Serum cystatin C, kidney injury molecule-1, neutrophil gelatinase-associated lipocalin, klotho and fibroblast growth factor-23 in the early prediction of acute kidney injury associated with sepsis in a Chinese emergency cohort study

Renal arterial resistive index versus novel biomarkers for the early prediction of sepsis-associated acute kidney injury

Acute kidney injury (AKI) is a critical complication of sepsis. There is a continuous need to identify and validate biomarkers for early detection. Serum and urinary biomarkers have been investigated, such as neutrophil gelatinase associated lipocalin (NGAL) and cystatin C (Cys C), but their reliability in the intensive care unit (ICU) remains unknown. Renal hemodynamics can be investigated by measuring the renal resistive index (RRI). This study aimed to compare the performance of RRI, serum NGAL (sNGAL), urinary NGAL (uNGAL), and serum Cys C levels as early predictors of the diagnosis and persistence of sepsis-associated AKI. A total of 166 adult patients with sepsis syndrome were enrolled immediately after ICU admission. Biomarkers were measured directly (T1) and on day 3 (T3). RRI was measured directly (T1) and 24 h later (T2). Patients were categorized (according to the occurrence and persistence of AKI within the first 7 days) into three groups: no AKI, transient AKI, and persistent AKI. The incidence rate of sepsis-associated AKI was 60.2%. Sixty-six patients were categorized as in the no AKI group, while another 61 were in transient AKI and only 39 were in persistent AKI. The RRI value (T1 ≥ 0.72) was the best tool for predicting AKI diagnosis (area under the receiver operating characteristic curve, AUROC = 0.905). Cys C (T1 ≥ 15.1 mg/l) was the best tool to predict the persistence of AKI (AUROC = 0.977). RRI (T1) was the best predictive tool for sepsis-associated AKI, while Cys C was the best predictor of its persistence and 28-day mortality.

Participatory Health Cadre Model to Improve Exclusive Breastfeeding Coverage with King's Conceptual System

Objective

The purpose of this research is to develop a participatory health cadre model to enhance exclusive breastfeeding coverage through initial stages using the Imogene King model.

Methods

This study employs a mixed-methods approach with sequential exploratory designs. Qualitative research utilized in-depth interviews with informants including the head of the community health center, nutrition officers from the health center, the coordinator of Maternal and Child Health (MCH) midwives, village midwives, breastfeeding mothers, families of breastfeeding mothers, and health cadres. Quantitative research respondents consist of health cadres. The quantitative study utilizes a quasi-experimental method with a design paradigm known as the one-group pre and post-test design to measure health cadre perception on exclusive breastfeeding.

Results

This study yields elements from Imogene King that form a participatory health cadre model to enhance exclusive breastfeeding coverage, consisting of interaction, perception, communication, transaction, role, growth and development, time, and space. Transactions represent the objective integration of the health cadre participation model, as demonstrated by the behavioral shifts observed in mothers regarding breastfeeding their infants. The t-test results indicate that exclusive breastfeeding monitoring training is effective and successful in enhancing exclusive breastfeeding coverage (Sig. value = 0.000 < 0.05). In addition, the effectiveness of exclusive breastfeeding monitoring training falls within the category of good or high.

Conclusion

The research findings indicate the success of the participatory health cadre model in improving exclusive breastfeeding coverage.

Acute Kidney Injury Prediction Model Using Cystatin-C, Beta-2 Microglobulin, and Neutrophil Gelatinase-Associated Lipocalin Biomarker in Sepsis Patients

Introduction

AKI is a frequent complication in sepsis patients and is estimated to occur in almost half of patients with severe sepsis. However, there is currently no effective therapy for AKI in sepsis. Therefore, the therapeutic approach is focused on prevention. Based on this, there is an opportunity to examine a panel of biomarker models for predicting AKI.

Material and Methods

This prospective cohort study analysed the differences in Cystatin C, Beta-2 Microglobulin, and NGAL levels in sepsis patients with AKI and sepsis patients without AKI. The biomarker modelling of AKI prediction was done using machine learning, namely Orange Data Mining. In this study, 130 samples were analysed by machine learning. The parameters used to obtain the biomarker panel were 23 laboratory examination parameters.

