/1.73 m2 92.9% 7.1% 80.0% 20.0% 88.9% 11.1% 58.3% 41.7%
Normal filtration
eGFR=90–160 mL/min/1.73 m2 69.2% 30.8% 58.3% 41.7% 62.5% 37.5% 38.1% 61.9%
Low filtration
eGFR<90 mL/min/1.73 m2 0.0% 100.0%

The comparison revealed no positive correlation between hyperfiltration and increased fractional excretion of phosphate, which is particularly evident during the first chemotherapy cycle. In all children with reduced eGFR, an elevated FeP% was observed.

A comparison was again made between the 12-hour eGFR calculated using the body surface area – corrected Schwartz formula and the fractional excretion of phosphate (FeP%) (Fig. 4).

Figure 4.

Comparison between FeP and eGFR (Schwartz-corrected).

Once again, no positive correlation was found between the cycles with hyperfiltration and elevated FeP% (>20%). Using the adjusted formula, it was found that elevated FeP% was mainly observed in children from cycles with reduced eGFR. Only in the “4–10” cycle it was equally represented across all eGFR groups, unlike the classic Schwartz formula, where increased phosphate excretion was mainly seen in children with normal eGFR.

Fractional excretion of phosphate and renal phosphate threshold

Nephrotoxic chemotherapeutic agents primarily cause tubular damage, which is why this study focused on two tubular markers: the fractional excretion of phosphate (FeP%) and renal phosphate threshold, because phosphate reabsorption is disrupted earliest in tubular injury, making it the most clinically significant impairment. Both markers were measured before and at 12 hours after each chemotherapy course. Fig. 5 presents the summarized results for all cycles.

Figure 5.

Levels of FeP% at 0 and at 12 hours.

A statistically significant change was observed in the number of patients with FeP% above 20%. Before the chemotherapy cycle, 10.3% of the subjects had FeP% above 20%, while at 12 hours after the cycle, this increased to 35.3%. This indicates a statistically significant rise in the number of patients with fractional excretion of phosphate exceeding 20% at 12 hours post-chemotherapy (Table 12).

Table 12.

Dynamics of FeP% values for all 116 cycles

Marker Before chemotherapy cycle At 12 hours after the chemotherapy cycle Change (% and 95% CI) P value
FeP% 10.3% 35.3% 25.00% (15.78, 34.22) <0.0001

Distributed by cycles, the results for fractional excretion of phosphate at 0 hour and at 12 hours are presented in Fig. 6.

Figure 6.

Changes in fractional excretion of phosphates before and after chemotherapy across treatment cycles.

Presented by cycles, it was found that FeP% increases significantly during the “4–10” cycle at 12 hours after chemotherapy, indicating that tubular damage increases with the number of chemotherapy cycles administered.

Using the Wilcoxon signed ranks test (Table 13), a statistically significant difference was found in urinary FeP% levels before and after chemotherapy cycle, across all consecutive cycles.

Table 13.

Urinary FeP% values before and after consecutive chemotherapy cycle

Parameters
Median (IQR)* z p
FeP% in urine N Before chemotherapy cycle 12 hours after chemotherapy cycle
First cycle 26 7.47 (3.597–13.192) 11.36 (9.067–17.597) −2.381 0.017
Second cycle 23 9.28 (3.75–16.31) 16.38 (9.23–27.41) −2.281 0.023
Third cycle 16 7.24 (2.107–12.087) 17.88 (8.595–19.96) −2.896 0.004
Cycles from 4 to 10 47 6.91 (4.69–11.44) 19.72 (13.85–30.157) −2.856 <0.001

Renal phosphate threshold

The renal phosphate threshold (TmP/GFR) is a more accurate marker as it is adjusted for the glomerular filtration rate. A statistically significant change was observed in the renal phosphate threshold across all cycles. Before chemotherapy, 77.6% (90 cycles) had a normal renal phosphate threshold, while at 12 hours after therapy, this decreased to 58.6% (68 cycles). This represents a statistically significant reduction (p=0.0031) in the number of cycles with a normal renal phosphate threshold at 12 hours post-chemotherapy. Fig. 7 graphically presents the values of this parameter by cycles.

