Abstract
Acute kidney injury (AKI) is a complication of COVID-19 and urinary biomarkers of renal “stress” and “injury” can be utilised for an early diagnosis. In this prospective observational study, critically-ill adult patients with COVID-19 disease from November 2020 to May 2021 were recruited and urine samples collected at the time of admission to ICU, and subsequently at 12, 24 and 48 h and analysed for biomarkers of “renal injury” namely KIM-1, NGAL, IL-18, and cell cycle arrest biomarker IGFBP-7 and TIMP-2 which have been reported to increase following renal stress. Spot urine samples were collected and to adjust for dilution, sample volume and rate of urine production, assays of the biomarkers were normalised by dividing the values with urinary creatinine concentration. The incidence of AKI in our study was 22.64% (24/106). Patients in the AKI group were older with significantly higher requirements for invasive mechanical ventilation, inotropic support, longer hospital stay, and higher mortality. Diagnostic performance of various biomarkers to predict AKI was compared with receiver operator characteristics curve analysis. TIMP-2 × IGFBP7 > 0.301 (ng/ml)2/1000 predicted AKI with sensitivity and specificity of 84.1% and 31.6% (AUC = 0.579, and p < 0.035), respectively. AUC was 0.663 with a p-value < 0.001 when cut-off values after normalisation were > 0.87 (ng/mmol)2/1000. IL-18, TIM-1, NGAL predicted AKI at cut-off values of 24.936, 12,372.766, and 8017.74 ng/mmol with AUC of 0.653 (p ≤ 0.001), 0.646 (p ≤ 0.001), and 0.552 (p = 0.035), respectively. Urinary cell cycle arrest biomarkers have a more promising role over biomarkers of renal injury in identification of patients at risk of AKI.
Keywords: Urinary kidney stress biomarkers, Insulin-like growth factor-binding protein 7 (IGFBP-7), Tissue inhibitor of metalloproteinases 2 (TIMP-2), Neutrophil gelatinase-associated lipocalin (NGAL), Kidney injury molecule 1 (KIM-1), Interleukin-18 (IL-18), Acute renal failure, COVID-19
Introduction
The incidence of acute kidney injury (AKI) in critically ill Coronavirus disease-2019 (COVID-19) patients has been reported to be about 27% [1]. AKI increases mortality (74.31%) and thus, identification of patients “at risk” of AKI is important.
A pro-inflammatory urinary cytokine storm characterizes AKI in patients with COVID-19. Interleukin-12, macrophage inflammatory protein-1β, Interleukin-1 receptor, granulocyte-colony stimulating factor, interferon-gamma-inducible protein 10 and Interleukin-5 have been reported to be predictive of AKI [2]. Cell cycle arrest urinary biomarker insulin-like growth factor-binding protein 7 (IGFBP-7) and tissue inhibitor of metalloproteinases (TIMP-2) are predictive of subclinical AKI in critically ill COVID-19 patients [3, 4]. However, the utility of pre-injury (“stress induced”) cell cycle arrest biomarker versus injury biomarkers has not been evaluated. This prospective observational study was conducted to compare the diagnostic performance of urinary cell cycle arrest markers (product of IGFBP-7, and TIMP-2) and biomarkers of renal injury [Neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule 1/T cell immunoglobulin mucin domain-1 (KIM-1/TIM-1), Interleukin-18 (IL-18)] in predicting AKI in critically ill adult patients admitted in COVID ICU.
Material and Methods
This prospective observational cohort study was conducted in a tertiary care research and referral hospital after obtaining approval from the institute ethics committee (Reference number NK/6759/Study/331) and written informed consent from the patients/or next of kin. Patients aged ≥ 18 years with COVID-19 infection confirmed by real-time polymerase chain reaction (RT-PCR) and severe illness defined as SpO2 < 94% on room air at sea level, a ratio of arterial partial pressure of oxygen to fraction of inspired oxygen (PaO2:FiO2) < 300 mmHg, a respiratory rate > 30 breaths/min, or with lung infiltrates in chest X-ray suggestive of COVID-19 pneumonia, were recruited from November 2020 to May 2021. Patients with pre-existing AKI, a history of chronic kidney disease (CKD) with estimated glomerular filtration rate (eGFR) < 60 ml/min, end-stage renal failure on regular dialysis, kidney transplant recipients, and pregnant and breastfeeding women were excluded.
