Cystatin C compared to serum creatinine as a marker of acute kidney injury in critically ill neonates

Novel acute kidney injury biomarkers and their utility in children and adolescents-overview

Acute kidney injury (AKI) affects a significant percentage of the pediatric population. Currently, the diagnosis of AKI in children still uses traditional laboratory methods (ex. creatinine or urea serum concentration and measurement of urine output). It has significant limitations. Early stages of AKI in children may be almost asymptomatic. In-depth assessment with the pRIFLE scale is helpful, but requires bladder catheterization and precise monitoring of hourly diuresis, as well as multiple blood draws to determine changes in creatinine concentration and estimate glomerular filtration rate (eGFR). The diagnostic methods lack a marker that would the early and potentially reversible phase of kidney damage. This paper reviews recent data on selected AKI markers in children, including their diagnostic and prognostic potential.

Artificial intelligence in early detection and prediction of pediatric/neonatal acute kidney injury: current status and future directions

Acute kidney injury (AKI) has a significant impact on the short-term and long-term clinical outcomes of pediatric and neonatal patients, and it is imperative in these populations to mitigate the pathways leading to AKI and be prepared for early diagnosis and treatment intervention of established AKI. Recently, artificial intelligence (AI) has provided more advent predictive models for early detection/prediction of AKI utilizing machine learning (ML). By providing strong detail and evidence from risk scores and electronic alerts, this review outlines a comprehensive and holistic insight into the current state of AI in AKI in pediatric/neonatal patients. In the pediatric population, AI models including XGBoost, logistic regression, support vector machines, decision trees, naïve Bayes, and risk stratification scores (Renal Angina Index (RAI), Nephrotoxic Injury Negated by Just-in-time Action (NINJA)) have shown success in predicting AKI using variables like serum creatinine, urine output, and electronic health record (EHR) alerts. Similarly, in the neonatal population, using the "Baby NINJA" model showed a decrease in nephrotoxic medication exposure by 42%, the rate of AKI by 78%, and the number of days with AKI by 68%. Furthermore, the "STARZ" risk stratification AI model showed a predictive ability of AKI within 7 days of NICU admission of AUC 0.93 and AUC of 0.96 in the validation and derivation cohorts, respectively. Many studies have reported the superiority of using biomarkers to predict AKI in pediatric patients and neonates as well. Future directions include the application of AI along with biomarkers (NGAL, CysC, OPN, IL-18, B2M, etc.) in a Labelbox configuration to create a more robust and accurate model for predicting and detecting pediatric/neonatal AKI.

An early warning model to predict acute kidney injury in sepsis patients with prior hypertension

Background

In the context of sepsis patients, hypertension has a significant impact on the likelihood of developing sepsis-associated acute kidney injury (S-AKI), leading to a considerable burden. Moreover, sepsis is responsible for over 50 % of cases of acute kidney injuries (AKI) and is linked to an increased likelihood of death during hospitalization. The objective of this research is to develop a dependable and strong nomogram framework, utilizing the variables accessible within the first 24 h of admission, for the anticipation of S-AKI in sepsis patients who have hypertension.

Methods

In this study that looked back, a total of 462 patients with sepsis and high blood pressure were identified from Nanfang Hospital. These patients were then split into a training set (consisting of 347 patients) and a validation set (consisting of 115 patients). A multivariate logistic regression analysis and a univariate logistic regression analysis were performed to identify the factors that independently predict S-AKI. Based on these independent predictors, the model was constructed. To evaluate the efficacy of the designed nomogram, several analyses were conducted, including calibration curves, receiver operating characteristics curves, and decision curve analysis.

Results

The findings of this research indicated that diabetes, prothrombin time activity (PTA), thrombin time (TT), cystatin C, creatinine (Cr), and procalcitonin (PCT) were autonomous prognosticators for S-AKI in sepsis individuals with hypertension. The nomogram model, built using these predictors, demonstrated satisfactory discrimination in both the training (AUC = 0.823) and validation (AUC = 0.929) groups. The S-AKI nomogram demonstrated superior predictive ability in assessing S-AKI within the hypertension grade I (AUC = 0.901) set, surpassing the hypertension grade II (AUC = 0.816) and III (AUC = 0.810) sets. The nomogram exhibited satisfactory calibration and clinical utility based on the calibration curve and decision curve analysis.

Conclusion

In patients with sepsis and high blood pressure, the nomogram that was created offers a dependable and strong evaluation for predicting S-AKI. This evaluation provides valuable insights to enhance individualized treatment, ultimately resulting in improved clinical outcomes.