Figure 7.

Renal phosphate threshold at 0 and 12 hours presented by cycles.

A statistically significant difference in levels was again observed in each of the chemotherapy cycles performed (Table 14).

Table 14.

Values of renal phosphate threshold (TmP/GFR) before and after consecutive chemotherapy cycles

Parameter
Median (IQR)* z p
Tmp/GFR N Before chemotherapy cycle 12 hours after chemotherapy cycle
First cycle 24 1.335 (1.063–1.527) 1.103 (0.922–1.34) −2.419 0.016
Second cycle 23 1.259 (1.053–1.486) 0.986 (0.616–1.202) −2.224 0.026
Third cycle 15 1.384 (1.24–1.519) 1.02 (0.975–1.236) −2.897 0.004
Cycles from 4 to 10 45 1.301 (1.108–1.525) 0.866 (0.649–1.108) −4.713 <0.001

To assess the clinical significance of tubular damage, serum phosphorus and magnesium levels were monitored. Hypophosphatemia was detected in 5 cycles before chemotherapy, representing children undergoing a subsequent cycle, which accounts for 4% of all cycles. At 12 hours after chemotherapy, hypophosphatemia was observed in 14 cycles, representing 12% of all cycles. To evaluate whether the changes in electrolytes, namely phosphorus and magnesium, across cycles were statistically significant, the Wilcoxon signed ranks test was again used. The results are presented in Tables 15 and 16.

It was found that there was a statistically significant difference in serum phosphorus levels during the third and “4–10” cycles. Regarding magnesium, statistically significant changes were observed during the second, third, and “4–10” cycles. These data indicate that with the accumulation of nephrotoxicity, tubular injury progresses to clinically evident electrolyte disturbances, ultimately resulting in clinical manifestations.

Discussion

One of the widely discussed in the literature early biomarkers for diagnosing subclinical AKI, as well as a predictive marker for the development of clinically manifested AKI, is NGAL. This marker has been shown to have high sensitivity and specificity for detecting acute kidney injury following an episode of hypoxia (e.g., newborns with perinatal asphyxia, children after cardiac surgery under cardiopulmonary bypass).[7-12] Regarding its role as a marker of drug-induced nephrotoxicity, and specifically following chemotherapy, there is very little and conflicting data.[13-17] Table 17 presents the most frequently cited studies on NGAL and its role as an early marker of AKI in pediatric oncology.[18,19]

Table 15.

Serum phosphorus levels before and after consecutive chemotherapy cycles

Parameter
Median (IQR)* z p
Phosphorus mmol/L N Before chemotherapy cycle 12 hours after chemotherapy cycle
First cycle 24 1.435 (1.332–1.617) 1.34 (1.112–1.495) −1.544 0.123
Second cycle 23 1.43 (1.2–1.56) 1.25 (1.09–1.43) −1.703 0.089
Third cycle 16 1.48 (1.392–1.655) 1.24 (1.19–1.415) −2.793 0.005
Cycles from 4 to 10 45 1.4 (1.25–1.585) 1.17 (0.892–1.392) −1.088 <0.001
Table 16.

Serum magnesium levels before and after consecutive chemotherapy cycles

Parameter
Median (IQR)* z p
Magnesium mmol/L N Before chemotherapy cycle 12 hours after chemotherapy cycle
First cycle 25 0.87 (0.815–0.92) 0.87 (0.77–1.44) −1.543 0.123
Second cycle 24 0.82 (0.75–0.907) 1.17 (0.79–1.38) −2.695 0.007
Third cycle 16 0.865 (0.822–0.902) 1.27 (0.815–1.57) −2.529 0.011
Cycles from 4 to 10 45 0.83 (0.76–0.9) 0.885 (0.79–1.387) −2.761 0.006
Table 17.