The investigators collected urine samples from the patients eligible for the study at the time of admission to the ICU and subsequently at 12, 24, and 48 h after inclusion. Patients were followed up for the development of AKI using Kidney Disease Improving Global Outcomes (KDIGO) criteria from the first day of ICU admission until the first five days of admission/discharge or death, whichever was earlier [5].
Urine collection was terminated in patients who developed AKI. Details of demographic data, chronic comorbidities, and clinical features on admission were recorded. A clear, clean, leak proof, disposable, and particle-free urine collection specimen container labelled with the patient's full name and hospital registration number, date, and time of the sample collection was used as per infection control precautions. The collected sample was stored immediately at a low temperature (4 °C), and after 48 h, the sample was centrifuged at 4000g for 15 min. The supernatant was collected and stored at − 80 °C and analysed using enzyme-linked immunosorbent assay (ELISA) for KIM-1/TIM-1(ELISA Kit, Cat No ab235081), NGAL (ELISA Kit Cat No KIT036), IL-18 (ELISA Kit ab215539), USA), and product of IGFBP-7 (ELISA Kit; ab213790) and TIMP-2 (ELISA Kit; ab270213).
The laboratory tests, namely hemoglobin, neutrophil-to-lymphocyte ratio, the ratio of partial pressure of arterial oxygen to the fraction of inspired oxygen (PaO2:FiO2), serum lactate levels, total leucocyte count (TLC), platelet count, activated partial thromboplastin test (APTT), international normalized ratio (INR), D-dimer, fibrinogen, total proteins, albumin, liver function tests viz. aspartate aminotransferase (AST), alanine aminotransaminase (ALT), lactate dehydrogenase (LDH), pro-brain type natriuretic peptide (pro-BNP), troponin-T, C-reactive protein (CRP), serum ferritin and procalcitonin were monitored daily.
Normally distributed continuous data were expressed as the mean ± standard deviation, and non-normally distributed continuous variables were expressed as the median [25th percentile, 75th percentile]. The Chi-square or Fisher’s exact test was used as applicable for categorical variables, and Student's t-test was used to compare continuous variables. IBM® SPSS® for Windows, Version 20.0, Armonk, NY: IBM Corp. was used to conduct the analysis, and a two-tailed p-value of < 0.05 was considered statistically significant. The statistically significant predictors obtained through the regression model were considered for the receiver operating characteristics (ROC) curve analysis.
Results
Out of 300 patients screened for inclusion in the COVID-ICU, 164 fulfilled the inclusion criteria for recruitment (Fig. 1). Out of these, 106 patients could be followed up within the first 48 h of admission, and thus, ELISA for all urinary biomarkers was done in this cohort.
Fig. 1.
Study flow diagram
As per the KDIGO criteria, 18 patients had stage 1 and 2 AKI, six patients developed stage 3 AKI, and the overall incidence of AKI was 22.6% (n = 24). The median day of development of AKI was 3 (range 2–5) days post-admission.
The demographic characteristics of the entire cohort are summarised in Table 1. Patients who developed AKI were older (p = 0.039) and had a higher requirement for invasive mechanical ventilation and inotropic support (p < 0.001). The hospital stay was longer in the AKI group, which also had a statistically significant higher mortality (p = 0.001).
Table 1.
