Early prediction of COVID-19-associated acute kidney injury: Are serum NGAL and serum Cystatin C levels better than serum creatinine?

COVID-19 Infection in Autosomal Dominant Polycystic Kidney Disease and Chronic Kidney Disease Patients: Progression of Kidney Disease

INTRODUCTION

the COVID-19 pandemic has brought to light the intricate interplay between viral infections and preexisting health conditions. In the field of kidney diseases, patients with Autosomal Dominant Polycystic Kidney Disease (ADPKD) and Chronic Kidney Disease (CKD) face unique challenges when exposed to the SARS-CoV-2 virus. This study aims to evaluate whether SARS-CoV-2 virus infection impacts renal function differently in patients suffering from ADPKD and CKD when compared to patients suffering only from CKD.

MATERIALS AND METHODS

clinical data from 103 patients were collected and retrospectively analyzed. We compared the renal function of ADPKD and CKD patients at two distinct time points: before COVID-19 infection (T0) and 1 year after the infection (T1). We studied also a subpopulation of 37 patients with an estimated glomerular filtration rate (eGFR) < 60 mL/min and affected by ADPKD and CKD.

RESULTS

clinical data were obtained from 59 (57.3%) ADPKD patients and 44 (42.7%) CKD patients. At T1, ADPKD patients had significantly higher serum creatinine levels compared to CKD patients, and a significantly lower eGFR was observed only in ADPKD patients with eGFR < 60 mL/min compared to CKD patients (p < 0.01, p < 0.05; respectively). Following COVID-19 infection, ADPKD-CKD patients exhibited significantly higher variation in both median serum creatinine (p < 0.001) and median eGFR (p < 0.001) compared to CKD patients.

CONCLUSION

the interplay between COVID-19 and kidney disease is complex. In CKD patients, the relationship between COVID-19 and kidney disease progression is more established, while limited studies exist on the specific impact of COVID-19 on ADPKD patients. Current evidence does not suggest that ADPKD patients are at a higher risk of SARS-CoV-2 infection; however, in our study we showed a significant worsening of the renal function among ADPKD patients, particularly those with an eGFR < 60 mL/min, in comparison to patients with only CKD after a one-year follow-up from COVID-19 infection.

Organ-Dysfunction Markers in Mild-to-Moderate COVID-19 Convalescents

Background: A coronavirus disease 2019 (COVID-19) outbreak led to a worldwide pandemic. COVID-19 not only caused acute symptoms during the severe phase of the disease, but also induced long-term side effects on the functioning of many organs and systems. Symptoms that were associated with the disease and present at least 3 months after recovery were named long COVID. The aim of this study was to assess if mild-to-moderate COVID-19 may lead to the dysfunction of respiratory, cardiovascular, neural, and renal systems in healthy blood donors who recovered from the disease at least 6 months earlier. Methods: Here, we examined 294 adults among volunteer blood donors divided into convalescents (n = 215) and healthy controls (n = 79). Concentrations of soluble CD163, TGF beta, Lp-PLA2, NCAM-1, S100, NGAL, and creatinine were measured either by ELISA or automated methods. The probability value p < 0.05 was considered as statistically significant. Results: We found significant differences in Lp-PLA2, S100, and NCAM-1 between convalescents and never-infected subjects. Lp-PLA2 and NCAM-1 were lower, and S100 higher, in convalescents than in the control group. Conclusion: Mild-to-moderate COVID-19 convalescents are at a low risk of developing lung fibrosis or chronic kidney disease. However, they should regularly carry out their prophylaxis examinations for early detection of possible negative outcomes of COVID-19.

Development and validation of a machine learning model to predict the use of renal replacement therapy in 14,374 patients with COVID-19

PURPOSE

To develop a model to predict the use of renal replacement therapy (RRT) in COVID-19 patients.

MATERIALS AND METHODS

Retrospective analysis of multicenter cohort of intensive care unit (ICU) admissions of Brazil involving COVID-19 critically adult patients, requiring ventilatory support, admitted to 126 Brazilian ICUs, from February 2020 to December 2021 (development) and January to May 2022 (validation). No interventions were performed.