Studies on NGAL in pediatric oncology as a marker for nephrotoxicity

Year Authors Medication Number of patients NGAL results
2022 Asma Abdel Sameea Mahmoud et al. Cisplatin, methotrexate 52 children after treatment A good predictor of tubular damage in children who have completed treatment
2017 Kelly R. McMahon et al.[16] Cisplatin 150 children Predictive marker for AKI
2021 Eryk Latoch et al. Ifosfamide, cisplatin 60 children after treatment Marker for CKD
2022 Kelly R. McMahon et al.[14] Cisplatin 159 children Predictive marker, but during the later chemotherapy cycles
2017 Maya Sterling et al.[15] Ifosfamide, cisplatin 51 children Predictive for AKI with ifosfamide, but not with cisplatin
2015 Mohamed A. Almalky[13] Cisplatin, ifosfamide 60 children High predictive value for AKI
2014 Elisa Ylinen et al.[17] High dosed methotrexate 20 children No advantages over other markers – cystatin C
2015 V. Kesik[19] Ifosfamide 29 children Marker for CKD diagnosing
2016 Katarzyna Musiol et al. Cisplatin, ifosfamide, methotrexate 38 children Low sensitivity for CKD

In the study by Mohamed Almalky et al., conducted among 30 children with solid tumors undergoing chemotherapy, the authors found a statistically significant difference between the number of children diagnosed with AKI based on urinary NGAL levels (10 out of 30) and those diagnosed using serum creatinine levels (0 out of 30). However, they used an NGAL cutoff value of >25 ng/mL for predictive significance[13], rather than the >113 ng/mL threshold commonly discussed in the literature.

In another large prospective study by McMahon et al., involving 159 children undergoing chemotherapy with cisplatin, urinary NGAL levels were found to have low predictive value for AKI. Higher NGAL values were observed during later infusions of the drug compared to the initial cycles of therapy (AUC-ROC 0.54–0.75 for late infusion vs. AUC-ROC 0.48–0.65 for early infusion).[14]

In a study by Miloševski-Lomić et al., published in 2023, emphasis is placed on the higher predictive value of using a combination of biomarkers (NGAL, KIM-1, L-FABP) for the diagnosis of AKI following the administration of nephrotoxic drugs in children, compared to the use of each biomarker individually.[20]

In our study, using the KDIGO criteria, two episodes of AKI were diagnosed following courses of nephrotoxic medication. In both episodes, NGAL levels remained unchanged. In two children with disease relapse, elevated NGAL levels were observed both before and after the chemotherapy course; all of these courses were within the “cycle 4–10” group, meaning the patients had undergone previous chemotherapy cycles. For the rest of the children, especially for the second, third, and “4–10” cycles, a statistically significant difference in NGAL levels was observed; however, the values remained within the reference range and, in most cases, were lower than those measured prior to chemotherapy. One of the questions that arises is whether this could be due to urine dilution as a result of the hyperhydration administered to all children after each chemotherapy cycle. To account for this dilution effect, a NGAL-to-creatinine ratio was calculated from a single urine sample collected 12 hours after the chemotherapy cycle. A statistically significant increase of this ratio was observed only in the “4–10 cycle“ group, indicating that NGAL levels rise as a result of the cumulative nephrotoxic effect—an observation consistent with the findings of Kelly R. McMahon. This identifies NGAL as a marker that is not reliable for diagnosing AKI induced by nephrotoxic drugs, particularly chemotherapeutic agents.