Demographic and clinical profile of cohort
| Parameter(s) | Total (n = 106) | AKI patients (n = 24) | Non-AKI patients (n = 82) | p-value |
|---|---|---|---|---|
| Age (years) | 57.0 (44.75, 66.00) | 59.50 (50.25, 73.25) | 55.00 (41.50, 65.00) | 0.039* |
| Male sex (n, %) | 62 (58.49) | 16 (66.67) | 46 (56.09) | 0.481 |
| Existing co-morbidities (n, %) | ||||
| Hypertension | 53 (50.00) | 15 (62.50) | 38 (46.34) | 0.246 |
| Diabetes mellitus | 43 (40.56) | 10 (41.67) | 33 (40.24) | 1.000 |
| Coronary artery disease | 15 (14.15) | 5 (20.83) | 10 (12.19) | 0.322 |
| Chronic obstructive pulmonary disease | 1 (0.94) | 0.00 | 1 (1.22) | 1.000 |
| Bronchial asthma | 3 (2.83) | 1 (4.16) | 2 (2.44) | 0.541 |
| Pulmonary tuberculosis | 1 (0.94) | 0.00 | 1 (1.22) | 1.000 |
| Cerebrovascular accident | 2 (1.88) | 1 (4.16) | 1 (1.22) | 0.403 |
| Obesity | 9 (8.49) | 3 (12.5) | 6 (7.32) | 0.420 |
| Hypothyroidism | 8 (7.54) | 3 (12.5) | 5 (6.09) | 0.377 |
| Clinical features on admission (n, %) | ||||
| Fever | 92 (86.79) | 21 (87.50) | 71 (86.58) | 1.000 |
| Cough | 68 (64.15) | 14 (58.33) | 54 (65.85) | 0.629 |
| Shortness of breath | 89 (83.96) | 21 (87.50) | 68 (82.92) | 0.757 |
| Myalgia | 3 (2.83) | 1 (4.16) | 2 (2.43) | 0.541 |
| Sore throat | 8 (7.54) | 0.00 | 8 (9.75) | 0.194 |
| Ageusia | 1 (0.94) | 0.00 | 1 (1.22) | 1.000 |
| Gastro-intestinal symptoms | 4 (3.77) | 0.00 | 4 (4.88) | 0.572 |
| ICU management | ||||
| Need for intubation (n, %) | 45 (42.45) | 19 (79.16) | 26 (31.70) | < 0.001* |
| Inotropes (n, %) | 25 (23.58) | 15 (62.50) | 10 (12.19) | < 0.001* |
| Hospital stay (days) | 15.00 (11.00, 21.25) | 20.00 (15.00, 27.00) | 15.00 (10.75, 19.00) | 0.010* |
| Mortality (n, %) | 46 (43.39) | 18 (75.00) | 28 (34.14) | 0.001* |
*p-value < 0.05 is significant
The hemoglobin and albumin values were lower in the AKI group and the difference was statistically significant. In contrast, neutrophil to lymphocyte ratio, procalcitonin, troponin-T, and pro-BNP values were significantly higher on the day of admission in ICU in patients who subsequently developed AKI 48 h after ICU admission (Table 2).
Table 2.