RESULTS

Eight machine learning models' classifications were evaluated. Models were developed using an 80/20 testing/train split ratio and cross-validation. Thirteen candidate predictors were selected using the Recursive Feature Elimination (RFE) algorithm. Discrimination and calibration were assessed. Temporal validation was performed using data from 2022. Of 14,374 COVID-19 patients with initial respiratory support, 1924 (13%) required RRT. RRT patients were older (65 [53-75] vs. 55 [42-68]), had more comorbidities (Charlson's Comorbidity Index 1.0 [0.00-2.00] vs 0.0 [0.00-1.00]), had higher severity (SAPS-3 median: 61 [51-74] vs 48 [41-58]), and had higher in-hospital mortality (71% vs 22%) compared to non-RRT. Risk factors for RRT, such as Creatinine, Glasgow Coma Scale, Urea, Invasive Mechanical Ventilation, Age, Chronic Kidney Disease, Platelets count, Vasopressors, Noninvasive Ventilation, Hypertension, Diabetes, modified frailty index (mFI) and Gender, were identified. The best discrimination and calibration were found in the Random Forest (AUC [95%CI]: 0.78 [0.75-0.81] and Brier's Score: 0.09 [95%CI: 0.08-0.10]). The final model (Random Forest) showed comparable performance in the temporal validation (AUC [95%CI]: 0.79 [0.75-0.84] and Brier's Score, 0.08 [95%CI: 0.08-0.1]).

CONCLUSIONS

An early ML model using easily available clinical and laboratory data accurately predicted the use of RRT in critically ill patients with COVID-19. Our study demonstrates that using ML techniques is feasible to provide early prediction of use of RRT in COVID-19 patients.

NEW PREDICTIVE BIOMARKERS FOR SCREENING COVID-19 PATIENTS WITH RHABDOMYOLYSIS IN COMBINATION WITH CYSTATIN C

ABSTRACT

Purpose: Cystatin C (CysC) has been linked to the prognosis of corona virus disease 2019 (COVID-19). The study aims to investigate a predictor correlated with CysC screening for poor prognosis in COVID-19 patients combined with skeletal muscle (SKM) impairment and rhabdomyolysis (RM). Methods: A single-center retrospective cohort analysis was carried out. Demographic information, clinical data, laboratory test results, and clinical outcome data were gathered and analyzed. Results: According to the inclusion and exclusion criteria, 382 patients were included in this study. The subjects were divided into three groups based on CysC tertiles. Multivariate analysis revealed that SaO 2 (hazard ratio [HR], 0.946; 95% confidence interval [CI], 0.906-0.987; P = 0.011), CysC (HR, 2.124; 95% CI, 1.223-3.689; P = 0.008), aspartate aminotransferase (AST) (HR, 1.009; 95% CI, 1.000-1.018; P = 0.041), and hypersensitive C-reactive protein (HR, 1.005; 95% CI, 1.000-1.010; P = 0.045) were significantly associated with survivals. The area under curve (AUC) in the model characterized by RM incidence was 0.819 (0.698-0.941), as shown by CysC receiver operating characteristic curves. LDH*CysC and AST*CysC had better predictive values than CysC and the best prediction for RM, with an AUC of 0.880 (0.796,0.964) for LDH*CysC ( P < 0.05, vs CysC) and 0.925 (0.878,0.972) for AST*CysC ( P < 0.05, vs CysC). Conclusion: CysC is an essential evaluation indicator for COVID-19 patients' prognosis. AST*CysC and LDH*CysC have superior predictive value to CysC for SKM, RM, and death, and optimal classification for RM.

