Role Of Urinary Kidney Stress Biomarkers For Early Recognition Of Subclinical Acute Kidney Injury in Critically Ill COVID-19 Patients Ⅲ

4. Discussion 

Upon hospital admission, a large fraction of patients had subclinical signs of kidney dysfunctions that did not yet constitute AKI. During the subsequent days, AKI became a common complication in our patients, affecting 49% during hospitalization. This frequency was similar to that which was observed in previous studies, reporting AKI in 50% of the patients with COVID-19 in the ICU [2]. We found that [TIMP-2] × [IGFBP7] ≥ 0.2 (ng/mL)2/1000 was a risk factor for AKI. In addition, the survival analysis indicated that the time to AKI was significantly shorter in individuals with higher [TIMP-2] × [IGFBP7]. To our knowledge, few studies have examined the performance of biomarkers for the prediction of AKI onset in critically ill patients with COVID-19. A small study reported that patients with COVID-19-associated AKI and high levels of [TIMP-2] × [IGFBP7] were more likely to progress to renal replacement therapy than those with AKI but with low [TIMP-2] × [IGFBP7] [16]. Our findings are in line with previous reports, describing elevated levels of [TIMP-2] × [IGFBP7] as predictors of adverse outcomes in various clinical conditions, e.g. death, dialysis, or progression to severe AKI in patients with septic shock [17]; AKI in patients after major surgery [18]; imminent risk of AKI in critically ill patients [7]; and AKI in platinum-treated patients at the ICU [19]. The mechanism that is proposed is that after initial damage, IGFBP7 and TIMP-2 are expressed in tubular cells. IGFBP7 directly increases the expression of p53 and p21, and TIMP-2 stimulates p27 expression, leading to transitory G1 cell cycle arrest, preventing the division of damaged cells [5]. Thus, since the G1 cell cycle arrest is a common response to tubular damage, these biomarkers may better reflect damage regardless of etiology. TIMP-2 is both expressed and secreted preferentially by cells of distal tubule origin, while IGFBP7 is equally expressed across tubule cell types yet preferentially secreted by cells of proximal tubule origin. In human kidney tissue, strong staining of IGFBP7 was observed in the luminal brush border region of a subset of proximal tubule cells, and TIMP-2 stained intracellularly in distal tubules [20]. AKI-induced urinary [TIMP-2] × [IGFBP7] has also been attributed to increased filtration, decreased tubule reabsorption, and proximal tubule cell urinary leakage of both molecules [21].

HOW LONG DOES IT TAKE FOR CISTANCHE TO WORK?

The combination of [TIMP-2] × [IGFBP7] had the best performance for AKI prediction at values above 0.2 (ng/mL)2/1000. This cutoff was based on the overall behavior of the biomarkers in the patients that were studied here. However, different cutoffs for these biomarkers have been reported in other studies, so specific groups of patients may require the identification of optimal cutoff values based on their respective values of AUC, sensitivity, specificity, PPV, NPV, and accuracy. The cutoff values may be affected by the severity of AKI. That is, higher cutoffs may be found in patients with AKI stages 2 and 3, and lower cutoffs may be found in patients with AKI stage 1 or subclinical AKI. Moreover, AKI is a complex syndrome involving a series of complex cellular and molecular pathways, and the different cutoffs may reflect mechanistic differences between the various etiologies of AKI [5]. The pathophysiologic mechanisms of AKI in COVID-19 are thought to be multifactorial including systemic immune and inflammatory responses that are induced by a viral infection, systemic tissue hypoxia, reduced renal perfusion, endothelial damage, and direct epithelial infection with SARS-CoV-2 [22].

In our cohort, the time to AKI was significantly shorter in individuals with NGAL ≥ 45 ng/mL than in those with <45 ng/mL, but NGAL was not a risk factor for AKI during hospitalization. The fact that the performance of NGAL was significantly better on day 7 than during the whole hospitalization period, suggests that NGAL has a narrow predictive time window for AKI, and that may explain why it was not a risk factor for AKI during the whole hospitalization. In addition, NGAL has proved to be less discriminating in the development of septic-associated or adult cardiac-surgery-associated AKI than in other types of AKI, possibly because neutrophils themselves may be a source of NGAL in the setting of systemic inflammation [23]. 