Biochemical parameters on ICU admission
| S. No | Biochemical parameter | Total (n = 106) | AKI (n = 24) | Non AKI (n = 82) | p-value |
|---|---|---|---|---|---|
| 1 | Hemoglobin*(g/dl) | 11.73 ± 2.14 | 10.65 ± 2.27 | 12.05 ± 2.01 | 0.004* |
| 2 | PaO2:FiO2 ratio (mmHg) | 111.5 (83.75, 144) | 106.69 (85.53, 139.18) | 112 (82.60, 144) | 0.81 |
| 3 | Lactate (mmol/l) | 2.0 (1.63, 2.52) | 2.0 (1.53, 2.92) | 2.00 (1.63, 2.48) | 0.56 |
| 4 | Total leucocyte count (× 109/L) | 12.40 ± 5.41 | 14.78 ± 7.81 | 11.70 ± 4.30 | 0.08 |
| 5 | Platelet count (× 109/L) | 228 (150, 332) | 228 (140, 311) | 230.5 (151, 336) | 0.23 |
| 6 | Activated prothrombin time (s) | 29.5 (27.48, 33.13) | 31.6 (27.63, 38.35) | 29.05 (27.38, 33.00) | 0.24 |
| 7 | International normalized ratio | 1.165 (1.08, 1.25) | 1.20 (1.10, 1.25) | 1.16 (1.07, 1.25) | 0.38 |
| 8 | D-dimer (ng/ml) | 1174.76 (610.69, 3639.75) | 1222.5 (763.02, 13,778.75) | 1132.75 (530.31, 3115) | 0.06 |
| 9 | Fibrinogen (g/l) | 5.3 (4.32, 6.62) | 5.00 (3.51, 6.15) | 5.49 (4.42, 6.69) | 0.11 |
| 10 | Total proteins*(g/dl) | 6.39 ± 0.74 | 5.87 ± 0.64 | 6.54 ± 0.70 | 0.06 |
| 11 | Aspartate aminotransferase (U/L) | 47 (31.5, 84.08) | 56.95 (33.85, 110.73) | 45.75 (27.7, 77.45) | 0.08 |
| 12 | Alanine aminotransferase (U/L) | 42.9 (23.75, 71.08) | 54.25 (25.75, 91.3) | 41.75 (23.53, 68.5) | 0.21 |
| 13 | Lactate dehydrogenase (U/L) | 467 (330, 607) | 525 (384, 677.5) | 463 (324.75, 600) | 0.30 |
| 14 | Ferritin (ng/ml) | 816.5 (421.88, 1400) | 1211 (521, 1713) | 765 (390, 1244) | 0.10 |
| 15 | NLR | 14.666 (8.58, 30.34) | 27.368 (14.15, 45.09) | 12.5 (7.81, 22.88) | 0.002* |
| 16 | Procalcitonin (ng/ml) | 0.163 (0.083, 1.07) | 0.74 (0.263, 3.100) | 0.133 (0.070, 0.406) | < 0.001* |
| 17 | Albumina (g/dl) | 3.12 ± 0.44 | 2.86 ± 0.40 | 3.19 ± 0.43 | 0.001* |
| 18 | CRP (mg/l) | 77.6 (54.03, 128.25) | 100 (60, 158) | 72 (49.37, 110.30) | 0.05 |
| 19 | Troponin-T (pg/ml) | 12 (5.65, 28.33) | 26.55 (13.14, 62.27) | 9.38 (5.25, 22.23) | 0.004* |
| 20 | CKMB (U/L) | 23.3 (17, 31.55) | 24.4 (17, 31.1) | 21.5 (16.97, 32.4) | 0.74 |
| 21 | Pro-BNP (pg/ml) | 573.4 (254.95, 1610.5) | 1420 (439, 2612) | 468.55 (237.4, 1321.75) | 0.01* |
CKMB—Creatine kinase MB, CRP—C-reactive protein, NLR—neutrophil to lymphocyte ratio, PaO2:FiO2—ratio of partial pressure of arterial oxygen to fraction of inspired oxygen, Pro-BNP—Pro-brain type natriuretic peptide
*p-value < 0.05 is significant
aExpressed as mean ± standard deviation. All other parameters are expressed as median (inter-quartile range)
As per methodology, four urine samples were to be collected from each patient, so the number of samples for 106 patients was expected to be 424. However, only 417 urine samples could be sent for ELISA. Urinary creatinine could not be estimated in 20 samples, and the corresponding values of serum creatinine could not be traced for 31 samples. In 26 samples, the corresponding creatinine values were above 1.5 at all time periods of collection (undiagnosed CKD), and the collection time of 59 samples was after the patients had developed AKI. Thus, the results of urinary biomarkers from 281 samples were used to predict the risk of AKI in the cohort.
