A high proportion of critically ill patients with COVID-19 develop acute kidney injury (AKI) and die. The early recognition of subclinical AKI could contribute to AKI prevention. Therefore, this study was aimed at exploring the role of the urinary biomarkers NGAL and [TIMP-2] × [IGFBP7] for the early detection of AKI in this population. This prospective, longitudinal cohort study included critically ill COVID-19 patients without AKI at study entry. Urine samples were collected on admission to critical care areas for determination of NGAL and [TIMP-2] × [IGFBP7] concentrations. The demographic information, comorbidities, clinical, and laboratory data were recorded. The study outcomes were the development of AKI and mortality during hospitalization. Of the 51 individuals that were studied, 25 developed AKI during hospitalization (49%). Of those, 12 had persistent AKI (23.5%). The risk factors for AKI were male gender (HR = 7.57, 95% CI: 1.28–44.8; p = 0.026) and [TIMP-2] × [IGFBP7] ≥ 0.2 (ng/mL)2/1000 (HR = 7.23, 95% CI: 0.99–52.4; p = 0.050). Mortality during hospitalization was significantly higher in the group with AKI than in the group without AKI (p = 0.004). Persistent AKI was a risk factor for mortality (HR = 7.42, 95% CI: 1.04–53.04; p = 0.046). AKI was frequent in critically ill COVID-19 patients. The combination of [TIMP-2] × [IGFBP7] 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 detection of AKI in ritically ill COVID-19 patients remains to be explored in large clinical trials.

In hospitalized COVID-19 patients, disease progression leading to acute kidney injury (AKI) may be driven by immune dysregulation. We explored the role of urinary cytokines and their relationship with kidney stress biomarkers in COVID-19 patients before and after the development of AKI. Of 51 patients, 54.9% developed AKI. The principal component analysis indicated that in subclinical AKI, epidermal growth factor (EGF) and interferon (IFN)-α were associated with a lower risk of AKI, while interleukin-12 (IL-12) and macrophage inflammatory protein (MIP)-1β were associated with a higher risk of AKI. After the manifestation of AKI, EGF and IFN-α remained associated with a lower risk of AKI, while IL-1 receptor (IL-1R), granulocyte-colony stimulating factor (G-CSF), interferon-gamma-inducible protein 10 (IP-10) and IL-5 were associated with a higher risk of AKI. EGF had an inverse correlation with kidney stress biomarkers. Subclinical AKI was characterized by a significant up-regulation of kidney stress biomarkers and proinflammatory cytokines. The lack of EGF regenerative effects and IFN-α antiviral activity seemed crucial for renal disease progression. AKI involved a proinflammatory urinary cytokine storm.

Background: A high proportion of critically ill patients with COVID-19 develop acute kidney injury (AKI) and die. Early recognition of subclinical AKI could contribute to AKI prevention. Therefore, this study was aimed at exploring the role of the urinary biomarkers NGAL and [TIMP-2]•[IGFBP7] for early detection of AKI in this population. Methods: This prospective, longitudinal cohort study included critically ill COVID-19 patients without AKI at study entry. Urine samples were collected on admission to critical care areas for determination of NGAL and [TIMP-2]•[IGFBP7] concentrations. Demographic information, comorbidities, clinical and laboratory data were recorded. The study outcomes were development of AKI and mortality during hospitalization. Comparisons of individuals who developed AKI during hospitalization vs. those without AKI were made using chi-squared test for categorical variables and Mann-Whitney U for continuous variables. Urinary biomarkers and their cutoff values were selected based on the highest sensitivity, specificity and area under the receiver-operating characteristics curve with 95% confidence intervals for prediction of AKI. Selected biomarkers and cutoffs were used in the Kaplan-Meier survival analyses for the time to AKI. Logistic regression analysis was used to identify the association between relevant covariates with AKI and mortality. For all analyses, two-sided P values £0.05 were considered statistically significant.Results: Of the 51 individuals studied, 25 developed AKI during hospitalization (49%). The risk factors for AKI were male gender (HR=7.57, 95% CI: 1.28-44.8; p=0.026) and [TIMP-2]•[IGFBP7] ³ 0.2 (ng/ml)2/1000 (HR=7.23 , 95% CI: 0.99-52.4; p=0.050). Mortality during hospitalization was significantly higher in the group with AKI than in the group without AKI (p=0.004). Persistent AKI was a risk factor for mortality (HR=7.42, 95% CI: 1.04-53.04; p=0.046).Conclusions: The combination of [TIMP-2]•[IGFBP7], together with clinical information, were useful for identification of subclinical AKI in critically ill COVID-19 patients. The role of additional biomarkers and their possible combinations for detection of AKI in critically ill COVID-19 patients remains to be explored in large clinical trials.