Contrary to our findings, a recent cohort study found that urinary NGAL > 150 ng/mL predicted the diagnosis, duration, and severity of AKI and acute tubular injury, as well as hospital stay, dialysis, shock, and death in patients with acute COVID-19 [24]. Contrasting results may be explained by the fact that some patients in that study probably had AKI when the urinary samples were collected, while we only included patients without AKI at the time of urine sample collection. Therefore, the median value of NGAL in the AKI group (50.2 ng/mL) and the selected cutoff (45 ng/mL), were far lower in our patients since they had subclinical AKI. In addition, it is unclear if a higher proportion of their patients had AKI stage 2 and stage 3, while most of our patients developed AKI stage 1 on subsequent days. This is relevant because that study also reported a correlation between urinary NGAL levels and AKI severity. In another recent study, NGAL was also found as an independent risk factor for AKI in patients with COVID-19, but that study also included some patients who already had AKI when the urine samples were collected [25]. Thus, we suggest that in patients with COVID-19, higher NGAL cutoff values seem to be useful in predicting AKI progression but not AKI onset. However, since the number of patients in our study was indeed small, we would not dismiss the possible independent predictive value of NGAL that perhaps could have been revealed by the addition of more patients. Regardless of the selected cutoff values, our findings are in line with a study that reported significantly higher NGAL levels in patients with COVID-19 without evidence of AKI on presentation who subsequently developed AKI stages 1 to 3 within seven days of admission, compared with those who did not develop AKI [26]. In contrast with our findings, the urinary NGAL, but not [TIMP-2] × [IGFBP7], independently predicted AKI in a cohort of decompensated cirrhotic patients, suggesting that different biomarkers should be used in different patient groups [27]. 

The survival analysis indicated that mortality was more frequent in patients who developed persistent AKI during hospitalization. The concept that time should also be considered in the description of AKI and not only the severity was demonstrated in a study reporting that the duration of AKI following surgery was independently associated with hospital mortality after adjusting for the severity of illness [28]. Transient AKI may reflect a temporary reduction in renal function without structural damage, whereas persistent AKI would reflect structural tubular damage [29]. Based on these observations, persistent AKI has become a relevant endpoint in subsequent studies and it has consistently been associated with mortality [30].

Since we studied patients with normal kidney function at a baseline, the conclusions of this study may not be applicable to patients with acute-on-chronic renal functional impairment. This is, unfortunately, the disadvantage of renal biomarkers, which provide excellent prediction on evolving AKI in patients with previously intact kidneys but are of limited value in patients with preexisting renal disease. The use of biomarkers has some limitations, and it should be considered that their value for the prediction of AKI is limited to patients who are critically ill. When used in patients who are low-risk, the false positive rate may increase. When used before an injurious exposure has occurred, the test will not forecast AKI. Similarly, the test might not remain positive for a long time after injury [3]. If positive results are obtained, the test should be interpreted along with other clinical factors and nephrology consultation should be considered. When used properly, biomarker-guided interventions are useful in AKI prevention. This was demonstrated in a clinical trial including high-risk patients, defined as urinary [TIMP-2] × [IGFBP7] > 0.3 undergoing cardiac surgery. In that study, the implementation of the KDIGO guidelines, consisting of the optimization of volume status and hemodynamics, avoidance of nephrotoxic drugs, and prevention of hyperglycemia, resulted in an absolute risk reduction of 16.6% in the incidence of AKI compared with the standard care [31].

An important limitation of our study was the small sample size. Another study limitation was that patients with incomplete clinical files or those who were transferred to other hospitals due to the scarcity of ICU beds were not included in the study, and this may represent a selection bias. Considering that the standardized definitions of AKI are based on sCr and urine output [32], then inaccessibility to nursing records that are restricted to COVID-19 areas represents an important study limitation because urine output was not used for the diagnosis of AKI, and sCr was not adjusted for fluid-balance. It deserves to be mentioned that both the groups had similar median values of baseline sCr, but we think that the differences between groups might be explained by the fact that in the AKI group, the sCr values were more dispersed, interquartile ranges were wider, and individuals were older. The patients with AKI had higher urea levels, but we could not exclude volume depletion in this group. The lack of pre-hospital baseline sCr measurements was also a study limitation because baseline sCr values were an estimation. One additional study limitation was that our study was conducted at a national referral center for respiratory diseases receiving disproportionately more patients with severe COVID-19, and this represents a potential source of referral bias.

5. Conclusions

Elevated values of urinary [TIMP-2] × [IGFBP7] were risk factors for AKI and persistent AKI was a risk factor for mortality. These biomarkers, together with clinical information, were useful for the identification of subclinical AKI in critically ill COVID-19 patients. The role of additional biomarkers and their possible combinations for the early detection of AKI in critically ill COVID-19 patients remains to be explored in large clinical trials. Preventable causes of AKI should be reduced.

