Association of urinary mitochondrial DNA with COVID-19 related acute kidney injury, mitochondrial stress, and inflammation in renal transplant recipients

Introduction: ACE-receptors are profusely expressed in the renal cell, making it highly susceptible for severe acute respiratory syndrome corona virus-2 (SARS-CoV-2) infection. After entering the cells, the virus induces high levels of cytokines, chemokines, and inflammatory responses, resulting neutrophilic infiltration, activation, and profuse reactive oxygen species (ROS) formation, leading to cellular necrosis and acute tubular injury. Proximal convoluted tube cell are rich in mitochondria and susceptible for developing acute kidney injury (AKI) due to mitochondrial stress. Early detection of AKI may helpful in its management, limiting the severity, avoiding nephrotoxic medicines and modifying the drug dose depending on renal function. Therefore, in the current study, we have determined the utility of urinary mitochondrial DNA (umt-DNA) and neutrophil gelatinase-associated lipocalin (NGAL) in predicting COVID-19-associated acute kidney injury (AKI) and mitochondrial stress and demonstrated the inflammatory response of urinary mt-DNA. Method(s): Live-related RTRs(n=66), who acquired SARS-CoV-2 infection and were admitted to a COVID hospital were included and subclassified into AKI (N=19) with > 25% spike in serum creatinine level from the pre-COVID-19 serum creatinine level, and non-AKI (N=47) whose serum creatinine value remained stable similar to the baseline value, or a rise of < 25% of the baseline values of pre-COVID-19. A 50ml urine sample was collected and umt-DNA and N-GAL was determined by the RT-PCR and ELISA methods respectively. A 10ml blood sample from 10 healthy volunteers was also collected for PBMC isolation and inflammatory response demonstration. A 1x106 PBMC was stimulated for 24hrs. with 1microg/ml of urinary DNA or TLR9 agonist CpG oligodeoxynucleotide (5'-tcgtcgttttcggcgc:gcgccg-3') in duplicate. Unstimulated PBMCs served as control. The gene expression of IL-10, IL-6, MYD88 was analyzed by the RT-PCR and IL-6, IL-10 level in supernatants by the ELISA. Result(s): Both the urinary mitochondrial gene ND-1 and NGAL level was significantly higher in AKI group compared to non-AKI. The mean ND-1 gene Ct in AKI group was (19.44+/-2.58 a.u) compared to non-AKI (21.77+/-3.60;p=0.013). The normalized ND-1 gene Ct in AKI was (0.79+/-0.11 a.u) compared to non-AKI (0.89+0.14;P=0.007). The median urinary NGAL level in AKI group was (453.53;range, 320.22-725.02, 95% CI) ng/ml compared to non-AKI (212.78;range, 219.80-383.06, 95%CI;p=0.015). The median urine creatinine normalized uNGAL was 4.78 (0.58-70.39) ng/mg in AKI group compared to 11.26 ng/mg (0.41-329.71) in non-AKI group. The area under curve of ND-1 gene Ct was 0.725, normalized ND-1 Ct was 0.713 and uNGAL was 0.663 and normalized uNGAL was 0.667 for detecting the AKI and mitochondrial stress. The IL-10 gene expression was downregulated in umt-DNA treated PBMCs compared to control (-3.5+/-0.40vs1.02+/-0.02, p<0.001). IL-6 and Myd88 gene expression was upregulated. The culture supernatant IL-10 and IL-6 level in umt-DNA treatment PBMCs vs control was 10.65+/-2.02 vs 30.3+/-5.47, p=0.001;and 200.2+/-33.67 vs 47.6+/-12.83, p=0.001 pg/ml respectively. Conclusion(s): Urinary mt-DNA quantification can detect the Covid19 associated AKI and mitochondrial distress with higher sensitivity than uNGAL in RTRs. Urinary mt-DNA also induces a robust inflammatory response in PBMCs, which may exacerbate the Covid19 associated allograft injury. No conflict of interestCopyright © 2023

The Significance of Epidermal Growth Factor and Other Urinary Cytokines since Subclinical Aki in Patients with Covid-19