Results

This study used SVM and the Naïve Bayes model of machine learning. The SVM model's sensitivity, specificity, NPV, and PPV were 50%, 94.4%, 71.4%, and 87.5%, respectively. For the Naïve Bayes model, the sensitivity, specificity, NPV, and PPV were 83.3%, 77.8%, 87.5%, and 71.4%, respectively.

Discussion

This study's SVM machine learning model has higher AUC and specificity but lower sensitivity. The Naïve Bayes model had better sensitivity; it can be used to predict AKI in sepsis patients.

Conclusion

The Naïve Bayes machine learning model in this study is useful for predicting AKI in sepsis patients.

Combination of Urinary Neutrophil Gelatinase-associated Lipocalin, Kidney Injury Molecular-1, and Angiotensinogen for the Early Diagnosis and Mortality Prediction of Septic Acute Kidney Injury

BACKGROUND

Acute kidney injury (AKI) is one of the most severe complications of sepsis. This study was conducted to analyze the role of urinary neutrophil gelatinase-associated lipocalin (uNGAL), urinary kidney injury molecular-1 (uKIM-1), and urinary angiotensinogen (uAGT) in the early diagnosis and mortality prediction of septic AKI.

METHODS

The prospective study enrolled 80 sepsis patients in the ICU and 100 healthy individuals and divided patients into an AKI group and a non-AKI group. uNGAL, uKIM-1, uAGT, serum creatinine/procalcitonin/C-reaction protein, and other indicators were determined, and clinical prediction scores were recorded. The sensitivity and specificity of uNGAL, uKIM-1, and uAGT in diagnosis and mortality prediction were analyzed by the receiver operator characteristic (ROC) curve and the area under the curve (AUC).

RESULTS

uNGAL, uKIM-1, and uAGT levels were higher in sepsis patients than healthy controls, higher in AKI patients than non-AKI patients, and higher in AKI-2 and AKI-3 patients than AKI-1 patients. At 0 h after admission, the combined efficacy of three indicators in septic AKI diagnosis (ROC-AUC: 0.770; sensitivity: 82.5%; specificity: 70.0%) was better than a single indicator. At 24 h, uNGAL, uKIM-1, and uAGT levels were higher in sepsis non-survivals than survivals and higher in septic AKI non-survivals than septic AKI survivals. The combined efficacy of three indicators in the prediction of sepsis/septic AKI mortality (ROC-AUC: 0.828/0.847; sensitivity: 71.4%/100.0%; specificity: 82.7%/66.7%) was better than a single indicator.

CONCLUSION

uNGAL, uKIM-1, and uAGT levels were increased in septic AKI, and their combination helped the early diagnosis and mortality prediction.

Soluble interleukin-2 receptor predicts acute kidney injury and in-hospital mortality in patients with acute myocardial infarction

BACKGROUND

Acute kidney injury (AKI) is the most common and critical complication in patients with acute myocardial infarction (AMI). This study aims to evaluate the significance of elevated soluble interleukin 2 receptor (sIL-2R) levels in predicting AKI and mortality.

METHODS

A total of 446 patients with AMI were enrolled between January 2020 and July 2022, including 58 patients with AKI and 388 without AKI. The sIL-2R levels were measured using a commercially available chemiluminescence enzyme immunoassay. Logistic regression analysis was used to examine the risk factors for AKI. Discrimination was assessed based on the area under the receiver operating characteristic curve. The model was internally validated using 10-fold cross-validation.