Plasma neutrophil gelatinase-associated lipocalin independently predicts dialysis need and mortality in critical COVID-19

Neutrophil gelatinase-associated lipocalin (NGAL) is a novel kidney injury and inflammation biomarker. We investigated whether NGAL could be used to predict continuous renal replacement therapy (CRRT) and mortality in critical coronavirus disease 2019 (COVID-19). This prospective multicenter cohort study included adult COVID-19 patients in six intensive care units (ICUs) in Sweden between May 11, 2020 and May 10, 2021. Blood was sampled at admission, days two and seven in the ICU. The samples were batch analyzed for NGAL, creatinine, and cystatin c after the end of the study period. Initiation of CRRT and 90-day survival were used as dependent variables in regression models. Of 498 included patients, 494 were analyzed regarding CRRT and 399 were analyzed regarding survival. Seventy patients received CRRT and 154 patients did not survive past 90 days. NGAL, in combination with creatinine and cystatin c, predicted the subsequent initiation of CRRT with an area under the curve (AUC) of 0.95. For mortality, NGAL, in combination with age and sex, had an AUC of 0.83. In conclusion, NGAL is a valuable biomarker for predicting subsequent initiation of CRRT and 90-day mortality in critical COVID-19. NGAL should be considered when developing future clinical scoring systems.

IL-6 and SAA-Strong Predictors for the Outcome in COVID-19 CKD Patients

In this prospective study, we assessed biomarkers of inflammation (IL-6 and SAA) from the serum of 120 COVID-19 patients, of whom 70 had chronic kidney disease. All the samples were taken at emergency-department (ED) admission. Our goal was to relate the biomarkers to the results of death and acute kidney injury. All the patients underwent chest computer tomography to estimate the severity score (0-5), which was performed at hospital admission. Finally, biomarkers were also evaluated in a healthy control group and in non-COVID-19-CKD patients. IL-6 and SAA were statistically different between the subgroups, i.e., they were significantly increased in patients with COVID-19. Both of the biomarkers (IL-6 and SAA) were independently associated with mortality, AKI and a higher grade of pathological changes in the lung's parenchyma. Both high baseline levels of IL-6 and SAA on hospital admission were highly correlated with a later ventilatory requirement and mortality, independent of hospital stay. Mortality was found to be significantly higher when the chest CT severity score was 3-4, compared with a severity score of 0-2 (p < 0.0001). Conclusions: at the admission stage, IL-6 and SAA are useful markers for COVID-19 patients with CKD.

Acute kidney injury in critical COVID-19 patients: usefulness of urinary biomarkers and kidney proximal tubulopathy

Tubular injury is the main cause of acute kidney injury (AKI) in critically ill COVID-19 patients. Proximal tubular dysfunction (PTD) and changes in urinary biomarkers, such as NGAL, TIMP-2, and IGFBP7 product ([TIMP-2]•[IGFBP7]), could precede AKI. We conducted a prospective cohort study from 2020/03/09 to 2020/05/03, which consecutively included all COVID-19 patients who had at least one urinalysis, to assess the incidence of PTD and AKI, and the effectiveness of PTD, NGAL, and [TIMP-2]•[IGFBP7] in AKI and persistent AKI prediction using the area under the receiver operating characteristic curves (AUCs), Kaplan-Meier methodology (log-rank tests), and Cox models. Among the 60 patients admitted to the ICU with proven COVID-19 (median age: 63-year-old (interquartile range: IQR, 55-74), 45 males (75%), median simplified acute physiology score (SAPS) II: 34 (IQR, 22-47) and median BMI: 25.7 kg/m2 (IQR, 23.3-30.8)) analyzed, PTD was diagnosed in 29 patients (48%), AKI in 33 (55%) and persistent AKI in 20 (33%). Urinary NGAL had the highest AUC for AKI prediction: 0.635 (95%CI: 0.491-0.779) and persistent AKI prediction: 0.681 (95%CI: 0.535-0.826), as compared to PTD and [TIMP-2]•[IGFBP7] (AUCs <0.6). AKI was independently associated with higher SAPSII (HR = 1.04, 95%CI: 1.01-1.06, p = 0.005) and BMI (HR = 1.07, 95%CI: 1.00-1.14, p = 0.04) and persistent AKI with higher SAPSII (HR = 1.03, 95%CI: 1.00-1.06, p = 0.048) and nephrotoxic drug use (HR = 3.88, 95%CI: 1.20-12.5, p = 0.02). In conclusion, in critically ill COVID-19 patients, the incidence of PTD and AKI was relatively high. NGAL was the best urinary biomarker for predicting AKI, but only clinical severity was independently associated with its occurrence.