Introduction: 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 the kidney stress biomarkers neutrophil gelatinase-associated lipocalin (NGAL), tissue inhibitor of metalloproteinases-2 (TIMP-2) and insulin-like growth factor binding protein (IGFBP7) in COVID-19 patients without AKI at study entry. Method(s): Prospective, longitudinal cohort study included critically ill COVID-19 patients without AKI at the time they were enrolled to the study. Urine samples were collected on admission to critical care areas for determination of NGAL, [TIMP-2]*[IGFBP7] and cytokines concentrations with a second sample 5 days after the first urine sample. Demographic information, clinical and laboratory data were collected. Diagnosis and staging of AKI were based on KDIGO criteria using serum creatinine (sCr) levels and urine output. The urinary concentrations of TIMP-2 and IGFBP7 were determined ELISA in the same way NGAL. The concentrations of cytokines and chemokines in urine were measured with a Luminex 200 instrument. We performed descriptive statistics including means and standard deviations for normally distributed continuous variables;medians and interquartile ranges (IQR) for non-parametric distributions;and proportions for categorical variables. Logistic regression analysis was used to identify the association between relevant covariates with AKI. Principal component analysis (PCA) was performed to compress and simplify the size of the data set by keeping the most important information and analyzing the structure of the observations and the variables. Correlation of identified cytokines with kidney stress biomarkers was explored using the Spearman test. All statistical tests were two-sided, p values <0.05 were considered statistically significant. The analysis was conducted using R Studio 1.4.1717. Result(s): Of included 51 patients. Of those, 30 were males (58.8%);the median age was 53 years (IQR: 40-61);14 had systemic arterial hypertension (27.5%);16 had diabetes (31.4%);and 21 were obese (41.2%). 54.9% developed AKI. After adjusting for possible confounding variables, only EGF >4600 pg/ml remained associated with lower risk of AKI (OR=0.095, 95% CI: 0.01-0.81, p=0.031.In the PCA of day 1, Epidermal Growth Factor (EGF) and interferon (IFN)-alpha were associated with a lower risk of AKI (OR=0.24, 95% CI: 0.07-0.78, p=0.017), while Interleukin (IL)-12 and macrophage inflammatory protein (MIP)-1b were associated with a higher risk of AKI (OR=51.09, 95% CI: 2.12-1233, p=0.015). In the PCA of day 5, EGF and IFN-alpha remained associated with a lower risk of AKI (OR=0.09, 95% CI: 0.01-0.74;p=0.024), while IL-1 Receptor, granulocyte-colony stimulating factor (G-CSF), interferon-gamma-inducible protein 10 (IP-10) and IL-5 were associated with a higher risk of AKI (OR=7.7, 95% CI: 1.06-55.74, p=0.043). EGF had an inverse correlation with [TIMP2] x IGFBP7] (R-0.73, p=<0.001) and with NGAL (R= -0.63, p=0.002). Conclusion(s): Subclinical AKI was characterized by a significant up-regulation of NGAL, TIMP-2, IGFBP7 and proinflammatory cytokines. The lack of EGF regenerative effects and IFN-alpha antiviral activity seemed crucial for renal disease progression. AKI involved a proinflammatory urinary cytokine storm. No conflict of interestCopyright © 2023

Longitudinal Analysis of Urinary Cytokines and Biomarkers in COVID-19 Patients with Subclinical Acute Kidney Injury.

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)-1ß 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.

Features of the course of acute pyelonephritis in children with an experience of a new coronavirus infection (COVID-19)

