Association between urinary dickkopf-3, acute kidney injury, and subsequent loss of kidney function in patients undergoing cardiac surgery: an observational cohort study

Clinical impact of Wnt5a expression on persistence of acute kidney injury in patients with urosepsis

Abnormal Wnt5a expression is associated with dysregulated inflammation and organ dysfunction. However, the effect of Wnt5a activation on the duration of organ dysfunction remains unclear. This prospective study investigated the association between Wnt5a levels and persistent acute kidney injury (AKI) in patients with urosepsis. Serum creatinine and Wnt5a levels were measured on days 1 and 5 and at discharge in 87 patients diagnosed with urosepsis. Patients with urosepsis were classified into an improving acute kidney injury (AKI) group and a persistent or worsening AKI group according to the AKI stage on days 1 and 5. AKI recovery was defined as a discharge-to-baseline serum creatinine ratio of <1.5. Twenty-eight patients with urosepsis (32.2%) had persistent or worsening AKI, and their Wnt5a levels were higher on days 1 and 5 and at discharge than those with improving AKI. The association between Wnt5a levels and persistent or worsening AKI was maintained after adjusting for age, sex, baseline serum creatinine levels, and disease severity. Moreover, elevated Wnt5a levels were associated with an increased risk of major adverse kidney events. High Wnt5a levels at discharge were associated with unrecovered AKI and participants with AKI recovery had a steeper Wnt5a slope over time than those without recovery, irrespective of age, sex, baseline serum creatinine level, or disease severity. Assessment of Wnt5a expression was helpful in predicting AKI persistence and adverse outcomes in patients with urosepsis. Therefore, Wnt5a may serve as a valuable bio-marker for identifying the risk of persistence of AKI.

Biomarkers of acute kidney injury: From discovery to the future of clinical practice

Purpose of this review Acute kidney injury (AKI) is a complex syndrome whose development is associated with an increased morbidity and mortality. Recent studies show that this syndrome is a common complication in critically ill and surgical patients the trajectory of which may differ. As AKI can be induced by different triggers, it is complex and therefore challenging to manage patients with AKI. This review strives to provide a brief historical perspective on AKI, elucidate recent developments in diagnosing and managing AKI, and show the current usage of novel biomarkers in both clinical routine and research. In addition, we provide a perspective on potential future developments and their impact of AKI understanding and management. Recent findings/developments Recent studies show the merits of stress and damage biomarkers, highlighting limitations of the current KDIGO definition that only uses the functional biomarkers serum creatinine and urine output. The use of novel biomarkers led to the introduction of the concept of "subclinical AKI". This new classification may allow a more distinct management of affected or at risk patients. Ongoing studies, such as BigpAK-2 and PrevProgAKI, investigate the implementation of biomarker-guided interventions in clinical practice and may demonstrate an improvement in patients' outcome. Summary The ongoing scientific efforts surrounding AKI have deepened our understanding of the syndrome prompting an expansion of existing concepts. A future integration of stress and damage biomarkers in AKI management, may lead to an individualized therapy in this area.

Bradykinin induces acute kidney injury after hypothermic circulatory arrest through the repression of the Nrf2-xCT pathway

Postoperative acute kidney injury (AKI) is a common complication in patients undergoing deep hypothermic circulatory arrest (HCA); however, its underlying pathogenesis is unclear. In this study, we established a rat cardiopulmonary bypass model and demonstrated that hypothermia during HCA, rather than circulatory arrest, was responsible for the occurrence of AKI. By recruiting 56 patients who underwent surgery with HCA and analyzing the blood samples, we found that post-HCA AKI was associated with an increase in bradykinin. Animal experiments confirmed this and showed that hypothermia during HCA increased bradykinin levels by increasing kallikrein expression. Mechanistically, bradykinin inhibited the Nrf2-xCT pathway through B2R and caused renal oxidative stress damage. Application of Icatibant, a B2R inhibitor, reversed changes in the Nrf2-xCT pathway and oxidative stress damage. Finally, Icatibant reversed hypothermia-induced AKI in vivo. This finding reveals the pathogenesis of AKI after HCA and helps to provide therapeutic strategy for patients with post-HCA AKI.

AKIMLpred: An interpretable machine learning model for predicting acute kidney injury within seven days in critically ill patients based on a prospective cohort study

BACKGROUND

Early recognition and timely intervention for AKI in critically ill patients were crucial to reduce morbidity and mortality. This study aimed to use biomarkers to construct a optimal machine learning model for early prediction of AKI in critically ill patients within seven days.

METHODS

The prospective cohort study enrolled 929 patients altogether who were admitted in ICU including 680 patients in training set (Jiefang Campus) and 249 patients in external testing set (Binjiang Campus). After performing strict inclusion and exclusion criteria, 421 patients were selected in training set for constructing predictive model and 167 patients were selected in external testing for evaluating the predictive performance of resulting model. Urine and blood samples were collected for kidney injury associated biomarkers detection. Baseline clinical information and laboratory data of the study participants were collected. We determined the average prediction efficiency of six machine learning models through 10-fold cross validation.

RESULTS

In total, 78 variables were collected when admission in ICU and 43 variables were statistically significant between AKI and non-AKI cohort. Then, 35 variables were selected as independent features for AKI by univariate logistic regression. Spearman correlation analysis was used to remove two highly correlated variables. Three ranking methods were used to explore the influence of 33 variables for further determining the best combination of variables. The gini importance ranking method was found to be applicable for variables filtering. The predictive performance of AKIMLpred which constructed by the XGBoost algorithm was the best among six machine learning models. When the AKIMLpred included the nine features (NGAL, IGFBP7, sCysC, CAF22, KIM-1, NT-proBNP, IL-6, IL-18 and L-FABP) with the highest influence ranking, its model had the best prediction performance, with an AUC of 0.881 and an accuracy of 0.815 in training set, similarly, with an AUC of 0.889 and an accuracy of 0.846 in validation set. Moreover, the performace was slightly outperformed in testing set with an AUC of 0.902 and an accuracy of 0.846. The SHAP algorithm was used to interpret the prediction results of AKIMLpred. The web-calculator of AKIMLpred was shown for predicting AKI with more convenient(https://www.xsmartanalysis.com/model/list/predict/model/html?mid=8065&symbol=11gk693982SU6AE1ms21). AKIMLpred was better than the optimal model built with only routine tests for predicting AKI in critically ill patients within 7 days.

CONCLUSION

The model AKIMLpred constructed by the XGBoost algorithm with selecting the nine most influential biomarkers in the gini importance ranking method had the best performance in predicting AKI in critically ill patients within 7 days. This data-driven predictive model will help clinicians to make quick and accurate diagnosis.

Cardiac Surgery-Associated Acute Kidney Injury

AKI is a common and serious complication of cardiac surgery that has a significant impact on patient morbidity and mortality. The Kidney Disease Improving Global Outcomes definition of AKI is widely used to classify and identify AKI associated with cardiac surgery (cardiac surgery-associated AKI [CSA-AKI]) on the basis of changes in serum creatinine and/or urine output. There are various preoperative, intraoperative, and postoperative risk factors for the development of CSA-AKI which should be recognized and addressed as early as possible to expedite its diagnosis, reduce its occurrence, and prevent or ameliorate its devastating complications. Crucial issues are the inaccuracy of serum creatinine as a surrogate parameter of kidney function in the perioperative setting of cardiothoracic surgery and the necessity to discover more representative markers of the pathophysiology of AKI. However, except for the tissue inhibitor of metalloproteinase-2 and insulin-like growth factor binding protein 7 ratio, other diagnostic biomarkers with an acceptable sensitivity and specificity are still lacking. This article provides a comprehensive review of various aspects of CSA-AKI, including pathogenesis, risk factors, diagnosis, biomarkers, classification, prevention, and treatment management.

Urinary cytokeratin 20 as a predictor for chronic kidney disease following acute kidney injury

BACKGROUNDIdentifying patients with acute kidney injury (AKI) at high risk of chronic kidney disease (CKD) progression remains a challenge.METHODSKidney transcriptome sequencing was applied to identify the top upregulated genes in mice with AKI. The product of the top-ranking gene was identified in tubular cells and urine in mouse and human AKI. Two cohorts of patients with prehospitalization estimated glomerular filtration rate (eGFR) ≥ 45 mL/min/1.73 m2 who survived over 90 days after AKI were used to derive and validate the predictive models. AKI-CKD progression was defined as eGFR < 60 mL/min/1.73 m2 and with minimum 25% reduction from baseline 90 days after AKI in patients with prehospitalization eGFR ≥ 60 mL/min/1.73 m2. AKI-advanced CKD was defined as eGFR < 30 mL/min/1.73 m2 90 days after AKI in those with prehospitalization eGFR 45-59 mL/min/1.73 m2.RESULTSKidney cytokeratin 20 (CK20) was upregulated in injured proximal tubular cells and detectable in urine within 7 days after AKI. High concentrations of urinary CK20 (uCK20) were independently associated with the severity of histological AKI and the risk of AKI-CKD progression. In the Test set, the AUC of uCK20 for predicting AKI-CKD was 0.80, outperforming reported biomarkers for predicting AKI. Adding uCK20 to clinical variables improved the ability to predict AKI-CKD progression, with an AUC of 0.90, and improved the risk reclassification.CONCLUSIONThese findings highlight uCK20 as a useful predictor for AKI-CKD progression and may provide a tool to identify patients at high risk of CKD following AKI.FUNDINGNational Natural Science Foundation of China, National Key R&D Program of China, 111 Plan, Guangdong Key R&D Program.