Patterns of Circulating Cytokines and Vascular Markers' Response in the Presence of COVID-19 in Kidney Transplant Recipients Compared with Non-Transplanted Patients

COVID-19's severity has been associated with a possible imbalance in the cross-regulation of cytokines and vascular mediators. Since the beginning of the pandemic, kidney transplant recipients (KTRs) have been identified as patients of high vulnerability to more severe diseases. Thus, aiming to describe the patterns of cytokines and vascular mediators and to trace patients' differences according to their KTR status, this prospective study enrolled 67 COVID-19 patients (20 KTRs) and 29 non-COVID-19 controls before vaccination. A panel comprising 17 circulating cytokines and vascular mediators was run on samples collected at different time points. The cytokine and mediator patterns were investigated via principal component analysis (PCA) and correlation-based network (CBN). In both groups, compared to their respective controls, COVID-19 was associated with higher levels of cytokines and vascular mediators. Differentiating between the KTRs and non-KTRs, the number of correlations was much higher in the non-KTRs (44 vs. 14), and the node analysis showed the highest interactions of NGAL and sVCAM-1 in the non-KTRs and KTRs (9 vs. 4), respectively. In the PCA, while the non-KTRs with COVID-19 were differentiated from their controls in their IL-10, IFN-α, and TNF-α, this pattern was marked in the NGAL, sVCAM-1, and IL-8 of the KTRs.

Comparing diagnostic accuracy of biomarkers for acute kidney injury after major surgery: A PRISMA systematic review and network meta-analysis

BACKGROUND

The timely identification of patients at risk of acute kidney injury (AKI), along with early prevention, real-time monitoring, and prompt intervention, plays a crucial role in enhancing patient prognosis after major surgery.

METHODS

We conducted a comprehensive search across multiple databases, including Web of Science, EMBASE, MEDLINE, China National Knowledge Infrastructure, and Cochrane Library. Each study's risk of bias was independently evaluated as low, moderate, or high, utilizing criteria adapted from Quality Assessment of Diagnostic Accuracy Studies 2. The analysis was performed using STATA V.17.0 and R software V.3.4.1. Diagnostic tests were ranked based on the dominance index. We performed meta-analyses to calculate odds ratios (ORs) and 95% confidence intervals (CIs) individually. We then carried out a network meta-analysis to compare the performances of these biomarkers.

RESULTS

Fifteen studies were included in this analysis. The meta-analysis findings revealed that among all the biomarkers assessed, serum cystatin C (s-CysC) (hierarchical summary receiver operating characteristic curve [HSROC] 82%, 95% CI 0.78-0.85) exhibited the highest HSROC value. The network meta-analysis demonstrated that urinary kidney injury molecule-1 (u-KIM-1) and s-CysC displayed relatively higher sensitivity and specificity, respectively. In subgroup analyses, u-KIM-1 in the urine output (OU) group (OR 303.75, 95% CI 3.39-1844.88), s-CysC in the non-OU group (OR 10.31, 95% CI 3.09-26.2), interleukin-18 in the noncardiac surgery group (OR 46.20, 95% CI 0.48-307.68), s-CysC in the cardiac group (OR 12.42, 95% CI 2.9-35.86), u-KIM-1 in the retrospective group (OR 243.00, 95% CI 1.73-1582.11), and s-CysC in the prospective group (OR 8.35, 95% CI 2.34-21.15) had the best diagnostic accuracy. However, it is important to note that existing publication bias may reduce the reliability of the above-mentioned results.

CONCLUSION

The biomarker of s-CysC has the highest HSROC value to predicting acute kidney injury after major surgery in meta-analysis and relatively higher specificity in network meta-analyses. u-KIM-1 exhibited relatively higher sensitivity, with best diagnostic accuracy in the OU and retrospective group in the subgroup analysis.