Purpose. To determine the clinical and laboratory features of the course of acute pyelonephritis in children with a history of COVID-19 and to determine the risk of developing chronic kidney disease. Material and methods. The main cohort consisted of 36 patients with the debut of acute pyelonephritis, who had previously suffered COVID-19, the average age was 7,5 years. The comparison group included 47 patients with the onset of acute pyelonephritis and a normal level of IgG antibodies to SARS-CoV-2, the average age was 7,0 years. Laboratory tests included full blood count and urinalysis, blood biochemistry, procalcitonin, urine microalbumin, urine creatinine, Zimnitsky urine test, bacteriological urine test, glomerular filtration rate, coagulogram, and Lipocalin-2 associated with neutrophil gelatinase (NGAL) in the urine. Results. Patients with a history of COVID-19, at the onset of acute pyelonephritis, demonstrated a higher incidence of apostematous pyelonephritis, a higher degree of damage to the tubulointerstitial kidney tissue, a high frequency and a higher level of hematuria, proteinuria, hyperfiltration, hypostenuria, as well as a more significant increase in fibrinogen, CRP, procalcitonin, and uNGAL/Cr levels than children of the comparison group. It was shown that almost half of the children with acute pyelonephritis who had had COVID-19 retained urinary syndrome during examination 3–4 months after the onset of the disease. Conclusion. Thus, long-term effects of the SARS-CoV-2 virus on the renal parenchyma were confirmed, even in asymptomatic children. The high level of uNGAL/Cr in children with acute pyelonephritis who had had COVID-19, which was almost 8 times higher than in the comparison group, reflects more pronounced damage to the tubulointerstitial kidney tissue. The risk of developing chronic kidney disease in this group was 3,5 times higher.

URINARY BIOMARKERS TO PREDICT ACUTE KIDNEY DAMAGE AND MORTALITY IN COVID-19

BACKGROUND AND AIMS: Acute kidney injury (AK) is a frequent condition in patients hospitalized for COVID-19. There are only few reports on the use of urinary biomarkers in COVID-19 and no data comparing the prognostic use of individual biomarkers in the prediction of adverse outcome so far. METHOD: We performed a prospective monocentric study on the value of urinary biomarkers to predict the composite endpoint of a transfer to the intensive care unit (ICU), the need for renal replacement therapy (RRT), mechanical ventilation and in-hospital mortality. A total of 41 patients hospitalized for COVID-19 were enrolled in this study. Urine samples were obtained shortly after admission in order to assess neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule-1 (KIM-1), calprotectin and vanin-1. RESULTS: We identified calprotectin as a predictor of a severe course of the disease, requiring intensive care treatment (AUC 0.728, P = .016). Positive and negative predictive values were 78.6% and 76.9%, respectively, using a cut-off concentration of 127.8 ng/mL. NGAL tended to predict COVID-19 associated AKI without reaching statistical significance (AUC 0.669, P = .053). The best parameter in the prediction of in-hospital mortality was NGAL as well (AUC 0.674, P = .077). KIM-1 and vanin-1 did not reach significance for any of the investigated endpoints. CONCLUSION: While KIM-1 and vanin-1 did not provide prognostic clinical information in the context of COVID-19, this study shows that urinary calprotectin and NGAL concentrations are independent predictors of an adverse course of the disease. Calprotectin and NGAL may thereby constitute helpful adjuncts in the identification of patients at increased risk who may benefit from upcoming antiviral agents to SARS-CoV-2.

Serum neutrophil gelatinase-associated lipocalin concentrations are significantly associated with the severity of COVID-19 in pregnant patients.

OBJECTIVES: To evaluate serum neutrophil gelatinase-associated lipocalin (NGAL) concentrations of pregnant women complicated with coronavirus disease 2019 (COVID-19) and investigate its diagnostic value for the severity of COVID-19. METHODS: Of the 46 pregnant women with COVID-19 included in the study, we further classified these women into 2 subgroups: the non-severe COVID-19 group (n=25) and the severe COVID-19 group (n=21). RESULTS: Neutrophil gelatinase-associated lipocalin plasma concentrations were significantly higher in pregnant women complicated with severe COVID-19 (90 [53.1-207.7] ng/ml) compared to those from pregnant women with non-severe COVID-19 (51.8 [39.6-70.3] ng/ml) and healthy pregnant women (44.3 [32.2-54.1] ng/ml, p<0.001). Also, at a cutoff value of 72 ng/ml, NGAL predicted severe COVID-19 with a sensitivity rate of 57% and a specificity rate of 84%. Serum NGAL level (adjusted hazard ratio [aHR]=1.020, 95% confidence interval [CI]= [1.006-1.035], p=0.007), and D-dimer level (aHR=2.371, 95% CI= [1.085-5.181], p=0.030) were the variables that were revealed to be significantly associated with the disease severity. CONCLUSION: We demonstrated that NGAL was highly associated with COVID-19 severity. We consider that NGAL might be a useful biomarker to diagnose the disease severity in patients with COVID-19.