Urinary Dickkopf 3 Is Not an Independent Risk Factor in a Cohort of Kidney Transplant Recipients and Living Donors

Urinary dickkopf 3 (uDKK3) is a marker released by kidney tubular epithelial cells that is associated with the progression of chronic kidney disease (CKD) and may cause interstitial fibrosis and tubular atrophy. Recent evidence suggests that uDKK3 can also predict the loss of kidney function in CKD patients and kidney transplant recipients, regardless of their current renal function. We conducted a prospective study on 181 kidney transplant (KTx) recipients who underwent allograft biopsy to determine the cause, analyzing the relationship between uDKK3 levels in urine, histological findings, and future allograft function progression. Additionally, we studied 82 living kidney donors before unilateral nephrectomy (Nx), 1-3 days after surgery, and 1 year post-surgery to observe the effects of rapid kidney function loss. In living donors, the uDKK3/creatinine ratio significantly increased 5.3-fold 1-3 days after Nx. However, it decreased significantly to a median level of 620 pg/mg after one year, despite the absence of underlying primary kidney pathology. The estimated glomerular filtration rate (eGFR) decreased by an average of 29.3% to approximately 66.5 (±13.5) mL/min/1.73 m2 after one year, with no further decline in the subsequent years. uDKK3 levels increased in line with eGFR loss after Nx, followed by a decrease as the eGFR partially recovered within the following year. However, uDKK3 did not correlate with the eGFR at the single time points in living donors. In KTx recipients, the uDKK3/creatinine ratio was significantly elevated with a median of 1550 pg/mg compared to healthy individuals or donors after Nx. The mean eGFR in the recipient group was 35.5 mL/min/1.73 m2. The uDKK3/creatinine ratio was statistically associated with the eGFR at biopsy but was not independently associated with the eGFR one year after biopsy or allograft loss. In conclusion, uDKK3 correlates with recent and future kidney function and kidney allograft survival in the renal transplant cohort. Nevertheless, our findings indicate that the uDKK3/creatinine ratio has no prognostic influence on future renal outcome in living donors and kidney recipients beyond the eGFR, independent of the presence of acute renal graft pathology, as correlations are GFR-dependent.

Association of Urinary Dickkopf-3 Levels with Cardiovascular Events and Kidney Disease Progression in Systolic Blood Pressure Intervention Trial

Key Points

In unadjusted analyses, elevated urinary Dickkopf-3 levels were strongly associated with higher risks of cardiovascular disease, ESKD, AKI, and mortality. However, associations were substantially weakened after adjustment for eGFR and albuminuria, suggesting limited prognostic value.

Background

Urinary Dickkopf-3 (uDKK3) is a tubular epithelial-derived profibrotic protein secreted into the urine under tubular stress. It is associated with kidney disease progression in persons with CKD and diabetes and postoperative and contrast-associated AKI. We explored associations of uDKK3 with cardiovascular disease (CVD), kidney, and mortality outcomes within the subset of Systolic Blood Pressure Intervention Trial participants with nondiabetic CKD.

Methods

We included 2344 participants with eGFR <60 ml/min per 1.73 m2 at baseline. We used Cox proportional hazards models to evaluate associations of uDKK3 with CVD (acute decompensated heart failure, myocardial infarction, acute coronary syndrome, stroke, or CVD death), kidney outcomes (incident ESKD, incident AKI, and eGFR decline ≥30%), and all-cause mortality. We used linear mixed models to examine the association of uDKK3 with annual percentage change in eGFR. Models were adjusted for demographic and clinical characteristics, eGFR, and albuminuria.

Results

Over a median follow-up of 3.5 years, there were 292 CVD, 73 ESKD, 183 AKI, 471 eGFR decline, and 228 mortality events. In multivariable models without adjustment for eGFR and albuminuria, uDKK3 was strongly associated with CVD, ESKD, AKI, eGFR decline ≥30%, and mortality. However, after further adjustment for eGFR and albuminuria, uDKK3 was no longer associated with risks for composite CVD (hazard ratio, 1.07; 95% confidence interval, 0.92 to 1.23), ESKD (0.80; 0.62 to 1.02), AKI (1.01; 0.85 to 1.21), eGFR decline ≥30% (0.88; 0.79 to 0.99), or mortality (1.02; 0.87 to 1.20). For the linear eGFR change outcome, higher uDKK3 also had no association in the fully adjusted model (−0.03; −0.41 to 0.36).

Conclusions

Among individuals with hypertension and nondiabetic CKD, higher uDKK3 appeared to have associations with a greater risk of CVD events, incident ESKD, incident AKI, eGFR decline ≥30%, and mortality but these associations were not independent of eGFR and albuminuria.

Prognostic Biomarkers and AKI: Potential to Enhance the Identification of Post-Operative Patients at Risk of Loss of Renal Function

Acute kidney injury (AKI) is a common complication after surgery and the more complex the surgery, the greater the risk. During surgery, patients are exposed to a combination of factors all of which are associated with the development of AKI. These include hypotension and hypovolaemia, sepsis, systemic inflammation, the use of nephrotoxic agents, tissue injury, the infusion of blood or blood products, ischaemia, oxidative stress and reperfusion injury. Given the risks of AKI, it would seem logical to conclude that early identification of patients at risk of AKI would translate into benefit. The conventional markers of AKI, namely serum creatinine and urine output are the mainstay of defining chronic kidney disease but are less suited to the acute phase. Such concerns are compounded in surgical patients given they often have significantly reduced mobility, suboptimal levels of nutrition and reduced muscle bulk. Many patients may also have misleadingly low serum creatinine and high urine output due to aggressive fluid resuscitation, particularly in intensive care units. Over the last two decades, considerable information has accrued with regard to the performance of what was termed "novel" biomarkers of AKI, and here, we discuss the most examined molecules and performance in surgical settings. We also discuss the application of biomarkers to guide patients' postoperative care.

A proposed framework for advancing acute kidney injury risk stratification and diagnosis in children: a report from the 26th Acute Disease Quality Initiative (ADQI) conference

Acute kidney injury (AKI) in children is associated with increased morbidity, reduced health-related quality of life, greater resource utilization, and higher mortality. Improvements in the timeliness and precision of AKI diagnosis in children are needed. In this report, we highlight existing, novel, and on-the-horizon diagnostic and risk-stratification tools for pediatric AKI, and outline opportunities for integration into clinical practice. We also summarize pediatric-specific high-risk diagnoses and exposures for AKI, as well as the potential role of real-time risk stratification and clinical decision support to improve outcomes. Lastly, the key characteristics of important pediatric AKI phenotypes will be outlined. Throughout, we identify key knowledge gaps, which represent prioritized areas of focus for future research that will facilitate a comprehensive, timely and personalized approach to pediatric AKI diagnosis and management.

From Acute to Chronic: Unraveling the Pathophysiological Mechanisms of the Progression from Acute Kidney Injury to Acute Kidney Disease to Chronic Kidney Disease

This article provides a thorough overview of the biomarkers, pathophysiology, and molecular pathways involved in the transition from acute kidney injury (AKI) and acute kidney disease (AKD) to chronic kidney disease (CKD). It categorizes the biomarkers of AKI into stress, damage, and functional markers, highlighting their importance in early detection, prognosis, and clinical applications. This review also highlights the links between renal injury and the pathophysiological mechanisms underlying AKI and AKD, including renal hypoperfusion, sepsis, nephrotoxicity, and immune responses. In addition, various molecules play pivotal roles in inflammation and hypoxia, triggering maladaptive repair, mitochondrial dysfunction, immune system reactions, and the cellular senescence of renal cells. Key signaling pathways, such as Wnt/β-catenin, TGF-β/SMAD, and Hippo/YAP/TAZ, promote fibrosis and impact renal function. The renin-angiotensin-aldosterone system (RAAS) triggers a cascade leading to renal fibrosis, with aldosterone exacerbating the oxidative stress and cellular changes that promote fibrosis. The clinical evidence suggests that RAS inhibitors may protect against CKD progression, especially post-AKI, though more extensive trials are needed to confirm their full impact.