Reviewing methods of deep learning for diagnosing COVID-19, its variants and synergistic medicine combinations

The COVID-19 pandemic has necessitated the development of reliable diagnostic methods for accurately detecting the novel coronavirus and its variants. Deep learning (DL) techniques have shown promising potential as screening tools for COVID-19 detection. In this study, we explore the realistic development of DL-driven COVID-19 detection methods and focus on the fully automatic framework using available resources, which can effectively investigate various coronavirus variants through modalities. We conducted an exploration and comparison of several diagnostic techniques that are widely used and globally validated for the detection of COVID-19. Furthermore, we explore review-based studies that provide detailed information on synergistic medicine combinations for the treatment of COVID-19. We recommend DL methods that effectively reduce time, cost, and complexity, providing valuable guidance for utilizing available synergistic combinations in clinical and research settings. This study also highlights the implication of innovative diagnostic technical and instrumental strategies, exploring public datasets, and investigating synergistic medicines using optimised DL rules. By summarizing these findings, we aim to assist future researchers in their endeavours by providing a comprehensive overview of the implication of DL techniques in COVID-19 detection and treatment. Integrating DL methods with various diagnostic approaches holds great promise in improving the accuracy and efficiency of COVID-19 diagnostics, thus contributing to effective control and management of the ongoing pandemic.

Evaluation of Plasma Biomarkers to Predict Major Adverse Kidney Events in Hospitalized Patients With COVID-19

RATIONALE & OBJECTIVE

Patients hospitalized with COVID-19 are at increased risk for major adverse kidney events (MAKE). We sought to identify plasma biomarkers predictive of MAKE in patients hospitalized with COVID-19.

STUDY DESIGN

Prospective cohort study.

SETTING & PARTICIPANTS

A total of 576 patients hospitalized with COVID-19 between March 2020 and January 2021 across 3 academic medical centers.

EXPOSURE

Twenty-six plasma biomarkers of injury, inflammation, and repair from first available blood samples collected during hospitalization.

OUTCOME

MAKE, defined as KDIGO stage 3 acute kidney injury (AKI), dialysis-requiring AKI, or mortality up to 60 days.

ANALYTICAL APPROACH

Cox proportional hazards regression to associate biomarker level with MAKE. We additionally applied the least absolute shrinkage and selection operator (LASSO) and random forest regression for prediction modeling and estimated model discrimination with time-varying C index.

RESULTS

The median length of stay for COVID-19 hospitalization was 9 (IQR, 5-16) days. In total, 95 patients (16%) experienced MAKE. Each 1 SD increase in soluble tumor necrosis factor receptor 1 (sTNFR1) and sTNFR2 was significantly associated with an increased risk of MAKE (adjusted HR [AHR], 2.30 [95% CI, 1.86-2.85], and AHR, 2.26 [95% CI, 1.73-2.95], respectively). The C index of sTNFR1 alone was 0.80 (95% CI, 0.78-0.84), and the C index of sTNFR2 was 0.81 (95% CI, 0.77-0.84). LASSO and random forest regression modeling using all biomarkers yielded C indexes of 0.86 (95% CI, 0.83-0.89) and 0.84 (95% CI, 0.78-0.91), respectively.

LIMITATIONS

No control group of hospitalized patients without COVID-19.

CONCLUSIONS

We found that sTNFR1 and sTNFR2 are independently associated with MAKE in patients hospitalized with COVID-19 and can both also serve as predictors for adverse kidney outcomes.

PLAIN-LANGUAGE SUMMARY

Patients hospitalized with COVID-19 are at increased risk for long-term adverse health outcomes, but not all patients suffer long-term kidney dysfunction. Identification of patients with COVID-19 who are at high risk for adverse kidney events may have important implications in terms of nephrology follow-up and patient counseling. In this study, we found that the plasma biomarkers soluble tumor necrosis factor receptor 1 (sTNFR1) and sTNFR2 measured in hospitalized patients with COVID-19 were associated with a greater risk of adverse kidney outcomes. Along with clinical variables previously shown to predict adverse kidney events in patients with COVID-19, both sTNFR1 and sTNFR2 are also strong predictors of adverse kidney outcomes.