Gut microbiota and neonatal acute kidney injury biomarkers

One of the most frequent issues in newborns is acute kidney injury (AKI), which can lengthen their hospital stay or potentially raise their chance of dying. The gut-kidney axis establishes a bidirectional interplay between gut microbiota and kidney illness, particularly AKI, and demonstrates the importance of gut microbiota to host health. Since the ability to predict neonatal AKI using blood creatinine and urine output as evaluation parameters is somewhat constrained, a number of interesting biomarkers have been developed. There are few in-depth studies on the relationships between these neonatal AKI indicators and gut microbiota. In order to gain fresh insights into the gut-kidney axis of neonatal AKI, this review is based on the gut-kidney axis and describes relationships between gut microbiota and neonatal AKI biomarkers.

Single-cell sequencing reveals homogeneity and heterogeneity of the cytopathological mechanisms in different etiology-induced AKI

Homogeneity and heterogeneity of the cytopathological mechanisms in different etiology-induced acute kidney injury (AKI) are poorly understood. Here, we performed single-cell sequencing (scRNA) on mouse kidneys with five common AKI etiologies (CP-Cisplatin, IRI-Ischemia-reperfusion injury, UUO-Unilateral ureteral obstruction, FA-Folic acid, and SO-Sodium oxalate). We constructed a potent multi-model AKI scRNA atlas containing 20 celltypes with 80,689 high-quality cells. The data suggest that compared to IRI and CP-AKI, FA- and SO-AKI exhibit injury characteristics more similar to UUO-AKI, which may due to tiny crystal-induced intrarenal obstruction. Through scRNA atlas, 7 different functional proximal tubular cell (PTC) subtypes were identified, we found that Maladaptive PTCs and classical Havcr1 PTCs but not novel Krt20 PTCs affect the pro-inflammatory and pro-fibrotic levels in different AKI models. And cell death and cytoskeletal remodeling events are widespread patterns of injury in PTCs. Moreover, we found that programmed cell death predominated in PTCs, whereas apoptosis and autophagy prevailed in the remaining renal tubules. We also identified S100a6 as a novel AKI-endothelial injury biomarker. Furthermore, we revealed that the dynamic and active immune (especially Arg1 Macro_2 cells) -parenchymal cell interactions are important features of AKI. Taken together, our study provides a potent resource for understanding the pathogenesis of AKI and early intervention in AKI progression at single-cell resolution.

Construction and validation of an early warning model for predicting the acute kidney injury in elderly patients with sepsis

BACKGROUND

Sepsis-induced acute kidney injury (S-AKI) is a significant complication and is associated with an increased risk of mortality, especially in elderly patients with sepsis. However, there are no reliable and robust predictive models to identify high-risk patients likely to develop S-AKI. We aimed to develop a nomogram to predict S-AKI in elderly sepsis patients and help physicians make personalized management within 24 h of admission.

METHODS

A total of 849 elderly sepsis patients from the First Affiliated Hospital of Xi'an Jiaotong University were identified and randomly divided into a training set (75%, n = 637) and a validation set (25%, n = 212). Univariate and multivariate logistic regression analyses were performed to identify the independent predictors of S-AKI. The corresponding nomogram was constructed based on those predictors. The calibration curve, receiver operating characteristics (ROC)curve, and decision curve analysis were performed to evaluate the nomogram. The secondary outcome was 30-day mortality and major adverse kidney events within 30 days (MAKE30). MAKE30 were a composite of death, new renal replacement therapy (RRT), or persistent renal dysfunction (PRD).

RESULTS

The independent predictors for nomogram construction were mean arterial pressure (MAP), serum procalcitonin (PCT), and platelet (PLT), prothrombin time activity (PTA), albumin globulin ratio (AGR), and creatinine (Cr). The predictive model had satisfactory discrimination with an area under the curve (AUC) of 0.852-0.858 in the training and validation cohorts, respectively. The nomogram showed good calibration and clinical application according to the calibration curve and decision curve analysis. Furthermore, the prediction model had perfect predictive power for predicting 30-day mortality (AUC = 0.813) and MAKE30 (AUC = 0.823) in elderly sepsis patients.

CONCLUSION

The proposed nomogram can quickly and effectively predict S-AKI risk in elderly sepsis patients within 24 h after admission, providing information for clinicians to make personalized interventions.

The Monocyte-to-Lymphocyte Ratio Predicts Acute Kidney Injury After Acute Hemorrhagic Stroke

Objectives

Acute kidney injury (AKI) is a serious complication of acute hemorrhagic stroke (AHS). Early detection and early treatment are crucial for patients with AKI. We conducted a study to analyze the role of the monocyte-to-lymphocyte ratio (MLR) in predicting the development of AKI after AHS.

Methods

This retrospective observational study enrolled all subjects with AHS who attended the neurosurgical intensive care unit (NSICU) at the First Affiliated University of South China between 2018 and 2021. Patient demographics, laboratory data, treatment details, and clinical outcomes were recorded.