New biomarkers in acute kidney injury

Acute kidney injury (AKI) is a commonly encountered clinical syndrome. Although it often complicates community acquired illness, it is more common in hospitalized patients, particularly those who are critically ill or who have undergone major surgery. Approximately 20% of hospitalized adult patients develop an AKI during their hospital care, and this rises to nearly 60% in the critically ill, depending on the population being considered. In general, AKI is more common in older adults, in those with preexisting chronic kidney disease and in those with known risk factors for AKI (including diabetes and hypertension). The development of AKI is associated with an increase in both mortality and morbidity, including the development of post-AKI chronic kidney disease. Currently, AKI is defined by a rise in serum creatinine from either a known or derived baseline value and/or oliguria or anuria. However, clinicians may fail to recognize the initial development of AKI because of a delay in the rise of serum creatinine or because of inaccurate urine output monitoring. This, in turn, delays any putative measures to treat AKI or to limit its degree. Consequently, efforts have focused on new biomarkers associated with AKI that may allow early recognition of this syndrome with the intent that this will translate into improved patient outcomes. Here we outline current biomarkers associated with AKI and explore their potential in aiding diagnosis, understanding the pathophysiology and directing therapy.

The role of urinary Dickkopf-3 in the prediction of acute kidney injury: a systematic review meta-analysis

BACKGROUND

To systematically evaluate the diagnostic efficacy of urinary Dickkopf-Related Protein 3 (DKK-3) in acute kidney injury and to explore the clinical application value of urinary DKK-3.

METHOD

English databases (PubMed, Embase, Cochrane, and WOS) and Chinese databases (VIP, WanFang data, and China National Knowledge Internet) were screened for relevant papers published before March 12, 2023. After literature screening and data extraction, quality assessment was performed according to the QUADAS-2 scoring system. Then, the combined diagnostic and predictive parameters were calculated using a bivariate mixed effect meta-analysis model. Deek's funnel plot asymmetry test assessed publication bias, and Fagan's nomogram plot was used to verify its clinical utility.

RESULT

A total of 5 studies involving 2787 patients were included in this meta-analysis, of which 4 focused on contrast-induced acute kidney injury (CI-AKI) and 1 focused on AKI associated with cardiac surgery. The analysis showed that urine Dickkopf-3 has high diagnostic accuracy for AKI, with a sensitivity of 0.55 (95% CI [0.41, 0.68]), specificity of 0.80 (95% CI [0.70, 0.87]), positive likelihood ratio (PLR) of 2.7 [1.8, 4.1], negative likelihood ratio (NLR) of 0.56 [0.42, 0.75], diagnostic odds ratio (DOR) of 5 [3, 9], and AUC of 0.74 [0.70-0.77]. We did not perform subgroup analyses for predictive value due to the small number of included studies.

CONCLUSION

Urinary DKK3 may have limited predictive ability for acute kidney injury, especially for AKI associated with cardiac surgery. Therefore, urinary DKK3 may serve as a potential predictor for AKI. However, clinical studies with larger samples are still needed for validation.

Personalized acute kidney injury treatment

PURPOSE OF REVIEW

Acute kidney injury (AKI) is a complex syndrome that might be induced by different causes and is associated with an increased morbidity and mortality. Therefore, it is a very heterogeneous syndrome and establishing a "one size fits all" treatment approach might not work. This review aims to examine the potential of personalized treatment strategies for AKI.

RECENT FINDINGS

The traditional diagnosis of AKI is based on changes of serum creatinine and urine output, but these two functional biomarkers have several limitations. Recent research identified different AKI phenotypes based on clinical features, biomarkers, and pathophysiological pathways. Biomarkers, such as Cystatin C, NGAL, TIMP2∗IGFBP7, CCL14, and DKK-3, have shown promise in predicting AKI development, renal recovery, and prognosis. Biomarker-guided interventions, such as the implementation of the KDIGO bundle, have demonstrated an improvement in renal outcomes in specific patient groups.

SUMMARY

A personalized approach to AKI treatment as well as research is becoming increasingly important as it allows the identification of distinct AKI phenotypes and the potential for targeted interventions. By utilizing biomarkers and clinical features, physicians might be able to stratify patients into subphenotypes, enabling more individualized treatment strategies. This review highlights the potential of personalized AKI treatment, emphasizing the need for further research and large-scale clinical trials to validate the efficacy of these approaches.

Plasma indole-3-aldehyde as a novel biomarker of acute kidney injury after cardiac surgery: a reanalysis using prospective metabolomic data

BACKGROUND

Acute kidney injury (AKI) is a frequent complication of cardiac surgery that poses significant risks for both the development of chronic kidney diseases and mortality. Our previous study illustrated that heightened expression levels of faecal and plasma indole metabolites before the operation were associated with ischemic AKI. In this study, we aimed to validate the supposition that plasma indole-3-aldehyde (I3A) could serve as a predictive biomarker for AKI in patients undergoing cardiac surgery.

METHODS

This statistical reanalysis utilized AKI metabolomic data from patients scheduled for cardiac surgery between April 2022 and July 2022 in two tertiary hospitals. Faecal and blood samples were prospectively collected before surgery within 24 h, and variables related to the preoperative, intraoperative, and postoperative periods were recorded. AKI diagnosis was based on the Kidney Disease Improving Global Outcomes criteria.

RESULTS

In this study, 55 patients who underwent cardiac surgery were analyzed, and 27 of them (49.1%) developed postoperative AKI. Before surgery, these patients had significantly higher levels of faecal indole metabolites, including skatole, trans-3-indoleacrylic acid, and 5-methoxyindoleacetic acid. The plasma I3A, clinical model that considered perioperative and intraoperative variables, and their combination had area under the receiver operating characteristic curve (ROC) values of 0.79 (95% CI 0.67-0.91), 0.78 (95% CI 0.66-0.90), and 0.84 (95% CI 0.74-0.94) for predicting AKI, respectively. Furthermore, by utilizing net reclassification improvement and integrated discrimination improvement, plasma I3A showed significant improvements in risk reclassification compared to the clinical model alone.

CONCLUSIONS

The dysregulation of gut microbiota metabolism in patients scheduled for cardiac surgery can result in an increase in indoles from tryptophan metabolism, which may be associated with postoperative acute kidney injury (AKI). This suggests that indoles may serve as a predictive biomarker for AKI in patients undergoing cardiac surgery.

Identification of hub biomarkers and immune-related pathways participating in the progression of Kawasaki disease by integrated bioinformatics analysis

BACKGROUND

Kawasaki disease (KD) is a systemic vasculitis that commonly affects children and its etiology remains unknown. Growing evidence suggests that immune-mediated inflammation and immune cells in the peripheral blood play crucial roles in the pathophysiology of KD. The objective of this research was to find important biomarkers and immune-related mechanisms implicated in KD, along with their correlation with immune cells in the peripheral blood.

MATERIAL/METHODS

Gene microarray data from the Gene Expression Omnibus (GEO) was utilized in this study. Three datasets, namely GSE63881 (341 samples), GSE73463 (233 samples), and GSE73461 (279 samples), were obtained. To find intersecting genes, we employed differentially expressed genes (DEGs) analysis and weighted gene co-expression network analysis (WGCNA). Subsequently, functional annotation, construction of protein-protein interaction (PPI) networks, and Least Absolute Shrinkage and Selection Operator (LASSO) regression were performed to identify hub genes. The accuracy of these hub genes in identifying KD was evaluated using the receiver operating characteristic curve (ROC). Furthermore, Gene Set Variation Analysis (GSVA) was employed to explore the composition of circulating immune cells within the assessed datasets and their relationship with the hub gene markers.

RESULTS

WGCNA yielded eight co-expression modules, with one hub module (MEblue module) exhibiting the strongest association with acute KD. 425 distinct genes were identified. Integrating WGCNA and DEGs yielded a total of 277 intersecting genes. By conducting LASSO analysis, five hub genes (S100A12, MMP9, TLR2, NLRC4 and ARG1) were identified as potential biomarkers for KD. The diagnostic value of these five hub genes was demonstrated through ROC curve analysis, indicating their high accuracy in diagnosing KD. Analysis of the circulating immune cell composition within the assessed datasets revealed a significant association between KD and various immune cell types, including activated dendritic cells, neutrophils, immature dendritic cells, macrophages, and activated CD8 T cells. Importantly, all five hub genes exhibited strong correlations with immune cells.