Iron and iron-related proteins in COVID-19

COVID-19 can cause detrimental effects on health. Vaccines have helped in reducing disease severity and transmission but their long-term effects on health and effectiveness against future viral variants remain unknown. COVID-19 pathogenesis involves alteration in iron homeostasis. Thus, a contextual understanding of iron-related parameters would be very valuable for disease prognosis and therapeutics.Accordingly, we reviewed the status of iron and iron-related proteins in COVID-19. Iron-associated alterations in COVID-19 reported hitherto include anemia of inflammation, low levels of serum iron (hypoferremia), transferrin and transferrin saturation, and high levels of serum ferritin (hyperferritinemia), hepcidin, lipocalin-2, catalytic iron, and soluble transferrin receptor (in ICU patients). Hemoglobin levels can be low or normal, and compromised hemoglobin function has been proposed. Membrane-bound transferrin receptor may facilitate viral entry, so it acts as a potential target for antiviral therapy. Lactoferrin can provide natural defense by preventing viral entry and/or inhibiting viral replication. Serum iron and ferritin levels can predict COVID-19-related hospitalization, severity, and mortality. Serum hepcidin and ferritin/transferrin ratio can predict COVID-19 severity. Here, serum levels of these iron-related parameters are provided, caveats of iron chelation for therapy are discussed and the interplay of these iron-related parameters in COVID-19 is explained.This synopsis is crucial as it clearly presents the iron picture of COVID-19. The information may assist in disease prognosis and/or in formulating iron-related adjunctive strategies that can help reduce infection/inflammation and better manage COVID-19 caused by future variants. Indeed, the current picture will augment as more is revealed about these iron-related parameters in COVID-19.

Role of increased neutrophil extracellular trap formation on acute kidney injury in COVID-19 patients

Background

A strong association between elevated neutrophil extracellular trap (NET) levels and poor clinical outcomes in patients with coronavirus infection 2019 (COVID-19) has been reported. However, while acute kidney injury (AKI) is a common complication of COVID-19, the role of NETs in COVID-19-associated AKI is unclear. We investigated the association between elevated NETs and AKI and the prognostic role of NETs in COVID-19 patients.

Methods

Two representative markers of NETs, circulating nucleosomes and myeloperoxidase-DNA, were measured in 115 hospitalized patients. Serum levels of interleukin [IL]-6, monocyte chemotactic protein-1 [MCP-1], plasma von Willebrand factor (vWF) and urinary biomarkers of renal tubular damage (β2-microglobulin [β2M] and kidney injury molecule 1 [KIM-1]) were measured.

Results

AKI was found in 43 patients (37.4%), and pre-existing chronic kidney disease (CKD) was a strong risk factor for AKI. Higher circulating NET levels were a significant predictor of increased risk of initial ICU admission, in-hospital mortality (adjusted HR 3.21, 95% CI 1.08–9.19) and AKI (OR 3.67, 95% CI 1.30-10.41), independent of age, diabetes, pre-existing CKD and IL-6 levels. There were strong correlations between circulating nucleosome levels and urinary KIM-1/creatinine (r=0.368, p=0.001) and β2M (r=0.218, p=0.049) levels. NETs were also strongly closely associated with serum vWF (r = 0.356, p&lt;0.001), but not with IL-6 or MCP-1 levels.

Conclusions

Elevated NETs were closely associated with AKI, which was a strong predictor of mortality. The close association between NETs and vWF may suggest a role for NETs in COVID-19-associated vasculopathy leading to AKI.

Serum and Urinary Biomarkers in COVID-19 Patients with or without Baseline Chronic Kidney Disease

In a prospective, observational, non-interventional, single-center study, we assessed various plasma and urinary biomarkers of kidney injury (neutrophil gelatinase-associated Lipocain [NGAL], kidney-injury molecule-1 [KIM-1], and interleukin-18 [IL-18]); inflammation (IL-6, C-reactive protein [CRP]); plus angiotensin converting enzyme 2 (ACE2) in 120 COVID-19 patients (of whom 70 had chronic kidney disease (CKD) at emergency-department (ED) admission). Our aim was to correlate the biomarkers with the outcomes (death, acute kidney injury [AKI]). All patients had received a chest-CT scan at admission to calculate the severity score (0–5). Biomarkers were also assessed in healthy volunteers and non-COVID-19-CKD patients. These biomarkers statistically differed across subgroups, i.e., they were significantly increased in COVID-19 patients, except for urinary (u)KIM1 and uIL-18. Amongst the biomarkers, only IL-6 was independently associated with mortality, along with AKI and not using remdesivir. Regarding the prediction of AKI, only IL-6 and uKIM1 were significantly elevated in patients presenting with AKI. However, AKI could not be predicted. Having high baseline IL-6 levels was associated with subsequent ventilation requirement and death. The mortality rate was almost 90% when the chest CT-scan severity score was 3 or 4 vs. 6.8% when the severity score was 0–2 (p < 0.0001).