Normalised values of IL-18, TIM-1, and product of TIMP-2 and IGFBP-7 biomarkers were significantly higher in 44 samples collected from 24 patients who subsequently developed AKI within 48 h of admission in ICU when compared with values derived from 237 samples of 82 patients who did not develop AKI (Table 3). The normalised value of NGAL was higher in the AKI group but did not meet statistical significance (Table 3).
Table 3.
Comparison of the averaged urinary biomarker levels collected at 0, 12, 24, and 48 h post ICU admission in AKI and non-AKI group
| Parameters | AKI | Non-AKI | p-value |
|---|---|---|---|
| Normalised IL-18 | 17.7 (7.61, 40.65) | 9.56 (4.48, 17.86) | 0.001* |
| Normalised NGAL | 8350.75 (2918.27, 14,332.26) | 4882.4 (2240.64, 13,017.42) | 0.270 |
| Normalised KIM-1/TIM-1 | 6311.89 (3813.21, 14,277.73) | 4071.48 (2636.63, 6833.3) | 0.002* |
| Normalised product ([IGFBP7]×[TIMP-2]) | 0.33 (0.15, 1.2) | 0.19 (0.08, 0.46) | 0.001* |
AKI (n = 44 urine samples from 24 patients); Non-AKI (n = 237 urine samples from 82 patients)
All values are median (inter-quartile range). Units are ng/mmolCr for all except normalised product which is [IGFBP7]×[TIMP-2], which is expressed as (ng/mmol Cr)2/1000
AKI—acute kidney injury, Cr—creatinine, IGFBP-7—Insulin-like growth factor-binding protein 7, IL-18—interleukin-18, KIM-1/TIM-1—Kidney injury molecule-1/T-cell immunoglobulin mucin domain-1, NGAL—Neutrophil gelatinase-associated lipocalin, TIMP-2—Tissue inhibitor of metalloproteinases 2
*p-value < 0.05 is significant
Receiver operating characteristic curves were plotted to assess the overall diagnostic performance of the urinary biomarkers (Table 3) in predicting AKI (Fig. 2). The normalised product was the best parameter in terms of AUROC, positive predictive value, specificity, and diagnostic accuracy (Table 4). The product was the best parameter regarding sensitivity and negative predictive value. Normalized TIM-1 also was a suitable parameter in terms of specificity.
Fig. 2.
Receiver operating characteristics curve analysis (ROC) showing diagnostic performance of urinary biomarkers (normalized) in predicting AKI (n = 281); a = Interleukin-18 (IL-18); b = Neutrophil gelatinase-associated lipocalin (NGAL), c = Kidney injury molecule-1/T-cell immunoglobulin mucin domain-1 (KIM-1/TIM-1), d = normalised product of Insulin-like growth factor-binding protein 7 (IGFBP-7) and Tissue inhibitor of metalloproteinases 2 (TIMP-2); e = combined ROC curve for the parameters mentioned in a-d and the product of non-normalised IGFBP-7 and TIMP-2
Table 4.