Results

Of the 771 enrolled patients, 180 (23.3%) patients developed AKI. Compared to patients without AKI, those with AKI had a higher MLR and the neutrophil-lymphocyte ratio (NLR) at admission (P < 0.001). The MLR and the NLR at admission were associated with an increased AKI risk, with odds ratios (ORs) of 8.27 (95% CI: 4.23, 16.17, p < 0.001) and 1.17 (95% CI: 1.12, 1.22, p < 0.001), respectively. The receiver operating characteristic curve (ROC) analysis was conducted to analyze the ability of the MLR and NLR to predict AKI, and the areas under the curve (AUCs) of the MLR and the NLR were 0.73 (95% CI: 0.69, 0.77, p < 0.001) and 0.67 (95% CI: 0.62, 0.72, p < 0.001), with optimal cutoff values of 0.5556 and 11.65, respectively. The MLR and the NLR at admission were associated with an increased in-hospital mortality risk, with ORs of 3.13 (95% CI: 1.08, 9.04) and 1.07 (95% CI: 1.00, 1.14), respectively. The AUCs of the MLR and the NLR for predicting in-hospital mortality were 0.62 (95% CI: 0.54, 0.71, p = 0.004) and 0.52 (95% CI: 0.43, 0.62, p = 0.568), respectively. The optimal cutoff value for the MLR was 0.7059, with a sensitivity of 51% and a specificity of 73.3%.

Conclusions

MLR and NLR measurements in patients with AHS at admission could be valuable tools for identifying patients at high risk of early AKI. The MLR was positively associated with in-hospital mortality and the NLR showed a weak ability for the prediction of in-hospital mortality.

Serum Cystatin C as a predictor of acute kidney injury in neonates: a meta-analysis

OBJECTIVES

The objective of this meta-analysis is to evaluate the diagnostic value of serum Cystatin C in acute kidney injury (AKI) in neonates.

SOURCES

PubMed, Embase, Cochrane Library, Web of Science, China National Knowledge Infrastructure (CNKI), and WanFang Database were searched to retrieve the literature related to the diagnostic value of Cystatin C for neonatal AKI from inception to May 10, 2021. Subsequently, the quality of included studies was determined using the QUADAS-2 tool. Stata 15.0 statistical software was used to calculate the combined sensitivity (SEN), specificity (SPE), positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR). Additionally, meta-regression analysis and subgroup analysis contributed to explore the sources of heterogeneity.

SUMMARY OF THE FINDINGS

Twelve articles were included. The pooled sensitivity was 0.84 (95%CI: 0.74-0.91), the pooled specificity was 0.81 (95%CI: 0.75-0.86), the pooled PLR was 4.39 (95%CI: 3.23-5.97), the pooled NLR was 0.19 (95%CI: 0.11-0.34), and the DOR was 22.58 (95%CI: 10.44-48.83). The area under the receiver operating characteristic curve (AUC) was 0.88 (95%CI: 0.85-0.90). No significant publication bias was identified (p > 0.05).

CONCLUSIONS

Serum Cystatin C has a good performance in predicting neonatal AKI; therefore, it can be used as a candidate biomarker after the optimal level is determined by large prospective studies.

Neonatal Acute Kidney Injury

Acute kidney injury (AKI) is a common occurrence in the neonatal intensive care unit (NICU). In recent years, our knowledge of the incidence and impact of neonatal AKI on outcomes has expanded exponentially. Neonatal AKI has been shown to be associated with adverse outcomes including increased length of mechanical ventilation, prolonged length of stay, and rise in mortality. There has also been increasing work suggesting that neonates with AKI are at higher risk of chronic kidney disease (CKD). In the past, AKI had been defined multiple ways. The utilization of the neonatal modified Kidney Disease: Improving Global Outcomes (KDIGO) criteria as the standard definition for neonatal AKI in research and clinical care has driven the advances in our understanding of neonatal AKI over the last 10 years. This definition has allowed researchers and clinicians to better understand the incidence, risk factors, and outcomes associated with neonatal AKI across populations through a multitude of single-center studies and the seminal, multicenter Assessment of Worldwide Acute Kidney Injury Epidemiology in Neonates (AWAKEN) study. As the impacts of neonatal AKI have become clear, a shift in efforts toward identifying those at highest risk, protocolizing AKI surveillance, improving prevention and diagnosis, and expanding kidney support therapy (KST) for neonates has occurred. These efforts also include improving risk stratification (identifying high risk populations, including those with nephrotoxic medication exposure) and diagnostics (novel biomarkers and diagnostic tools). Recent work has also shown that the targeted use of methylxanthines may prevent AKI in a variety of high-risk populations. One of the most exciting developments in neonatal AKI is the advancement in technology to provide KST to neonates with severe AKI. In this comprehensive review we will provide an overview of recent work and advances in the field of neonatal AKI. This will include a detailed review of (1) the definition of neonatal AKI, (2) the epidemiology, risk factors, and outcomes associated with neonatal AKI, (3) improvements in risk stratification and diagnostics, (4) mitigation and treatment, (5) advancements in the provision of KST to neonates, and (6) the incidence and risk of subsequent CKD.