The role of kidney injury biomarkers in COVID-19

The coronavirus disease-2019 (COVID-19) outbreak has been declared a global pandemic. COVID-19-associated acute kidney injury (COVID-19 AKI) is related to a high mortality rate and serves as an independent risk factor for hospital death in patients with COVID-19. Early diagnosis would allow for earlier intervention and potentially improve patient outcomes. The goal of early identification of AKI has been the primary impetus for AKI biomarker research, and several kidney injury biomarkers have been demonstrated to be beneficial in predicting COVID-19 AKI as well as disease progression in COVID-19. Furthermore, such data provide valuable insights into the molecular mechanisms underlying this complex and unique disease and serve as a molecular phenotyping tool that could be utilized to direct clinical intervention. This review focuses on a number of kidney injury biomarkers, such as CysC, NAGAL, KIM-1, L-FABP, IL-18, suPAR, and [TIMP-2] • [IGFBP7], which have been widely studied in common clinical settings, such as sepsis, cardiac surgery, and contrast-induced AKI. We explore the role of kidney injury biomarkers in COVID-19 and discuss what remains to be learned.

Development and validation of a nomogram for the early prediction of acute kidney injury in hospitalized COVID-19 patients

Introduction

Acute kidney injury (AKI) is a prevalent complication of coronavirus disease 2019 (COVID-19) and is closely linked with a poorer prognosis. The aim of this study was to develop and validate an easy-to-use and accurate early prediction model for AKI in hospitalized COVID-19 patients.

Methods

Data from 480 COVID-19-positive patients (336 in the training set and 144 in the validation set) were obtained from the public database of the Cancer Imaging Archive (TCIA). The least absolute shrinkage and selection operator (LASSO) regression method and multivariate logistic regression were used to screen potential predictive factors to construct the prediction nomogram. Receiver operating curves (ROC), calibration curves, as well as decision curve analysis (DCA) were adopted to assess the effectiveness of the nomogram. The prognostic value of the nomogram was also examined.

Results

A predictive nomogram for AKI was developed based on arterial oxygen saturation, procalcitonin, C-reactive protein, glomerular filtration rate, and the history of coronary artery disease. In the training set, the nomogram produced an AUC of 0.831 (95% confidence interval [CI]: 0.774-0.889) with a sensitivity of 85.2% and a specificity of 69.9%. In the validation set, the nomogram produced an AUC of 0.810 (95% CI: 0.737-0.871) with a sensitivity of 77.4% and a specificity of 78.8%. The calibration curve shows that the nomogram exhibited excellent calibration and fit in both the training and validation sets. DCA suggested that the nomogram has promising clinical effectiveness. In addition, the median length of stay (m-LS) for patients in the high-risk group for AKI (risk score ≥ 0.122) was 14.0 days (95% CI: 11.3-16.7 days), which was significantly longer than 8.0 days (95% CI: 7.1-8.9 days) for patients in the low-risk group (risk score <0.122) (hazard ratio (HR): 1.98, 95% CI: 1.55-2.53, p < 0.001). Moreover, the mortality rate was also significantly higher in the high-risk group than that in the low-risk group (20.6 vs. 2.9%, odd ratio (OR):8.61, 95%CI: 3.45-21.52).

Conclusions

The newly constructed nomogram model could accurately identify potential COVID-19 patients who may experience AKI during hospitalization at the very beginning of their admission and may be useful for informing clinical prognosis.

Comparative accuracy of biomarkers for the prediction of hospital-acquired acute kidney injury: a systematic review and meta-analysis

Background

Several biomarkers have been proposed to predict the occurrence of acute kidney injury (AKI); however, their efficacy varies between different trials. The aim of this study was to compare the predictive performance of different candidate biomarkers for AKI.