Performance of study parameters for predicting AKI
| Variable | AUROC | Sensitivity | Specificity | PPV | NPV | Diagnostic accuracy | p-value |
|---|---|---|---|---|---|---|---|
| Normalised IL-18 (Cutoff: 24.936 ng/mmol Cr) | 0.653 | 47.7% (32–63) | 82.3% (77–87) | 33.3% (22–46) | 89.4% (85–93) | 76.9% (71–82) | < 0.001 |
| Normalised NGAL (Cutoff: 8017.74 ng/mmol Cr) | 0.552 | 52.3% (37–68) | 64.6% (58–71) | 21.5% (14–30) | 87.9% (82–92) | 62.6% (57–68) | 0.035* |
| Normalised KIM-1/TIM-1 (Cutoff: 12,372.766 ng/mmol Cr) | 0.646 | 34.1% (20–50) | 91.1% (87–94) | 41.7% (26–59) | 88.2% (83–92) | 82.2% (77–86) | < 0.001* |
| Product [Cutoff: 0.301 (ng/ml)2/1000)] | 0.579 | 84.1% (70–93) | 31.6% (26–38) | 18.6% (13–25) | 91.5% (83–96) | 39.9% (34–46) | 0.035* |
| Normalised product [Cutoff: 0.87 (ng/ml)2 /mmol Cr2 1000] | 0.663 | 38.6% (24–55) | 91.1% (87–94) | 44.7% (29–62) | 88.9% (84–93) | 82.9% (78–87) | < 0.001* |
Cut-off has been described by Receiver operating characteristic (ROC) analysis
AUROC—Area under ROC curve, Cr—creatinine, CI—Confidence interval, IGFBP-7—Insulin-like growth factor-binding protein 7, IL-18—interleukin-18, KIM-1/TIM-1—Kidney injury molecule-1/T cell immunoglobulin mucin domain-1, NPV—Negative predictive value, NGAL—Neutrophil gelatinase-associated lipocalin, PPV—Positive predictive value, TIMP-2—Tissue inhibitor of metalloproteinases 2
*p-value < 0.05 is significant
When comparing the values of the first urine samples only, time 0, i.e., collected immediately after admission to the ICU (Table 5), it was found that the normalised values of all “injury” biomarkers i.e., KIM-1, NGAL, IL-18 were significantly higher in the subset that subsequently developed AKI. The value of these biomarkers increased over the next 48 h as reported in averaged sample results in Table 3.
Table 5.
Urinary biomarkers (immediate post ICU admission i.e., time 0) in AKI and non-AKI group
| Biomarkers | Total (n = 106) | AKI (n = 24) | Non-AKI (n = 82) | p-value |
|---|---|---|---|---|
| Spot urine creatinine (mmol/l) | 3.02 (1.92, 4.80) | 1.99 (1.41, 3.71) | 3.20 (2.35, 4.97) | 0.006* |
| Normalised IL-18 (ng/mmol Cr) | 9.47 (5.20, 25.98) | 15.81 (7.77, 41.49) | 7.99 (4.82, 18.03) | 0.021* |
| Normalised NGAL (ng/mmol Cr) | 4676.78 (1834.77, 9795.57) | 7854.12 (4673.56, 15,535.70) | 3336.55 (1686.29, 8996.45) | 0.006* |
| Normalised KIM-1/TIM-1 (ng/mmol Cr) | 3871.18 (2760.62, 7003.69) | 5436.85 (3100.73, 15,304.79) | 3773.76 (2699.88, 6294.13) | 0.019* |
| Normalised product [IGFBP-7]×[TIMP-2] expressed as (ng/mmol Cr)2/1000 | 635.73 (321.49, 1374.52) | 641.31 (286.82, 1803.18) | 635.73 (321.49,1347.16) | 0.946 |
All parameters are expressed as median (inter-quartile range)
Cr—creatinine, IGFBP-7—Insulin-like growth factor-binding protein 7, IL-18—interleukin-18, KIM-1/TIM-1—Kidney injury molecule-1/T-cell immunoglobulin mucin domain-1, NGAL—Neutrophil gelatinase-associated lipocalin, TIMP-2—Tissue inhibitor of metalloproteinases 2
*p-value < 0.05 is significant
The value of product of cell cycle arrest pre-injury biomarkers IGFBP7 and TIMP-2 was on the contrary highest immediately after ICU admission (t = 0) and was also comparable in both groups. However, averaged values over the next 48 h were significantly lower in the non AKI group (Table 3).
Discussion
Urinary concentration of both “pre-injury” and “injury” biomarkers were significantly higher in AKI group when compared with the non AKI group in critically ill COVID-19 patients. Best parameter in terms of AUROC, positive predictive value, specificity, diagnostic accuracy was the normalised product of IGFBP7 and TIMP-2; pre-injury cell cycle arrest biomarkers.