Predictive nomogram model for major adverse kidney events within 30 days in sepsis patients with type 2 diabetes mellitus

BACKGROUND

In sepsis patients, Type 2 Diabetes Mellitus (T2DM) was associated with an increased risk of kidney injury. Furthermore, kidney damage is among the dangerous complications, with a high mortality rate in sepsis patients. However, the underlying predictive model on the prediction of major adverse kidney events within 30 days (MAKE30) in sepsis patients with T2DM has not been reported by any study.

METHODS

A total of 406 sepsis patients with T2DM were retrospectively enrolled and divided into a non-MAKE30 group (261 cases) and a MAKE30 group (145 cases). In sepsis patients with T2DM, univariate and multivariate logistic regression analyses were conducted to identify independent predictors of MAKE30. Based on the findings of multivariate logistic regression analysis, the corresponding nomogram was constructed. The nomogram was evaluated using the calibration curve, Receiver Operating Characteristic (ROC) curve, and decision curve analysis. A composite of death, new Renal Replacement Therapy (RRT), or Persistent Renal Dysfunction (PRD) comprised MAKE30. Finally, subgroup analyses of the nomogram for 30-day mortality, new RRT, and PRD were performed.

RESULTS

In sepsis patients with T2DM, Mean Arterial Pressure (MAP), Platelet (PLT), cystatin C, High-Density Lipoprotein (HDL), and apolipoprotein E (apoE) were independent predictors for MAKE30. According to the ROC curve, calibration curve, and decision curve analysis, the nomogram model based on those predictors had satisfactory discrimination (AUC = 0.916), good calibration, and clinical application. Additionally, in sepsis patients with T2DM, the nomogram model exhibited a high ability to predict the occurrence of 30-day mortality (AUC = 0.822), new RRT (AUC = 0.874), and PRD (AUC = 0.801).

CONCLUSION

The nomogram model, which is available within 24 hours after admission, had a robust and accurate assessment for the MAKE30 occurrence, and it provided information to better manage sepsis patients with T2DM.

The use of diagnostic tools for pediatric AKI: applying the current evidence to the bedside

Given the known deleterious consequences of acute kidney injury (AKI), exciting recent research efforts have focused on developing strategies for the earlier recognition of AKI in the pediatric population. Recognizing the limitations of serum creatinine, investigators have focused on the study of novel biomarkers and practical bedside tools for identifying patients at risk for AKI prior to a rise in serum creatinine. In PubMed, there are presently over 30 original research papers exploring the use of pediatric AKI risk prediction tools in just the last 2 years. The following review highlights the most recent advances in the literature regarding opportunities to refine our ability to detect AKI early. Importantly, this review discusses how prediction tools including novel urine and serum biomarkers, practical risk stratification tests, renal functional reserve, and electronic medical record alerts may ultimately be applied to routine clinical practice.

Machine learning applied to serum and cerebrospinal fluid metabolomes revealed altered arginine metabolism in neonatal sepsis with meningoencephalitis

Background

Neonatal sepsis with meningoencephalitis is a common complication of sepsis, which is a leading cause of neonatal death and neurological dysfunction. Early identification of neonatal sepsis with meningoencephalitis is particularly important for reducing brain damage. We recruited 70 patients with neonatal sepsis, 42 of which were diagnosed as meningoencephalitis, and collected cerebrospinal fluid (CSF) and serum samples. The purpose of this study was to find neonatal sepsis with meningoencephalitis-related markers using unbiased metabolomics technology and artificial intelligence analysis based on machine learning methods.

Results

We found that the characteristics of neonatal sepsis with meningoencephalitis were manifested mainly as significant decreases in the concentrations of homo-l-arginine, creatinine, and other arginine metabolites in serum and CSF, suggesting possible changes in nitric oxide synthesis. The antioxidants taurine and proline in the serum of the neonatal sepsis with meningoencephalitis increased significantly, suggesting abnormal oxidative stress. Potentially harmful bile salts and aromatic compounds were significantly increased in the serum of the group with meningoencephalitis. We compared different machine learning methods and found that the lasso algorithm performed best. Combining the lasso and XGBoost algorithms was successful in predicting the concentration of homo-l-arginine in CSF per the concentrations of metabolite markers in the serum.

Conclusions

On the basis of machine learning combined with analysis of the serum and CSF metabolomes, we found metabolite markers related to neonatal sepsis with meningoencephalitis. The characteristics of neonatal sepsis with meningoencephalitis were manifested mainly by changes in arginine metabolism and related changes in creatinine metabolism.