Emerging early diagnostic methods for acute kidney injury.

Many factors such as trauma and COVID-19 cause acute kidney injury (AKI). Late AKI have a very high incidence and mortality rate. Early diagnosis of AKI provides a critical therapeutic time window for AKI treatment to prevent progression to chronic renal failure. However, the current clinical detection based on creatinine and urine output isn't effective in diagnosing early AKI. In recent years, the early diagnosis of AKI has made great progress with the advancement of information technology, nanotechnology, and biomedicine. These emerging methods are mainly divided into two aspects: First, predicting AKI through models construct by machine learning; Second, early diagnosis of AKI through detection of newly-discovered early biomarkers. Currently, these methods have shown great potential and become an attractive tool for the early diagnosis of AKI. Therefore, it is very important to discuss and summarize these methods for the early diagnosis of AKI. In this review, we first systematically summarize the application of machine learning in AKI prediction algorithms and specific scenarios. In addition, we introduce the key role of early biomarkers in the progress of AKI, and then comprehensively summarize the application of emerging detection technologies for early AKI. Finally, we discuss current challenges and prospects of machine learning and biomarker detection. The review is expected to provide new insights for early diagnosis of AKI, and provided important inspiration for the design of early diagnosis of other major diseases.

Cystatin C and Neutrophil Gelatin-associated Lipocalin (NGAL) Can Predict Acute Kidney Injury and In-Hospital Mortality in COVID-19 Patients

Background: Prediction and early diagnosis of acute kidney injury (AKI) in critically ill Coronavirus disease 2019 (COVID-19) patients are of great importance. Therefore, using promising renal biomarkers such as cystatin C and neutrophil gelatinase-associated lipocalin (NGAL) to identify the risk of future AKI is crucial. Materials and Methods: A total of 89 adult patients with COVID-19 were included in this study. Serum cystatin C and NGAL concentration were assessed on intensive care unit (ICU) admission then repeated after 48 hours. Serum creatinine was followed for 7 days to report the development of AKI. Results: Among the COVID-19 patients, 28.1% developed AKI. Although admission serum creatinine was not significantly different between the AKI group and the non-AKI group (p=0.375), admission Cystatin C (p=0.018), and NGAL (p<0.001) were significantly different between both groups. After 48 hours, a change in Cystatin C level (p<0.001) but not NGAL (p=0.4) was a predictor for AKI. Logistic regression model including age (p=0.031), Cystatin C on 48 hrs (p=0.003) and NGAL on admission (p=0.015) could predict AKI in COVID-19 patients. Conclusion: Serum Cystatin C and NGAL in ICU could be used to predict AKI in COVID-19 patients. A logistic regression model including age, Cystatin C on 48hrs, and NGAL on admission might be a tool for individualized risk estimation of AKI in COVID-19 patients.

Role of Urinary Kidney Stress Biomarkers for Early Recognition of Subclinical Acute Kidney Injury in Critically Ill COVID-19 Patients.

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.

Development of a europium nanoparticles lateral flow immunoassay for NGAL detection in urine and diagnosis of acute kidney injury.

BACKGROUND: AKI is related to severe adverse outcomes and mortality with Coronavirus Disease 2019 (COVID-19) patients, that early diagnosed and intervened is imperative. Neutrophil gelatinase-associated lipocalin (NGAL) is one of the most promising biomarkers for detection of acute kidney injury (AKI), but current detection methods are inadequacy, so more rapid, convenient and accuracy methods are needed to detect NGAL for early diagnosis of AKI. Herein, we established a rapid, reliable and accuracy lateral flow immunoassay (LFIA) based on europium nanoparticles (EU-NPS) for the detection of NGAL in human urine specimens. METHODS: A double-antibody sandwich immunofluorescent assay using europium doped nanoparticles was employed and the NGAL monoclonal antibodies (MAbs) conjugate as labels were generated by optimizing electric fusion parameters. Eighty-three urine samples were used to evaluate the clinical application efficiency of this method. RESULTS: The quantitative detection range of NGAL in AKI was 1-3000 ng/mL, and the detection sensitization was 0.36 ng/mL. The coefficient of variation (CV) of intra-assay and inter-assay were 2.57-4.98 % and 4.11-7.83 %, respectively. Meanwhile, the correlation coefficient between europium nanoparticles-based lateral fluorescence immunoassays (EU-NPS-LFIA) and ARCHITECT analyzer was significant (R2 = 0.9829, n = 83, p < 0.01). CONCLUSIONS: Thus, a faster and easier operation quantitative assay of NGAL for AKI has been established, which is very important and meaningful to diagnose the early AKI, suggesting that the assay can provide an early warning of final outcome of disease.