Regarding the classical indicators for diagnosing AKI, the more accurate measure is the estimation of eGFR. Its determination is extremely important in oncology, especially when nephrotoxic chemotherapy regimens are to be administered, in order to optimize drug dosing according to renal function. There are very few studies in the literature concerning GFR in pediatric oncology patients. The method used to determine GFR is also widely discussed. The most accurate methods for measuring absolute GFR are radioisotope techniques (such as [51Cr]-EDTA). Unfortunately, the need for multiple blood samples or the lack of access to the respective radioisotope makes these methods rarely used and less preferred in clinical practice. One of the proposed methods for estimating GFR is the classical Schwartz formula, which, given the low body weight of pediatric oncology patients, is recommended to be adjusted for body surface area.[6] An interesting finding across all these studies is the high prevalence of hyperfiltration, regardless of the chosen GFR measurement method, both before the start of treatment and after the initial chemotherapy cycles. Such is the study by Kwatra et al., which found a 43.5% rate of hyperfiltration among 177 children, measured by DTPA (diethylenetriaminepentaacetic acid) clearance during at least one chemotherapy cycle. In a study by Hjorth et al., also using a radioisotope method (iohexol clearance), a hyperfiltration rate of 31% was reported.[6,21,22] In the follow-up of eGFR levels in our study, a high rate of hyperfiltration was also observed, comparable to the cited studies, both at disease onset (55 cycles, 47.8%), and after chemotherapy (58 cycles, 50.43%) measured at 12 hours post-cycle. Hyperfiltration was defined as eGFR values >160 mL/min/1.73 m2. The pathophysiology of hyperfiltration is widely discussed, with the most accepted theory being the hypermetabolic state in which children with oncological diseases find themselves, especially during chemotherapy.[6,21,22] The hypothesis of increased creatinine excretion due to tubular injury during chemotherapy cycles as an etiology for hyperfiltration was not confirmed, since no statistically significant correlation was found between children with hyperfiltration and fractional excretion of phosphate or the renal phosphate threshold.

The use of the classical Schwartz formula is an easy and widely accessible method but tends to overestimate glomerular filtration. There are few studies in the literature regarding which formula to use for pediatric oncology patients, but all recommend formulas based on body weight. These include the body surface area–corrected Schwartz formula, the Brandt formula, or the Cockcroft–Gault formula.[6,21-25] Our study used two of these formulas, the adjusted Schwartz formula and the Brandt formula. With both formulas, the percentage of hyperfiltration observed with the classical Schwartz formula decreased significantly but remained high—up to 28%. This result is comparable to those obtained in studies measuring GFR in pediatric oncology patients using radioisotope methods; however, an increase in cases with reduced eGFR was also observed. Unlike the classical formula, where children with increased fractional excretion of phosphate and correspondingly low renal phosphate threshold mostly have normal GFR, the formulas based on body weight tend to show these children predominantly have reduced GFR.

Some of the medications used in pediatric oncology, especially in children with solid tumors, are nephrotoxic and primarily cause tubular injury. The proximal tubule, being the most metabolically active part of the tubular apparatus, is the most affected. This leads to the manifestation of partial or generalized proximal tubular dysfunction—renal Fanconi syndrome. The clinical presentation is characterized by aminoaciduria, hypophosphatemia, low-molecular-weight proteinuria (β2-microglobulin, retinol-binding protein), normoglycemic glucosuria, metabolic acidosis, hypokalemia, and hypouricemia. While tubular proteinuria and glucosuria do not have a significant impact on the body, increased phosphate excretion leading to hypophosphatemia is one of the main clinically important tubular dysfunctions as it leads to skeletal demineralization and osteomalacia, proximal muscle weakness, bone pain, spontaneous fractures, or the development of hypophosphatemic rickets. Due to preserved distal tubular function, polyuria is a rare clinical finding, and metabolic acidosis is generally mild (HCO3>15).[25-28]

By using only standard methods to assess renal function—serum creatinine levels and oliguria—this type of injury often goes unrecognized, since glomerular function is usually preserved, except in cases where the distal tubule is also affected and creatinine secretion is impaired. In the conducted study, increased phosphate excretion was observed before the chemotherapy cycle in only 10% of all 116 cycles, while 12 hours after the treatment course, the percentage increased significantly to 35.5%. Furthermore, as the cycle number increased, so did the number of children with tubular injury, indicating a cumulative nephrotoxic effect. A similar result was observed using the more precise marker, the renal phosphate threshold. A low renal phosphate threshold was present before the start of therapy in only 17 cycles (14.7%), and 12 hours after the cycle in 44 cycles (37.0%). Here too, an increase in the percentage of low renal phosphate threshold cases was observed with the increasing number of chemotherapy cycles administered.