CONCLUSION

Activated dendritic cells, neutrophils, and macrophages were closely associated with the pathogenesis of KD. Furthermore, the hub genes (S100A12, MMP9, TLR2, NLRC4, and ARG1) are likely to participate in the pathogenic mechanisms of KD through immune-related signaling pathways.

Update on persistent acute kidney injury in critical illnesses

Acute kidney injury (AKI) affects about half of patients admitted to the intensive care unit (ICU), and worsens their short- and long-term outcomes. Apparently self-limiting AKI episodes initiate a progression toward chronic kidney disease (CKD) through cellular and molecular mechanisms that are yet to be explained. In particular, persistent AKI, defined in 2016 by the Acute Dialysis Quality Initiative as an AKI which lasts more than 48 h from its onset, has been correlated with higher morbidity and mortality, and with a higher progression to acute kidney disease (AKD) and CKD than transient AKI (i.e. AKI with a reversal within 48 h). This classification has been also used in the setting of solid organ transplantation, demonstrating similar outcomes. Due to its incidence and poor prognosis and because prompt interventions seem to change its course, persistent AKI should be recognized early and followed-up also after its recovery. However, while AKI and CKD are well-described syndromes, persistent AKI and AKD are relatively new entities. The purpose of this review is to highlight the key phases of persistent AKI in ICU patients in terms of both clinical and mechanistic features in order to offer to clinicians and researchers an updated basis from which to start improving patients' care and direct future research.

Cardiac surgery-Associated acute kidney injury - A narrative review

Cardiac Surgery-Associated Acute Kidney Injury (CSA-AKI) is a serious complication seen in approximately 20-30% of cardiac surgery patients. The underlying pathophysiology is complex, often involving both patient- and procedure related risk factors. In contrast to AKI occurring after other types of major surgery, the use of cardiopulmonary bypass comprises both additional advantages and challenges, including non-pulsatile flow, targeted blood flow and pressure as well as the ability to manipulate central venous pressure (congestion). With an increasing focus on the impact of CSA-AKI on both short and long-term mortality, early identification and management of high-risk patients for CSA-AKI has evolved. The present narrative review gives an up-to-date summary on definition, diagnosis, underlying pathophysiology, monitoring and implications of CSA-AKI, including potential preventive interventions. The review will provide the reader with an in-depth understanding of how to identify, support and provide a more personalized and tailored perioperative management to avoid development of CSA-AKI.

Definition, Staging, and Role of Biomarkers in Acute Kidney Injury in the Context of Cardiovascular Interventions

Acute kidney injury (AKI) is a frequently occurring complication of cardiovascular interventions, and associated with adverse outcomes. Therefore, a clear definition of AKI is of paramount importance to enable timely recognition and treatment. Historically, changes in the serum creatinine and urine output have been used to define AKI, and the criteria have evolved over time with better understanding of the impact of AKI on the outcomes. However, the reliance on serum creatinine for these AKI definitions carries numerous limitations including delayed rise, inability to differentiate between hemodynamics versus structural injury and assay variability to name a few.

Early recognition and prevention of acute kidney injury in hospitalised children

Acute kidney injury is common in hospitalised children and is associated with poor patient outcomes. Once acute kidney injury occurs, effective therapies to improve patient outcomes or kidney recovery are scarce. Early identification of children at risk of acute kidney injury or at an early injury stage is essential to prevent progression and mitigate complications. Paediatric acute kidney injury is under-recognised by clinicians, which is a barrier to optimisation of inpatient care and follow-up. Acute kidney injury definitions rely on functional biomarkers (ie, serum creatinine and urine output) that are inadequate, since they do not account for biological variability, analytical issues, or physiological responses to volume depletion. Improved predictive tools and diagnostic biomarkers of kidney injury are needed for earlier detection. Novel strategies, including biomarker-guided care algorithms, machine-learning methods, and electronic alerts tied to clinical decision support tools, could improve paediatric acute kidney injury care. Clinical prediction models should be studied in different paediatric populations and acute kidney injury phenotypes. Research is needed to develop and test prevention strategies for acute kidney injury in hospitalised children, including care bundles and therapeutics.

Kidney outcome after mild to moderate COVID-19

BACKGROUND

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has a remarkable kidney tropism. While kidney effects are common in severe coronavirus disease 2019 (COVID-19), data on non-severe courses are limited. Here we provide a multilevel analysis of kidney outcomes after non-severe COVID-19 to test for eventual kidney sequela.

METHODS

This cross-sectional study investigates individuals after COVID-19 and matched controls recruited from the Hamburg City Health Study (HCHS) and its COVID-19 program. The HCHS is a prospective population-based cohort study within the city of Hamburg, Germany. During the COVID-19 pandemic the study additionally recruited subjects after polymerase chain reaction-confirmed SARS-CoV-2 infections. Matching was performed by age, sex and education. Main outcomes were estimated glomerular filtration rate (eGFR), albuminuria, Dickkopf3, haematuria and pyuria.

RESULTS

A total of 443 subjects in a median of 9 months after non-severe COVID-19 were compared with 1328 non-COVID-19 subjects. The mean eGFR was mildly lower in post-COVID-19 than non-COVID-19 subjects, even after adjusting for known risk factors {β = -1.84 [95% confidence interval (CI) -3.16 to -0.52]}. However, chronic kidney disease [odds ratio (OR) 0.90 (95% CI 0.48-1.66)] or severely increased albuminuria [OR 0.76 (95% CI 0.49-1.09)] equally occurred in post-COVID-19 and non-COVID-19 subjects. Haematuria, pyuria and proteinuria were also similar between the two cohorts, suggesting no ongoing kidney injury after non-severe COVID-19. Further, Dickkopf3 was not increased in the post-COVID-19 cohort, indicating no systematic risk for ongoing GFR decline [β = -72.19 (95% CI -130.0 to -14.4)].

CONCLUSION

While mean eGFR was slightly lower in subjects after non-severe COVID-19, there was no evidence for ongoing or progressive kidney sequela.

Acute Kidney Injury: Gaps and Opportunities for Knowledge and Growth

Acute kidney injury (AKI) occurs frequently in hospitalized patients, regardless of age or prior medical history. Increasing awareness of the epidemiologic problem of AKI has directly led to increased study of global recognition, diagnostic tools, both reactive and proactive management, and analysis of long-term sequelae. Many gaps remain, however, and in this article we highlight opportunities to add significantly to the increasing bodies of evidence surrounding AKI. Practical considerations related to initiation, prescription, anticoagulation, and monitoring are discussed. In addition, the importance of AKI follow-up evaluation, particularly for those surviving the receipt of renal replacement therapy, is highlighted as a push for global equity in the realm of critical care nephrology is broached. Addressing these gaps presents an opportunity to impact patient care directly and improve patient outcomes.

Recent Advances in Urinary Peptide and Proteomic Biomarkers in Chronic Kidney Disease: A Systematic Review

Biomarker development, improvement, and clinical implementation in the context of kidney disease have been a central focus of biomedical research for decades. To this point, only serum creatinine and urinary albumin excretion are well-accepted biomarkers in kidney disease. With their known blind spot in the early stages of kidney impairment and their diagnostic limitations, there is a need for better and more specific biomarkers. With the rise in large-scale analyses of the thousands of peptides in serum or urine samples using mass spectrometry techniques, hopes for biomarker development are high. Advances in proteomic research have led to the discovery of an increasing amount of potential proteomic biomarkers and the identification of candidate biomarkers for clinical implementation in the context of kidney disease management. In this review that strictly follows the PRISMA guidelines, we focus on urinary peptide and especially peptidomic biomarkers emerging from recent research and underline the role of those with the highest potential for clinical implementation. The Web of Science database (all databases) was searched on 17 October 2022, using the search terms "marker *" OR biomarker * AND "renal disease" OR "kidney disease" AND "proteome *" OR "peptid *" AND "urin *". English, full-text, original articles on humans published within the last 5 years were included, which had been cited at least five times per year. Studies based on animal models, renal transplant studies, metabolite studies, studies on miRNA, and studies on exosomal vesicles were excluded, focusing on urinary peptide biomarkers. The described search led to the identification of 3668 articles and the application of inclusion and exclusion criteria, as well as abstract and consecutive full-text analyses of three independent authors to reach a final number of 62 studies for this manuscript. The 62 manuscripts encompassed eight established single peptide biomarkers and several proteomic classifiers, including CKD273 and IgAN237. This review provides a summary of the recent evidence on single peptide urinary biomarkers in CKD, while emphasizing the increasing role of proteomic biomarker research with new research on established and new proteomic biomarkers. Lessons learned from the last 5 years in this review might encourage future studies, hopefully resulting in the routine clinical applicability of new biomarkers.