Proteomic Profiles in Patients with Thrombosis Due to COVID-19 Are Distinct from Non-COVID-19 Thrombosis

BACKGROUND. COVID-19 is a prothrombotic disease, characterized by endotheliopathy, hypercoagulability, and thromboembolic complications. We hypothesized that the pathogenesis of thromboembolism associated with COVID-19 might differ from thromboembolism in patients without COVID-19. In this study, we sought to evaluate the proteomic signatures of plasma from patients with venous thromboembolism with and without COVID-19. METHODS. Between December 17, 2020 and February 25, 2021 blood was collected from 48 hospitalized patients. Of these 24 had a confirmed diagnosis of COVID-19 infection (COVID+) and radiologic confirmation of arterial or venous thromboembolism (TE+);17 had COVID-19 infection with absence of arterial thrombosis clinically and absence of venous thromboembolism on lower extremity Doppler ultrasound or chest CT angiography (COVID+/TE-), while 7 were arterial or venous thromboembolism in the absence of COVID-19 (COVID-/TE+). Blood was collected in sodium citrate tubes and centrifuged at 4000 rpm for 20 minutes, with resulting plasma supernatant used for protein profiling performed at Eve Technologies (Calgary, Alberta, Canada). Institutional Review Board approval was obtained for this study. Statistical analysis was performed using GraphPad Prism (v9.1, GraphPad Software, San Diego, CA) and R (v4, R Core Team). P values <0.05 were considered statistically significant. A heatmap was generated using Heatmapper (heatmapper.ca) to represent the concentrations of proteins. RESULTS. The median age was 63 years;overall 25 (52%) were men (13 [54%] among COVID+/TE+, 11 [65%] among COVID+/TE-, and 1 [14%] among COVID-/TE+). In COVID-19 patients who developed thromboembolic events, several proteins associated with inflammation, complement activation, and hemostasis were present at higher levels than in non-COVID-19 patients who developed thromboembolic events (Fig. 1). These included complement factors C2 and C5a, pentraxin-3 (PTX-3), lipocalin-2 (LCN2), resistin (RETN), platelet endothelial cell adhesion molecule-1 (Pecam1), serum amyloid A (SAA), and tissue factor (TF). The heatmap indicates relative protein levels detected in each subject (columns) for proteins (rows) that had statistically significant differences between groups (Fig. 2). Heatmap revealed relatively lower levels of all proteins in patients with thromboembolism without COVID-19 and relatively higher levels of proteins in patients with COVID-19, and especially in ICU patients with COVID-19 and thromboembolism. CONCLUSIONS. Thromboembolic complications in patients with COVID-19 are associated with increased levels of various proteins involved in complement activation and immunothrombotic cascades, compared to thrombotic events in the absence of COVID-19. Activation of the classical complement pathway as evidenced by a relative increase in complement factor C2 may lead to increased TF activation, reflecting more substantial endothelial damage in COVID-19 patients. Higher levels of Pecam1, SAA, LCN2, and RETN all point to increased endotheliopathy, inflammation, and tissue damage in COVID-19 compared to non-COVID-19 thrombosis. These findings may offer insights into novel therapeutic strategies to treat immunothrombotic complications of COVID-19. [Formula presented] Disclosures: No relevant conflicts of interest to declare.

Prognostic Significance of Urinary Biomarkers in Patients Hospitalized With COVID-19.