Monitoring serum electrolyte levels of phosphorus and magnesium shows that with the cumulative nephrotoxic effect over successive cycles, clinically manifest hypophosphatemia and hypomagnesemia develop. The frequency of hypophosphatemia reported in the literature varies from 0 to 36.8%, and tubular phosphate reabsorption disorders range from 0 to 62%.[29] Our data are consistent with the ones reported in the literature. As a rule, upon the manifestation of tubular toxicity, the medication should be discontinued, or if it must be continued (with no alternative), its dose should be reduced according to the severity of acute kidney injury. In most cases, tubulopathy is reversible and transient, lasting a few months, but in some children, it becomes chronic, with a reported incidence of tubular injury reaching up to 20–25%.[30] This necessitates the development of a monitoring algorithm for children who have undergone chemotherapy, including for late side effects related to kidney function.

We present an algorithm for monitoring renal function in children undergoing chemotherapy with nephrotoxic drugs (Fig. 8).

Figure 8.

Algorithm for monitoring renal function in pediatric patients receiving nephrotoxic chemotherapy.

Our study has certain limitations, most notably the small sample size and its single-center design, which restrict the ability to draw more robust conclusions.

Conclusions

Acute kidney injury (AKI) is a common complication in the treatment of pediatric oncology patients. One of the leading risk factors for its development is drug-induced nephrotoxicity, particularly associated with nephrotoxic chemotherapeutic agents. This necessitates careful monitoring of renal function both during therapy and after its discontinuation.

The widely discussed biomarker NGAL, although considered an early marker of AKI, has limitations when used as a standalone diagnostic tool in children receiving nephrotoxic chemotherapy. While its role has been better established in hypoxic conditions and certain surgical settings, its diagnostic and predictive value in pediatric oncology remains controversial. It is influenced by multiple factors, including the cumulative effect of treatment cycles and the impact of hyperhydration.

The measurement of eGFR remains a key parameter in evaluating kidney function in pediatric oncology patients. The use of formulas adjusted for body surface area or body mass provides more accurate assessments compared to the classical Schwartz formula.

Markers of tubular injury, such as FeP% and Tmp/GFR (renal phosphate threshold), can serve as valuable tools for the early detection of drug-induced nephrotoxicity in pediatric oncology. The observed cumulative toxicity highlights the urgent need for the development of a standardized algorithm for monitoring renal function both during and after the completion of oncological treatment in order to ensure timely intervention and reduce the risk of progression to chronic kidney disease.

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Additional information

Ethical approval

This study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Ethics Committee of the Medical University of Plovdiv (protocol No. HO-11/2021).

Ethical statements

  • The authors declared that no clinical trials were used in the present study.
  • The authors declared that no experiments on humans or human tissues were performed for the present study.
  • Written informed consent was obtained from all subjects involved in the study. The signed informed consent forms are safely deposited at the Department of Pathophysiology in the Medical University of Plovdiv and available for review upon request.
  • The authors declared that no experiments on animals were performed for the present study.
  • The authors declared that no commercially available immortalized human and animal cell lines were used in the present study.

Conflict of interest

We declare no conflict of interest between the authors of this paper and other entities.

Artificial Intelligence (AI) use

he authors accept full responsibility for the content of the manuscript, including the disclosure of any use of AI.

No AI tools were used in the preparation of this manuscript.

Funding

No funding was reported.

Author contributions

All authors have contributed equally to the preparation of this manuscript and have permitted their names to be included as co-authors.

Author ORCIDs

Kameliya Bratoeva https://orcid.org/0000-0002-6235-3384

Data availability

All data used are referenced or included in the article.

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