Prediction of urinary dickkopf-3 for AKI, sepsis-associated AKI, and PICU mortality in children

BACKGROUND

Preoperative urinary dickkopf-3 (DKK3) is proposed as an early biomarker for the prediction of acute kidney injury (AKI) in patients undergoing cardiac surgery. We explored the clinical utility of urinary DKK3 for the early predictive value for AKI, sepsis-associated AKI (SA-AKI), and pediatric intensive care unit (PICU) mortality in critically ill children.

METHODS

Urine samples were collected during the first 24 h after admission for measurement of DKK3. AKI diagnosis was based on serum creatinine and urine output using the KDIGO criteria. SA-AKI was defined as AKI that occurred in children who met the sepsis criteria in accordance with the surviving sepsis campaign international guidelines for children.

RESULTS

Of the 420 children, 73 developed AKI, including 24 with SA-AKI, and 30 died during the PICU stay. The urinary DKK3 level was significantly associated with AKI, SA-AKI, and PICU mortality, even after adjustment for confounders. The area under the receiver operating characteristic curve of urinary DKK3 for the discrimination of AKI, SA-AKI, and PICU mortality was 0.70, 0.80, and 0.78, respectively.

CONCLUSION

Urinary DKK3 was independently associated with an increased risk for AKI, SA-AKI, and PICU mortality and may be predictive of the aforementioned issues in critically ill children.

IMPACT

Urinary dickkopf-3 (DKK3) has been identified as a preoperative biomarker for the prediction of acute kidney injury (AKI) following cardiac surgery or coronary angiography in adult patients. However, little is known about the clinical utility of urinary DKK3 in pediatric cohorts. This study demonstrated that urinary DKK3 is capable of early predicting AKI and pediatric intensive care unit (PICU) mortality and discriminating sepsis-associated AKI (SA-AKI) from other types of AKI. Urinary DKK3 may be an early biomarker for predicting AKI, SA-AKI, and PICU mortality in critically ill children.

Urinary DKK3 as a biomarker for short-term kidney function decline in children with chronic kidney disease: an observational cohort study

BACKGROUND

Childhood-onset chronic kidney disease is a progressive condition that can have a major effect on life expectancy and quality. We evaluated the usefulness of the kidney tubular cell stress marker urinary Dickkopf-related protein 3 (DKK3) in determining the short-term risk of chronic kidney disease progression in children and identifying those who will benefit from specific nephroprotective interventions.

METHODS

In this observational cohort study, we assessed the association between urinary DKK3 and the combined kidney endpoint (ie, the composite of 50% reduction of the estimated glomerular filtration rate [eGFR] or progression to end-stage kidney disease) or the risk of kidney replacement therapy (ie, dialysis or transplantation), and the interaction of the combined kidney endpoint with intensified blood pressure reduction in the randomised controlled ESCAPE trial. Moreover, urinary DKK3 and eGFR were quantified in children aged 3-18 years with chronic kidney disease and urine samples available enrolled in the prospective multicentre ESCAPE (NCT00221845; derivation cohort) and 4C (NCT01046448; validation cohort) studies at baseline and at 6-monthly follow-up visits. Analyses were adjusted for age, sex, hypertension, systolic blood pressure SD score (SDS), BMI SDS, albuminuria, and eGFR.

FINDINGS

659 children were included in the analysis (231 from ESCAPE and 428 from 4C), with 1173 half-year blocks in ESCAPE and 2762 in 4C. In both cohorts, urinary DKK3 above the median (ie, >1689 pg/mg creatinine) was associated with significantly greater 6-month eGFR decline than with urinary DKK3 at or below the median (-5·6% [95% CI -8·6 to -2·7] vs 1·0% [-1·9 to 3·9], p<0·0001, in ESCAPE; -6·2% [-7·3 to -5·0] vs -1·5% [-2·9 to -0·1], p<0·0001, in 4C), independently of diagnosis, eGFR, and albuminuria. In ESCAPE, the beneficial effect of intensified blood pressure control was limited to children with urinary DKK3 higher than 1689 pg/mg creatinine, in terms of the combined kidney endpoint (HR 0·27 [95% CI 0·14 to 0·55], p=0·0003, number needed to treat 4·0 [95% CI 3·7 to 4·4] vs 250·0 [66·9 to ∞]) and the need for kidney replacement therapy (HR 0·33 [0·13 to 0·85], p=0·021, number needed to treat 6·7 [6·1 to 7·2] vs 31·0 [27·4 to 35·9]). In 4C, inhibition of the renin-angiotensin-aldosterone system resulted in significantly lower urinary DKK3 concentrations (least-squares mean 12 235 pg/mg creatinine [95% CI 10 036 to 14 433] in patients not on angiotensin-converting enzyme inhibitors or angiotensin 2 receptor blockers vs 6861 pg/mg creatinine [5616 to 8106] in those taking angiotensin-converting enzyme inhibitors or angiotensin 2 receptor blockers, p<0·0001).

INTERPRETATION

Urinary DKK3 indicates short-term risk of declining kidney function in children with chronic kidney disease and might allow a personalised medicine approach by identifying those who benefit from pharmacological nephroprotection, such as intensified blood pressure lowering.

FUNDING

None.

Kidney fibrosis: from mechanisms to therapeutic medicines

Chronic kidney disease (CKD) is estimated to affect 10-14% of global population. Kidney fibrosis, characterized by excessive extracellular matrix deposition leading to scarring, is a hallmark manifestation in different progressive CKD; However, at present no antifibrotic therapies against CKD exist. Kidney fibrosis is identified by tubule atrophy, interstitial chronic inflammation and fibrogenesis, glomerulosclerosis, and vascular rarefaction. Fibrotic niche, where organ fibrosis initiates, is a complex interplay between injured parenchyma (like tubular cells) and multiple non-parenchymal cell lineages (immune and mesenchymal cells) located spatially within scarring areas. Although the mechanisms of kidney fibrosis are complicated due to the kinds of cells involved, with the help of single-cell technology, many key questions have been explored, such as what kind of renal tubules are profibrotic, where myofibroblasts originate, which immune cells are involved, and how cells communicate with each other. In addition, genetics and epigenetics are deeper mechanisms that regulate kidney fibrosis. And the reversible nature of epigenetic changes including DNA methylation, RNA interference, and chromatin remodeling, gives an opportunity to stop or reverse kidney fibrosis by therapeutic strategies. More marketed (e.g., RAS blockage, SGLT2 inhibitors) have been developed to delay CKD progression in recent years. Furthermore, a better understanding of renal fibrosis is also favored to discover biomarkers of fibrotic injury. In the review, we update recent advances in the mechanism of renal fibrosis and summarize novel biomarkers and antifibrotic treatment for CKD.

Biomarker-Based Prediction of Survival and Recovery of Kidney Function in Acute Kidney Injury

BACKGROUND

Acute kidney injury (AKI) affects increasing numbers of hospitalized patients; the prognosis remains poor. The diagnosis is still based on the 2012 published KDIGO criteria. Numerous new AKI biomarkers have been identified in recent years; they either reflect impaired excretory function or structural damage. The majority of markers are useful for AKI recognition under certain circumstances. Fewer data are available on the role of biomarkers in the prediction of in-hospital survival and renal recovery post-AKI. The current article is intended to provide information about these two aspects.

SUMMARY

The following databases were screened: PubMed, Web of Science, Cochrane Library, Scopus. The period lasted from 2000 until 2022. The following terms were applied: "AKI" AND "biomarker" AND "survival" OR "mortality" OR "recovery of kidney function" OR "renal recovery" OR "kidney recovery". The following terms were used for additional literature search: "TIMP-2" AND "IGFBP7" and "RNA biomarker" AND "hematology". Regarding mortality, exclusively those studies were selected that addressed the in-hospital mortality. Nine (9) studies were identified that evaluated biomarker-based prediction of in-hospital mortality and/or of recovery of kidney function (ROKF). A homogenous definition of ROKF is however missing yet. Currently, some biomarkers, measured early during the course of the disease, are associated with increased mortality risk and/or with a higher chance of renal recovery.

KEY MESSAGES

The literature provides only a few biomarker-related studies that address the issues of mortality and recovery. The definition of ROKF needs to be homogenized.

Development and Validation of a Machine Learning Predictive Model for Cardiac Surgery-Associated Acute Kidney Injury

OBJECTIVE

We aimed to develop and validate a predictive machine learning (ML) model for cardiac surgery associated with acute kidney injury (CSA-AKI) based on a multicenter randomized control trial (RCT) and a Medical Information Mart for Intensive Care-IV (MIMIC-IV) dataset.