Previous studies report that values of IGFBP7 × TIMP-2 ≥ 0.2 and > 0.3 (ng/ml)2/1000 are a risk factor for subclinical and clinical AKI, respectively; time to AKI being shorter in individuals with higher values [2, 3, 6]. Performance of urinary biomarkers expressed as either absolute values and/or after normalization hinder comparison between trials and so in this study we report both values [7]. Normalisation with urinary creatinine minimizes variability secondary to hydration status and urine output so normalised urinary biomarker concentration is recommended for early detection of incipient AKI but in established AKI, absolute values can be used. In our study sensitivity was 84.1% and negative predictive value was 91.5% for detecting AKI with a cut off value of cell cycle arrest biomarkers ≥ 0.3 (ng/ml)2/1000.
We report a significantly higher requirement of inotropic support in AKI group. Ischemic ATN is the most common precipitating factor for AKI in critically ill adults [1]. In this study normalised values of NGAL were higher in AKI group but difference was not statistically significant. Previous study also report comparable NGAL concentrations in pre-renal AKI, which is considered a milder form of injury [8]. NGAL is specific to intrinsic AKI, and discerns AKI from pre-renal azotemia and CKD [9]. Elevated NGAL though detectable in the urine 3 h after kidney injury, has a narrow predictive time window for AKI [3]. Cell cycle arrest biomarkers reflect tubular damage regardless of etiology and are thus preferred over biomarkers of cell injury.
Normalized values of IGFBP-7 × TIMP-2 at the time of ICU admission were comparable in this study. Thus, using “pre-injury” urinary biomarkers at admission for predicting AKI should not be encouraged during pandemics because patients are admitted at different stages of disease severity and post admission stabilisation decreases their levels and risk for AKI. The tubular function is retained during the initial stages of AKI. However, markers of “injury” if elevated at time of ICU admission are predictive of AKI and can thus be utilised.
Prospective study design and a comparative evaluation of both cell cycle arrest (pre-injury) and kidney injury biomarkers (NGAL, KIM-1/TIM-1, IL-18) are the strengths of our study. Previously published studies do not include all of them [10]. The limitations of this study are the single-centre design, small sample size, short duration of follow-up, and lack of reports of kidney biopsy. Data on fluid balance, diuretic use, medications like angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, statins, etc., was also not compared. The possibility of levels of TIM-1 being influenced by advanced age, diabetes, chronic inflammatory diseases, atherosclerotic disease, etc. cannot be ruled out. The results of this study mainly focus on pre-renal causes of AKI, and cut-offs may vary for other etiologies and subclinical AKI. Urinary biomarkers have been used to predict the survival of patients with severe COVID-19 disease, we have not performed a survival analysis in our study [11, 12]. COVID pandemic is over and further research in non-COVID etiologies is warranted.
To conclude, “pre-injury” urinary cell cycle arrest biomarkers (TIMP-2 and IGFBP-7) have a promising role in the identification of patients at risk of AKI. Single sample collections at time of ICU admissions are discouraged, serial sampling is recommended.
Acknowledgements
We want to acknowledge all resident doctors, nursing staff, technical support staff, and faculty from the Departments of Anaesthesia, Internal Medicine, Pulmonary Medicine, Cardiology, and Nephrology who worked in COVID ICUs.
Author Contributions
All authors made substantial contributions to conception and design, acquisition of data, and/or analysis and interpretation of data, took part in drafting the article or revising it critically for important intellectual content, gave final approval of the version to be published, and agree to be accountable for all aspects of the work.
Funding
A funding of Indian Rupees 831062 was received from the Medical Education and Research Cell of the Postgraduate Institute of Medical Education and Research, Chandigarh via sanction order no. IM/42/11–05-21–0046.
Declarations
Conflicts of Interests
Authors declare that there are no financial or non-financial competing interests directly of indirectly related to the work submitted for publication.