Author Contributions: Conceptualization, G.C.-A.; methodology, L.F.-H.; software, R.O.-O.; validation, M.C.-L.; formal analysis, I.L.-R.; investigation, N.C.-D.; resources, A.P.-P.; data curation, M.G.-N.; writing-original draft preparation, C.A.-d.l.B. and P.F.-C.; writing-review and editing, C.A.-d.l.B.; visualization, D.E.-I., Y.L.-V., E.P.-I., C.A.-d.l.B. and S.Á.-R.; supervision, P.M.D.R.-E.; project administration, G.C.-A.; funding acquisition, S.Á.-R. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by funds from the Mexican Government (Programa Presupuestal P016, Anexo 13 del Decreto del Presupuesto de Egresos de la Federación). Institutional Review Board Statement: The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of the National Institute of Respiratory Diseases (Approval No C26-20; 21 May 2020). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: All data generated and analyzed during this study were included in a Supplementary Materials File (S1 File Raw Data). Conflicts of Interest: The authors declare that there is no conflict of interest

References

1. García, L.F. Immune Response, Inflammation, and the Clinical Spectrum of COVID-19. Front. Immunol. 2020, 11, 1441. [CrossRef] [PubMed] 

2. Nadim, M.K.; Forni, L.G.; Mehta, R.L.; Connor, M.J., Jr.; Liu, K.D.; Ostermann, M.; Rimmelé, T.; Zarbock, A.; Bell, S.; Bihorac, A.; et al. COVID-19-associated acute kidney injury: Consensus report of the 25th Acute Disease Quality Initiative (ADQI) Workgroup. Nat. Rev. Nephrol. 2020, 16, 747–764. [CrossRef] [PubMed] 

3. Ronco, C.; Bellomo, R.; Kellum, J.A. Acute kidney injury. Lancet 2019, 394, 1949–1964. [CrossRef] 

4. MacLeod, A. NCEPOD report on acute kidney injury-must do better. Lancet 2009, 374, 1405–1406. [CrossRef] 

5. Haase, M.; Kellum, J.A.; Ronco, C. Subclinical AKI: An emerging syndrome with important consequences. Nat Rev Nephrol. 2012, 8, 735–739. [CrossRef]

6. Kashani, K.; Al-Khafaji, A.; Ardiles, T.; Artigas, A.; Bagshaw, S.M.; Bell, M.; Bihorac, A.; Birkhahn, R.; Cely, C.M.; Chawla, L.S.; et al. Discovery and validation of cell cycle arrest biomarkers in human acute kidney injury. Crit. Care. 2013, 17, R25. [CrossRef] 

7. Hoste, E.A.; McCullough, P.A.; Kashani, K.; Chawla, L.S.; Joannidis, M.; Shaw, A.D.; Feldkamp, T.; Uettwiller-Geiger, D.L.; McCarthy, P.; Shi, J.; et al. Derivation and validation of cutoffs for clinical use of cell cycle arrest biomarkers. Nephrol. Dial. Transplant. 2014, 29, 2054–2061. [CrossRef]

8. Bihorac, A.; Chawla, L.S.; Shaw, A.D.; Al-Khafaji, A.; Davison, D.L.; Demuth, G.E.; Fitzgerald, R.; Gong, M.N.; Graham, D.D.; Gunnerson, K.; et al. Validation of cell-cycle arrest biomarkers for acute kidney injury using clinical adjudication. Am. J. Respir. Crit. Care. Med. 2014, 189, 932–939. [CrossRef] 

9. Mishra, J.; Ma, Q.; Prada, A.; Mitsnefes, M.; Zahedi, K.; Barasch, J.; Devarajan, P. Identification of neutrophil gelatinase-associated lipocalin as a novel early urinary biomarker for ischemic renal injury. J. Am. Soc. Nephrol. 2003, 14, 2534–2543. [CrossRef] 

10. Levey, A.S.; Stevens, L.A.; Schmid, C.H.; Zhang, Y.L.; Castro, A.F., 3rd; Feldman, H.I.; Kusek, J.W.; Eggers, P.; Van Lente, F.; Greene, T.; et al. A new equation to estimate glomerular filtration rate. Ann. Intern. Med. 2009, 150, 604–612. [CrossRef] 

11. World Health Organization. Clinical Management of Severe Acute Respiratory Infection When Novel Coronavirus (nCoV) Infection Is Suspected: Interim Guidance. Published 13 March 2020. Available online: https://pesquisa.bvsalud.org/portal/ resource/pt/biblio-1053426 (accessed on 12 August 2021). 

12. Moore, P.K.; Hsu, R.K.; Liu, K.D. Management of Acute Kidney Injury: Core Curriculum 2018. Am. J. Kidney. Dis. 2018, 72, 136–148; [CrossRef] [PubMed]