METHODS

This was a subanalysis from a completed RCT approved by the Ethics Committee of Fuwai Hospital in Beijing, China (NCT03782350). Data from Fuwai Hospital were randomly assigned, with 80% for the training dataset and 20% for the testing dataset. The data from three other centers were used for the external validation dataset. Furthermore, the MIMIC-IV dataset was also utilized to validate the performance of the predictive model. The area under the receiver operating characteristic curve (ROC-AUC), the precision-recall curve (PR-AUC), and the calibration brier score were applied to evaluate the performance of the traditional logistic regression (LR) and eleven ML algorithms. Additionally, the Shapley Additive Explanations (SHAP) interpreter was used to explain the potential risk factors for CSA-AKI.

RESULT

A total of 6495 eligible patients undergoing cardiopulmonary bypass (CPB) were eventually included in this study, 2416 of whom were from Fuwai Hospital (Beijing), for model development, 562 from three other cardiac centers in China, and 3517 from the MIMICIV dataset, were used, respectively, for external validation. The CatBoostClassifier algorithms outperformed other models, with excellent discrimination and calibration performance for the development, as well as the MIMIC-IV, datasets. In addition, the CatBoostClassifier achieved ROC-AUCs of 0.85, 0.67, and 0.77 and brier scores of 0.14, 0.19, and 0.16 in the testing, external, and MIMIC-IV datasets, respectively. Moreover, the utmost important risk factor, the N-terminal brain sodium peptide (NT-proBNP), was confirmed by the LASSO method in the feature section process. Notably, the SHAP explainer identified that the preoperative blood urea nitrogen level, prothrombin time, serum creatinine level, total bilirubin level, and age were positively correlated with CSA-AKI; preoperative platelets level, systolic and diastolic blood pressure, albumin level, and body weight were negatively associated with CSA-AKI.

CONCLUSIONS

The CatBoostClassifier algorithms outperformed other ML models in the discrimination and calibration of CSA-AKI prediction cardiac surgery with CPB, based on a multicenter RCT and MIMIC-IV dataset. Moreover, the preoperative NT-proBNP level was confirmed to be strongly related to CSA-AKI.

MG53: A potential therapeutic target for kidney disease

Ensuring cell survival and tissue regeneration by maintaining cellular integrity is important to the pathophysiology of many human diseases, including kidney disease. Mitsugumin 53 (MG53) is a member of the tripartite motif-containing (TRIM) protein family that plays an essential role in repairing cell membrane injury and improving tissue regeneration. In recent years, an increasing number of studies have demonstrated that MG53 plays a renoprotective role in kidney diseases. Moreover, with the beneficial effects of the recombinant human MG53 (rhMG53) protein in the treatment of kidney diseases in different animal models, rhMG53 shows significant therapeutic potential in kidney disease. In this review, we elucidate the role of MG53 and its molecular mechanism in kidney disease to provide new approaches to the treatment of kidney disease.

Definitions, phenotypes, and subphenotypes in acute kidney injury-Moving towards precision medicine

The current definition of acute kidney injury (AKI) is generic and, based only on markers of function, is unsuitable for guiding individualized treatment. AKI is a complex syndrome with multiple presentations and causes. Targeted AKI management will only be possible if different phenotypes and subphenotypes of AKI are recognised, based on causation and related pathophysiology. Molecular signatures to identify subphenotypes are being recognised, as specific biomarkers reveal activated pathways. Assessment of individual clinical risk needs wider dissemination to allow identification of patients at high risk of AKI. New and more timely markers for glomerular filtration rate (GFR) are available. However, AKI diagnosis and classification should not be limited to GFR, but include tubular function and damage. Combining damage and stress biomarkers with functional markers enhances risk prediction, and identifies a population enriched for clinical trials targeting AKI. We review novel developments and aim to encourage implementation of these new techniques into clinical practice as a strategy for individualizing AKI treatment akin to a precision medicine-based approach.

Urinary Dickkopf-3 (DKK3) Is Associated with Greater eGFR Loss in Patients with Resistant Hypertension

Patients with resistant hypertension (HTN) demonstrate an increased risk of chronic kidney disease and progression to end-stage renal disease; however, the individual course of progression is hard to predict. Assessing the stress-induced, urinary glycoprotein Dickkopf-3 (uDKK3) may indicate ongoing renal damage and consecutive estimated glomerular filtration rate (eGFR) decline. The present study aimed to determine the association between uDKK3 levels and further eGFR changes in patients with resistant HTN. In total, 31 patients with resistant HTN were included. Blood pressure and renal function were measured at baseline and up to 24 months after (at months 12 and 24). uDKK3 levels were determined exclusively from the first available spot urine sample at baseline or up to a period of 6 months after, using a commercial ELISA kit. Distinctions between different patient groups were analyzed using the unpaired t-test or Mann-Whitney test. Correlation analysis was performed using Spearman's correlation. The median uDKK3 level was 303 (interquartile range (IQR) 150-865) pg/mg creatinine. Patients were divided into those with high and low eGFR loss (≥3 vs. <3 mL/min/1.73 m²/year). Patients with high eGFR loss showed a significantly higher median baseline uDKK3 level (646 (IQR 249-2555) (n = 13) vs. 180 (IQR 123-365) pg/mg creatinine (n = 18), p = 0.0412 (Mann-Whitney U)). Alternatively, patients could be classified into those with high and low uDKK3 levels (≥400 vs. <400 pg/mg creatinine). Patients with high uDKK3 levels showed significantly higher eGFR loss (-6.4 ± 4.7 (n = 11) vs. 0.0 ± 7.6 mL/min/1.73 m²/year (n = 20), p = 0.0172 (2-sided, independent t-test)). Within the entire cohort, there was a significant correlation between the uDKK3 levels and change in eGFR at the latest follow-up (Spearman's r = -0.3714, p = 0.0397). In patients with resistant HTN, high levels of uDKK3 are associated with higher eGFR loss up to 24 months later.

Diagnosis, pathophysiology and preventive strategies for cardiac surgery-associated acute kidney injury: a narrative review

Acute kidney injury (AKI) is a common and serious complication of cardiac surgery and is associated with increased mortality and morbidity, accompanied by a substantial economic burden. The pathogenesis of cardiac surgery-associated acute kidney injury (CSA-AKI) is multifactorial and complex, with a variety of pathophysiological theories. In addition to the existing diagnostic criteria, the exploration and validation of biomarkers is the focus of research in the field of CSA-AKI diagnosis. Prevention remains the key to the management of CSA-AKI, and common strategies include maintenance of renal perfusion, individualized blood pressure targets, balanced fluid management, goal-directed oxygen delivery, and avoidance of nephrotoxins. This article reviews the pathogenesis, definition and diagnosis, and pharmacological and nonpharmacological prevention strategies of AKI in cardiac surgical patients.

Artificial Intelligence and Machine Learning in Perioperative Acute Kidney Injury

Acute kidney injury (AKI) is a common complication after a surgery, especially in cardiac and aortic procedures, and has a significant impact on morbidity and mortality. Early identification of high-risk patients and providing effective prevention and therapeutic approach are the main strategies for reducing the possibility of perioperative AKI. Consequently, several risk-prediction models and risk assessment scores have been developed for the prediction of perioperative AKI. However, a majority of these risk scores are only derived from preoperative data while the intraoperative time-series monitoring data such as heart rate and blood pressure were not included. Moreover, the complexity of the pathophysiology of AKI, as well as its nonlinear and heterogeneous nature, imposes limitations on the use of linear statistical techniques. The development of clinical medicine's digitization, the widespread availability of electronic medical records, and the increase in the use of continuous monitoring have generated vast quantities of data. Machine learning has recently shown promise as a method for automatically integrating large amounts of data in predicting the risk of perioperative outcomes. In this article, we discussed the development, limitations of existing work, and the potential future direction of models using machine learning techniques to predict AKI after a surgery.

Dickkopf-3: An Update on a Potential Regulator of the Tumor Microenvironment

Dickkopf-3 (Dkk-3) is a member of the Dickkopf family protein of secreted Wingless-related integration site (Wnt) antagonists that appears to modulate regulators of the host microenvironment. In contrast to the clear anti-tumorigenic effects of Dkk-3-based gene therapies, the role of endogenous Dkk-3 in cancer is context-dependent, with elevated expression associated with tumor promotion and suppression in different settings. The receptors and effectors that mediate the diverse effects of Dkk-3 have not been characterized in detail, contributing to an ongoing mystery of its mechanism of action. This review compares the various functions of Dkk-3 in the tumor microenvironment, where Dkk-3 has been found to be expressed by subpopulations of fibroblasts, endothelial, and immune cells, in addition to epithelial cells. We also discuss how the activation or inhibition of Dkk-3, depending on tumor type and context, might be used to treat different types of cancers.