Ethical Statement
The studies involving human participants were reviewed and approved by Institute Ethics Committee of PGIMER. The patients/participants provided their written informed consent to participate in this study.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.Samra T, Ramachandran R, Kumar V, Ray A, Mahajan V, Ganesan R, et al. Biochemical predictors of acute kidney injury in critically ill COVID-19 patients. OMICS. 2022;26(12):650–9. 10.1089/omi.2022.0144. [DOI] [PubMed] [Google Scholar]
- 2.Casas-Aparicio G, Alvarado-de la Barrera C, Escamilla-Illescas D, León-Rodríguez I, Del Río-Estrada PM, González-Navarro M, et al. Longitudinal analysis of urinary cytokines and biomarkers in COVID-19 patients with subclinical acute kidney injury. Int J Mol Sci. 2022;23(23):15419. 10.3390/ijms232315419. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Casas-Aparicio G, Alvarado-de la Barrera C, Escamilla-Illescas D, León-Rodríguez I, Del Río-Estrada PM, Calderón-Dávila N, et al. Role of urinary kidney stress biomarkers for early recognition of subclinical acute kidney injury in critically ill COVID-19 patients. Biomolecules. 2022;12(2):275. 10.3390/biom12020275. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Greco M, De Rosa S, Boehm F, Spano S, Aceto R, Voza A, et al. Kinetics of the cell cycle arrest biomarkers (TIMP2 and IGFBP7) for the diagnosis of acute kidney injury in critically ill COVID-19 patients. Diagnostics (Basel). 2023;13(2):317. 10.3390/diagnostics13020317. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Khwaja A. KDIGO clinical practice guidelines for acute kidney injury. Nephron Clin Pract. 2012;120(4):c179–84. 10.1159/000339789. [DOI] [PubMed] [Google Scholar]
- 6.Husain-Syed F, Wilhelm J, Kassoumeh S, Birk HW, Herold S, Vadász I, et al. Acute kidney injury and urinary biomarkers in hospitalized patients with coronavirus disease-2019. Nephrol Dial Transplant. 2020;35(7):1271–4. 10.1093/ndt/gfaa162. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Ralib AM, Pickering JW, Shaw GM, Devarajan P, Edelstein CL, Bonventre JV, et al. Test characteristics of urinary biomarkers depend on quantitation method in acute kidney injury. J Am Soc Nephrol. 2012;23(2):322–33. 10.1681/ASN.2011040325. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Nejat M, Pickering JW, Devarajan P, Bonventre JV, Edelstein CL, Walker RJ, et al. Some biomarkers of acute kidney injury are increased in pre-renal acute injury. Kidney Int. 2012;81(12):1254–62. 10.1038/ki.2012.23. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Devarajan P. NGAL for the detection of acute kidney injury in the emergency room. Biomark Med. 2014;8(2):217–9. 10.2217/bmm.13.149. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Racovitan D, Hogeweg M, Doevelaar AA, Seidel M, Rohn B, Bettag S, et al. Urinary biomarkers to predict acute kidney damage and mortality in COVID-19. Clin Nephrol. 2023;99(4):161–71. 10.5414/CN110952. [DOI] [PubMed] [Google Scholar]
- 11.Xu K, Shang N, Levitman A, Corker A, Kudose S, Yaeh A, et al. Elevated neutrophil gelatinase-associated lipocalin is associated with the severity of kidney injury and poor prognosis of patients with COVID-19. Kidney Int Rep. 2021;6(12):2979–92. 10.1016/j.ekir.2021.09.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Bezerra GF, Meneses GC, Albuquerque PL, Lopes NC, Santos RS, da Silva JC, et al. Urinary tubular biomarkers as predictors of death in critically ill patients with COVID-19. Biomark Med. 2022;16(9):681–92. 10.2217/bmm-2021-0631. [DOI] [PMC free article] [PubMed] [Google Scholar]