RATIONALE & OBJECTIVE: Acute kidney injury (AKI) is common in patients with coronavirus disease 2019 (COVID-19) and associated with poor outcomes. Urinary biomarkers have been associated with adverse kidney outcomes in other settings and may provide additional prognostic information in patients with COVID-19. We investigated the association between urinary biomarkers and adverse kidney outcomes among patients hospitalized with COVID-19. STUDY DESIGN: Prospective cohort study. SETTING & PARTICIPANTS: Patients hospitalized with COVID-19 (n=153) at 2 academic medical centers between April and June 2020. EXPOSURE: 19 urinary biomarkers of injury, inflammation, and repair. OUTCOME: Composite of KDIGO (Kidney Disease: Improving Global Outcomes) stage 3 AKI, requirement for dialysis, or death within 60 days of hospital admission. We also compared various kidney biomarker levels in the setting of COVID-19 versus other common AKI settings. ANALYTICAL APPROACH: Time-varying Cox proportional hazards regression to associate biomarker level with composite outcome. RESULTS: Out of 153 patients, 24 (15.7%) experienced the primary outcome. Twofold higher levels of neutrophil gelatinase-associated lipocalin (NGAL) (HR, 1.34 [95% CI, 1.14-1.57]), monocyte chemoattractant protein (MCP-1) (HR, 1.42 [95% CI, 1.09-1.84]), and kidney injury molecule 1 (KIM-1) (HR, 2.03 [95% CI, 1.38-2.99]) were associated with highest risk of sustaining primary composite outcome. Higher epidermal growth factor (EGF) levels were associated with a lower risk of the primary outcome (HR, 0.61 [95% CI, 0.47-0.79]). Individual biomarkers provided moderate discrimination and biomarker combinations improved discrimination for the primary outcome. The degree of kidney injury by biomarker level in COVID-19 was comparable to other settings of clinical AKI. There was evidence of subclinical AKI in COVID-19 patients based on elevated injury biomarker level in patients without clinical AKI defined by serum creatinine. LIMITATIONS: Small sample size with low number of composite outcome events. CONCLUSIONS: Urinary biomarkers are associated with adverse kidney outcomes in patients hospitalized with COVID-19 and may provide valuable information to monitor kidney disease progression and recovery.

Incorporation of Urinary Neutrophil Gelatinase-Associated Lipocalin and Computed Tomography Quantification to Predict Acute Kidney Injury and In-Hospital Death in COVID-19 Patients.

BACKGROUND: The prevalence of acute kidney injury (AKI) in COVID-19 patients is high, with poor prognosis. Early identification of COVID-19 patients who are at risk for AKI and may develop critical illness and death is of great importance. OBJECTIVE: The aim of this study was to develop and validate a prognostic model of AKI and in-hospital death in patients with COVID-19, incorporating the new tubular injury biomarker urinary neutrophil gelatinase-associated lipocalin (u-NGAL) and artificial intelligence (AI)-based chest computed tomography (CT) analysis. METHODS: A single-center cohort of patients with COVID-19 from Wuhan Leishenshan Hospital were included in this study. Demographic characteristics, laboratory findings, and AI-assisted chest CT imaging variables identified on hospital admission were screened using least absolute shrinkage and selection operator (LASSO) and logistic regression to develop a model for predicting the AKI risk. The accuracy of the AKI prediction model was measured using the concordance index (C-index), and the internal validity of the model was assessed by bootstrap resampling. A multivariate Cox regression model and Kaplan-Meier curves were analyzed for survival analysis in COVID-19 patients. RESULTS: One hundred seventy-four patients were included. The median (±SD) age of the patients was 63.59 ± 13.79 years, and 83 (47.7%) were men.u-NGAL, serum creatinine, serum uric acid, and CT ground-glass opacity (GGO) volume were independent predictors of AKI, and all were selected in the nomogram. The prediction model was validated by internal bootstrapping resampling, showing results similar to those obtained from the original samples (i.e., 0.958; 95% CI 0.9097-0.9864). The C-index for predicting AKI was 0.955 (95% CI 0.916-0.995). Multivariate Cox proportional hazards regression confirmed that a high u-NGAL level, an increased GGO volume, and lymphopenia are strong predictors of a poor prognosis and a high risk of in-hospital death. CONCLUSIONS: This model provides a useful individualized risk estimate of AKI in patients with COVID-19. Measurement of u-NGAL and AI-based chest CT quantification are worthy of application and may help clinicians to identify patients with a poor prognosis in COVID-19 at an early stage.