Postoperative Acute Kidney Injury

Postoperative AKI is a common complication of major surgery and is associated with significant morbidity and mortality. The Kidney Disease Improving Global Outcomes AKI definition allows consensus classification and identification of postoperative AKI through changes in serum creatinine and/or urine output. However, such conventional diagnostic criteria may be inaccurate in the postoperative period, suggesting a potential to refine diagnosis by application of novel diagnostic biomarkers. Risk factors for the development of postoperative AKI can be thought of in terms of preoperative, intraoperative, and postoperative factors and, as such, represent areas that may be targeted perioperatively to minimize the risk of AKI. The treatment of postoperative AKI remains predominantly supportive, although application of management bundles may translate into improved outcomes.

Machine learning for the prediction of acute kidney injury in patients after cardiac surgery

Cardiac surgery-associated acute kidney injury (CSA-AKI) is the most prevalent major complication of cardiac surgery and exerts a negative effect on a patient's prognosis, thereby leading to mortality. Although several risk assessment models have been developed for patients undergoing cardiac surgery, their performances are unsatisfactory. In this study, a machine learning algorithm was employed to obtain better predictive power for CSA-AKI outcomes relative to statistical analysis. In addition, random forest (RF), logistic regression with LASSO regularization, extreme gradient boosting (Xgboost), and support vector machine (SVM) methods were employed for feature selection and model training. Moreover, the calibration capacity and differentiation ability of the model was assessed using net reclassification improvement (NRI) along with Brier scores and receiver operating characteristic (ROC) curves, respectively. A total of 44 patients suffered AKI after surgery. Fatty acid-binding protein (FABP), hemojuvelin (HJV), neutrophil gelatinase-associated lipocalin (NGAL), mechanical ventilation time, and troponin I (TnI) were correlated significantly with the incidence of AKI. RF was the best model for predicting AKI (Brier score: 0.137, NRI: 0.221), evidenced by an AUC value of 0.858 [95% confidence interval (CI): 0.792–0.923]. Overall, RF exhibited the best performance as compared to other machine learning algorithms. These results thus provide new insights into the early identification of CSA-AKI.

Consensus-Based Recommendations on Priority Activities to Address Acute Kidney Injury in Children: A Modified Delphi Consensus Statement

IMPORTANCE

Increasing evidence indicates that acute kidney injury (AKI) occurs frequently in children and young adults and is associated with poor short-term and long-term outcomes. Guidance is required to focus efforts related to expansion of pediatric AKI knowledge.

OBJECTIVE

To develop expert-driven pediatric specific recommendations on needed AKI research, education, practice, and advocacy.

EVIDENCE REVIEW

At the 26th Acute Disease Quality Initiative meeting conducted in November 2021 by 47 multiprofessional international experts in general pediatrics, nephrology, and critical care, the panel focused on 6 areas: (1) epidemiology; (2) diagnostics; (3) fluid overload; (4) kidney support therapies; (5) biology, pharmacology, and nutrition; and (6) education and advocacy. An objective scientific review and distillation of literature through September 2021 was performed of (1) epidemiology, (2) risk assessment and diagnosis, (3) fluid assessment, (4) kidney support and extracorporeal therapies, (5) pathobiology, nutrition, and pharmacology, and (6) education and advocacy. Using an established modified Delphi process based on existing data, workgroups derived consensus statements with recommendations.

FINDINGS

The meeting developed 12 consensus statements and 29 research recommendations. Principal suggestions were to address gaps of knowledge by including data from varying socioeconomic groups, broadening definition of AKI phenotypes, adjudicating fluid balance by disease severity, integrating biopathology of child growth and development, and partnering with families and communities in AKI advocacy.

CONCLUSIONS AND RELEVANCE

Existing evidence across observational study supports further efforts to increase knowledge related to AKI in childhood. Significant gaps of knowledge may be addressed by focused efforts.

Biomarkers for early detection and predicting outcomes in acute kidney injury

The current diagnosis of acute kidney injury relies on the measurement of serum creatinine levels and urine output. However, both measures are subject to considerable limitations; for example, change in serum creatinine levels ideally requires a knowledge of baseline function that is often not available. Furthermore, creatinine levels are influenced by many factors including diet, drug therapy, muscle mass, gender and ethnicity, which may lead to underestimation of the extent of renal dysfunction. Similarly, urine output lacks both specificity and sensitivity as a marker of acute kidney injury given that oliguria may be an appropriate physiological response to a multitude of stressors and that output may be maintained until significant renal damage has already occurred.

Given the well-documented consequences of acute kidney injury and the considerable burden associated with its development, much attention has focused on early identification of patients at high risk to try and improve outcomes. Many studies have focused on the identification of candidate molecules that may enable the early detection of individuals at risk of developing acute kidney injury, including constitutive proteins associated with kidney damage, as well as molecules upregulated in response to injury, non-renal products that may be filtered, reabsorbed or secreted by the kidney, and markers of renal stress. Such biomarkers may also aid stratification for adverse events, such as the need for kidney replacement therapy or progression to chronic kidney disease and end-stage kidney disease. This article discusses some of these novel biomarkers and assesses the role they may have in the understanding, management, diagnosis and prognostication of acute kidney injury.

Advances in laboratory detection of acute kidney injury

Recent advances have improved our understanding of the epidemiology and pathophysiology of acute kidney injury (AKI). So far, the Kidney Disease: Improving Global Outcome guidelines define and stratify kidney injury based on increases in serum creatinine level and/or decreases in urine output. Although the term AKI acknowledges the existence of cellular injury, its diagnosis is still only defined by the reduced excretory function of the kidney. New biomarkers that aid a better understanding of the relationship between acute tubular injury and kidney dysfunction have been identified, reflecting the advances in molecular biology. The expression of some of these novel biomarkers precedes changes in conventional biomarkers or can increase their predictive power. Therefore, they might enhance the clinical accuracy of the definition of AKI. This review summarizes the limitations of the current AKI classification and a panel of candidate biomarkers for augmenting AKI classification and recognition of AKI subphenotypes. We expect that the integration of appropriately selected biomarkers in routine clinical practice can improve AKI care.

Predictive Value of Glycosylated Hemoglobin for Post-operative Acute Kidney Injury in Non-cardiac Surgery Patients

Objective

Recent studies have indicated that patients (both with and without diabetes) with elevated hemoglobin A1c (HbA1c) have a higher rate of acute kidney injury (AKI) following cardiac surgery. However, whether HbA1c could help to predict post-operative AKI in patients after non-cardiac surgery is less clear. This study aims to explore the predictive value of pre-operative HbA1c for post-operative AKI in non-cardiac surgery.

Methods

We reviewed the medical records of patients (≥ 18 years old) who underwent non-cardiac surgery between 2011 and 2020. Patient-related variables, including demographic and laboratory and procedure-related information, were collected, and univariable and multivariable logistic regression analyses were performed to determine the association of HbA1c with AKI. The area under the receiver operating curve (AUC), net reclassification improvement index (NRI), and integrated discriminant improvement index (IDI) were used to evaluate the predictive ability of the model, and decision curve analysis was used to evaluate the clinical utility of the HbA1c-added predictive model.

Results

A total of 3.3% of patients (94 of 2,785) developed AKI within 1 week after surgery. Pre-operative HbA1c was an independent predictor of AKI after adjustment for some clinical variables (OR comparing top to bottom quintiles 5.02, 95% CI, 1.90 to 13.24, P < 0.001 for trend; OR per percentage point increment in HbA1c 1.20, 95% CI, 1.07 to 1.33). Compared to the model with only clinical variables, the incorporation of HbA1c increased the model fit, modestly improved the discrimination (change in area under the curve from 0.7387 to 0.7543) and reclassification (continuous net reclassification improvement 0.2767, 95% CI, 0.0715 to 0.4818, improved integrated discrimination 0.0048, 95% CI, -5e-04 to 0.0101) of AKI and non-AKI cases, NRI for non-AKI improvement 0.3222, 95% CI, 0.2864 to 0.3580 and achieved a higher net benefit in decision curve analysis.

Conclusion

Elevated pre-operative HbA1c was independently associated with post-operative AKI risk and provided predictive value in patients after non-cardiac surgery. HbA1c improved the predictive power of a logistic regression model based on traditional clinical risk factors for AKI. Further prospective studies are needed to demonstrate the results and clinical application.