Methods

In this systematic review, we searched PubMed, Medline, Embase, and the Cochrane Library for papers published up to August 15, 2022. We selected all studies of adults (> 18 years) that reported the predictive performance of damage biomarkers (neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule-1 (KIM-1), liver-type fatty acid-binding protein (L-FABP)), inflammatory biomarker (interleukin-18 (IL-18)), and stress biomarker (tissue inhibitor of metalloproteinases-2 × insulin-like growth factor-binding protein-7 (TIMP-2 × IGFBP-7)) for the occurrence of AKI. We performed pairwise meta-analyses to calculate odds ratios (ORs) and 95% confidence intervals (CIs) individually. Hierarchical summary receiver operating characteristic curves (HSROCs) were used to summarize the pooled test performance, and the Grading of Recommendations, Assessment, Development and Evaluations criteria were used to appraise the quality of evidence.

Results

We identified 242 published relevant studies from 1,803 screened abstracts, of which 110 studies with 38,725 patients were included in this meta-analysis. Urinary NGAL/creatinine (diagnostic odds ratio [DOR] 16.2, 95% CI 10.1–25.9), urinary NGAL (DOR 13.8, 95% CI 10.2–18.8), and serum NGAL (DOR 12.6, 95% CI 9.3–17.3) had the best diagnostic accuracy for the risk of AKI. In subgroup analyses, urinary NGAL, urinary NGAL/creatinine, and serum NGAL had better diagnostic accuracy for AKI than urinary IL-18 in non-critically ill patients. However, all of the biomarkers had similar diagnostic accuracy in critically ill patients. In the setting of medical and non-sepsis patients, urinary NGAL had better predictive performance than urinary IL-18, urinary L-FABP, and urinary TIMP-2 × IGFBP-7: 0.3. In the surgical patients, urinary NGAL/creatinine and urinary KIM-1 had the best diagnostic accuracy. The HSROC values of urinary NGAL/creatinine, urinary NGAL, and serum NGAL were 91.4%, 85.2%, and 84.7%, respectively.

Conclusions

Biomarkers containing NGAL had the best predictive accuracy for the occurrence of AKI, regardless of whether or not the values were adjusted by urinary creatinine, and especially in medically treated patients. However, the predictive performance of urinary NGAL was limited in surgical patients, and urinary NGAL/creatinine seemed to be the most accurate biomarkers in these patients. All of the biomarkers had similar predictive performance in critically ill patients.

Trial registrationCRD42020207883, October 06, 2020.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13054-022-04223-6.

Prognostic Performance of Cystatin C in COVID-19: A Systematic Review and Meta-Analysis

Cystatin C is a specific biomarker of kidney function. We perform this meta-analysis to determine the association of Cystatin C with the COVID-19 severity. In this systematic review and meta-analysis, we searched PubMed, EMBASE, Cochrane library, and Web of Science for studies published until 2nd September 2022 that reported associations between Cystatin C levels and COVID-19 severity. The analysis was performed using a random-effects model to calculate pooled standard mean difference (SMD). Twenty-five studies were included in the meta-analysis. Pooled analysis showed statistically significant differences of Cystatin C levels among survive vs. decreased patients (0.998 ± 0.225 vs. 1.328 ± 0.475 mg/dL, respectively; SMD = -2.14; 95%CI: -3.28 to -1.01; p < 0.001). Cystatin C levels in COVID-19 severe vs. non-severe groups varied and amounted to 1.485 ± 1.191 vs. 1.014 ± 0.601 mg/dL, respectively (SMD = 1.81; 95%CI: 1.29 to 2.32; p < 0.001). Additionally, pooled analysis showed that Cystatin C levels in patients with acute kidney injury (AKI) was 1.562 ± 0.885 mg/dL, compared to 0.811 ± 0.108 mg/dL for patients without AKI (SMD = 4.56; 95%CI: 0.27 to 8.85; p = 0.04). Summing up, Cystatin C is a potentially very good marker to be used in the context of COVID-19 disease due to the prognosis of patients' serious condition, risk of AKI and mortality. In addition, Cystatin C could be used as a marker of renal complications in COVID-19 other than AKI due to the need to monitor patients even longer after leaving the hospital.