Biochemical interaction of pyrvinium in gentamicin-induced acute kidney injury by modulating calcium dyshomeostasis and mitochondrial dysfunction

Acute kidney injury (AKI) has a poor clinical prognosis and increases the risk of chronic kidney failure (CKD). It is a common complication of organ failure in hospitalised patients (10-15% of all hospitalizations) and in intensive care unit (ICU) patients, with an incidence of up to 50%. Concerning ICU, AKI has a mortality rate ranging from 27% to 35%, rising to 60%-65% when dialysis is needed, with roughly 5%-20% of survivors requiring dialysis on discharge. AKI is believed to cause over 7 million deaths per year worldwide. Currently, there is no treatment for AKI or its progression to CKD. When activated by AKI, numerous pathways have been suggested as possible contributors to CKD progression. Wnt/β-catenin is a crucial regulator of kidney development that increases following the injury. Despite the overwhelming evidence that Wnt/β-catenin promotes AKI, tubulointerstitial fibrosis, a hallmark of CKD progression, is also promoted by this pathway. The therapeutic potential of Wnt/β-catenin in the treatment of AKI and the progression from AKI to CKD is being studied. This hypothesis aims to determine whether the Wnt/β-catenin inhibitor pyrvinium has a beneficial effect on the renal dysfunction and damage caused by Gentamicin.

Urinary extracellular vesicles and micro-RNA as markers of acute kidney injury after cardiac surgery

We hypothesised that measuring changes in urinary levels of EV and miR will predict the onset of acute kidney injury in cardiac surgery patients. The study was performed in the cohort of the REVAKI-2 trial. Urine samples were collected before and 24 h after the procedure from 94 cardiac surgery patients. Urinary particle concentrations and size distribution were assessed using NanoSight. EV derivation and levels were measured using flow cytometry. Samples from 10 selected patients were sequenced, and verification was performed with advanced TaqMan assays in samples from all patients. Urinary particle concentrations significantly increased in patients with AKI after surgery, with the percentage of EV positive for CD105 and β1-integrin also increasing. Pre-surgery podocalyxin-positive EV were significantly lower in patients with AKI. Their levels correlated with the severity of the injury. Pre-operative miR-125a-5p was expressed at lower levels in urine from patients with AKI when adjusted for urinary creatinine. Levels of miR-10a-5p were lower after surgery in AKI patients and its levels correlated with the severity of the injury. Pre-operative levels of podocalyxin EVs, urinary particle concentrations and miR-125a-5p had moderate AKI predictive value and, in a logistic model together with ICU lactate levels, offered good (AUC = 82%) AKI prediction.

The Predictive Value of Pre-operative N-Terminal Pro-B-Type Natriuretic Peptide in the Risk of Acute Kidney Injury After Non-cardiac Surgery

Objective

To evaluate the association between N-terminal pro-B-type natriuretic peptide (NT-proBNP) and risk of post-operative acute kidney injury (PO-AKI).

Methods

The electronic medical records and laboratory results were obtained from 3,949 adult patients (≥18 years) undergoing non-cardiac surgery performed between 1 October 2012 to 1 October 2019 at the Third Xiangya Hospital, Central South University, China. Collected data were analyzed retrospectively.

Results

In all, 5.3% (209 of 3,949) of patients developed PO-AKI. Pre-operative NT-proBNP was an independent predictor of PO-AKI. After adjustment for significant variables, OR for AKI of highest and lowest NT-proBNP quintiles was 1.96 (95% CI, 1.04–3.68, P = 0.008), OR per 1-unit increment in natural log transformed NT-proBNP was 1.20 (95% CI, 1.09–1.32, P < 0.001). Compared with clinical variables alone, the addition of NT-proBNP modestly improved the discrimination [change in area under the curve(AUC) from 0.82 to 0.83, ΔAUC=0.01, P = 0.024] and the reclassification (continuous net reclassification improvement 0.15, 95% CI, 0.01–0.29, P = 0.034, improved integrated discrimination 0.01, 95% CI, 0.002–0.02, P = 0.017) of AKI and non-AKI cases.

Conclusions

Results from our retrospective cohort study showed that the addition of pre-operative NT-proBNP concentrations could better predict post-operative AKI in a cohort of non-cardiac surgery patients and achieve higher net benefit in decision curve analysis.

Dickkopf 3-A New Indicator for the Deterioration of Allograft Function After Kidney Transplantation

Evidence of tubular atrophy and interstitial fibrosis is prognostically unfavorable and associated with a premature graft loss after kidney transplantation. Recently, Dickkopf 3 (DKK3), a profibrotic glycoprotein released by stressed tubular epithelial cells, has been identified to cause IF/TA by regulating the Wnt/β-catenin signaling and seems to engage a T-cell response. The aim of our study was to determine if a correlation between DKK3 and graft function exists and if DKK3 could be a new indicator to identify patients at risk for a deterioration in graft function. Patients, transplanted between 2016 and 2018, were analyzed with regard to DKK3 in the urine and graft function (creatinine, eGFR, albuminuria). Multivariable analyzes were used including known factors influencing graft function (PRA, donor age) to stress robustness of DKK3. The 3 and 12 month DKK3 values were significant predictors for subsequent graft function up to 36 months. An increase of DKK3 from month 3 to 12 of ≥ 25% showed a higher risk of an impaired graft function, with, e.g., a reduction in eGFR of about 9–10 ml/min in contrast to patients without intensified DKK3 increase. Induction therapy has an influence on DKK3 as patients induced with a T-cell depleting therapy showed a trend toward lower DKK3 values. In summary, our study is the first investigation of DKK3 in kidney transplant recipients and was able to show that DKK3 could forecast graft function. It is recommended to investigate the potential of DKK3 as a predictor of kidney function after transplantation in further studies.

Overview of Diagnostic Criteria and Epidemiology of Acute Kidney Injury and Acute Kidney Disease in the Critically Ill Patient

Since the description ischuria renalis by William Heberden (1), AKI has remained a prominent complication of critical illness. Beyond KRT, treatment has been limited by the capacity to phenotype this condition. Here, we chronicle the evolution of attempts to classify AKI, including the adoption of consensus definitions, the expansion of diagnosis and prognosis with novel biomarkers, and emerging tools such as artificial intelligence (AI).

Prediction of acute kidney injury after cardiac surgery: model development using a Chinese electronic health record dataset

Background

Acute kidney injury (AKI) is a major complication following cardiac surgery that substantially increases morbidity and mortality. Current diagnostic guidelines based on elevated serum creatinine and/or the presence of oliguria potentially delay its diagnosis. We presented a series of models for predicting AKI after cardiac surgery based on electronic health record data.

Methods

We enrolled 1457 adult patients who underwent cardiac surgery at Nanjing First Hospital from January 2017 to June 2019. 193 clinical features, including demographic characteristics, comorbidities and hospital evaluation, laboratory test, medication, and surgical information, were available for each patient. The number of important variables was determined using the sliding windows sequential forward feature selection technique (SWSFS). The following model development methods were introduced: extreme gradient boosting (XGBoost), random forest (RF), deep forest (DF), and logistic regression. Model performance was accessed using the area under the receiver operating characteristic curve (AUROC). We additionally applied SHapley Additive exPlanation (SHAP) values to explain the RF model. AKI was defined according to Kidney Disease Improving Global Outcomes guidelines.

Results

In the discovery set, SWSFS identified 16 important variables. The top 5 variables in the RF importance matrix plot were central venous pressure, intraoperative urine output, hemoglobin, serum potassium, and lactic dehydrogenase. In the validation set, the DF model exhibited the highest AUROC (0.881, 95% confidence interval [CI] 0.831–0.930), followed by RF (0.872, 95% CI 0.820–0.923) and XGBoost (0.857, 95% CI 0.802–0.912). A nomogram model was constructed based on intraoperative longitudinal features, achieving an AUROC of 0.824 (95% CI 0.763–0.885) in the validation set. The SHAP values successfully illustrated the positive or negative contribution of the 16 variables attributed to the output of the RF model and the individual variable’s effect on model prediction.

Conclusions

Our study identified 16 important predictors and provided a series of prediction models to enhance risk stratification of AKI after cardiac surgery. These novel predictors might aid in choosing proper preventive and therapeutic strategies in the perioperative management of AKI patients.

Molecular Mechanisms and Biomarkers Associated with Chemotherapy-Induced AKI

Acute kidney injury (AKI) is a life-threatening condition characterized by a rapid and transient decrease in kidney function. AKI is part of an array of conditions collectively defined as acute kidney diseases (AKD). In AKD, persistent kidney damage and dysfunction lead to chronic kidney disease (CKD) over time. A variety of insults can trigger AKI; however, chemotherapy-associated nephrotoxicity is increasingly recognized as a significant side effect of chemotherapy. New biomarkers are urgently needed to identify patients at high risk of developing chemotherapy-associated nephrotoxicity and subsequent AKI. However, a lack of understanding of cellular mechanisms that trigger chemotherapy-related nephrotoxicity has hindered the identification of effective biomarkers to date. In this review, we aim to (1) describe the known and potential mechanisms related to chemotherapy-induced AKI; (2) summarize the available biomarkers for early AKI detection, and (3) raise awareness of chemotherapy-induced AKI.