RESULTS

During hospitalization, 13% of patients developed AKI following AMI, with higher sIL-2R levels (0.61 ± 0.27 U/L vs. 0.42 ± 0.19 U/L, p = 0.003) and in-hospital all-cause mortality (12.1% vs. 2.6%, P < 0.001). The sIL-2R levels emerged as an independent risk factor for both AKI (OR = 5.08, 95% CI (1.04-24.84, p < 0.045) and in-hospital all-cause mortality (OR = 73.57,95% CI 10.24-528.41, p < 0.001) in AMI patients. The sIL-2R levels were found to be useful biomarkers in prediction of AKI and in-hospital all-cause mortality in patients with AMI (AUC: 0.771 and 0.894, respectively). The respective cutoff values for sIL-2R levels in predicting AKI and in-hospital all-cause mortality were determined to be 0.423 U/L and 0.615 U/L.

CONCLUSIONS

The level of sIL-2R was an independent risk factor and predictor for both AKI and in-hospital all-cause mortality in patients with AMI. These findings highlight the potential of sIL-2R as a valuable tool for identifying high-risk patients regarding AKI and in-hospital mortality.

Neutrophil elastase in dexmedetomidine alleviating sepsis-related renal injury in rats

BACKGROUND

This research was to investigate the mechanism of neutrophil elastase (NE) in dexmedetomidine (DEX) alleviates sepsis-related renal injury in rats.

METHODS

Sixty healthy male SD rats aged 6-7 weeks were randomly assigned to the control group (Sham group (S group)), Model group (M group), Model + DEX group (M + DEX group), and Model + DEX + Elaspol group (M + DEX + Elaspol (sivelestat) group), with 15 rats in each group. The renal morphology and pathological changes of different groups of rats after modeling were observed, and renal tubular injury was scored. Serum samples were collected at 6 h, 12 h, and 24 h after modeling, and the rats were sacrificed. Renal function indicators, including neutrophil gelatinase-associated lipoprotein (NGAL), kidney injury molecule-1 (KIM-1), tumor necrosis factor (TNF-α), interleukin-6 (IL-6), NE, serum creatinine (SCr), and blood urea nitrogen (BUN), were analyzed by enzyme-linked immunosorbent assay at different time periods. The level of NF-кB in renal tissue was detected by immunohistochemistry.

RESULTS

It was revealed that the general color of renal tissue in M group was dark red, swollen, and congested, and the renal tubular epithelial cells were significantly enlarged, with obvious vacuolar degeneration and inflammatory cell infiltration. Compared with M group, the color and morphology of renal tissue in M + DEX group and M + DEX + Elaspol group were improved, and the amount of inflammatory cell infiltration was reduced. The renal tubular injury score, SCr level, BUN level, NGAL level, KIM-1 level, TNF-α, IL-6, NE level, and NF-кB level in M group were significant different from S group 12 h after the operation (P < 0.001). The renal tubular injury score, SCr level, BUN level, NGAL level, KIM-1 level, TNF-α, IL-6, NE level, and NF-кB level in M + DEX group were significant different from M group (P < 0.01). The renal tubular injury score, SCr level, BUN level, NGAL level, KIM-1 level, TNF-α, IL-6, NE level, and NF-кB level in M + DEX + Elaspol group were significant different from those in M group at 12 h after the operation (P < 0.001).

CONCLUSION

NE plays an active role in the reduction of sepsis-related renal injury in rats by inhibiting the inflammatory response.

Diagnostic Accuracy of Plasma Cystatin C and Renal Resistive Index for Acute Kidney Injury in Critically Ill Patients: A Prospective Observational Study

Background

Acute kidney injury (AKI) is a quite common problem in critically ill patients. Serum cystatin C has emerged as a marker of AKI. This study was aimed to evaluate the diagnostic ability of serum Cystatin-C and Renal Resistive Index in prediction of AKI among critically ill patients.

Methodology

This prospective observational study was carried out in the department of Medicine, over a period of one year. After informed consent and ethical clearance total 120 critically ill patients suffering from sepsis were enrolled, out of which 70 patients developed AKI while 50 did not develop AKI during treatment in Intensive care unit (ICU). Serum cystatin C was measured on day 1 by particle-enhanced immune nephelometric assay, Renal resistive index (RRI) calculated by ratio of the velocities of arterial perfusion throughout the cardiac phase and glomerular filtration rate was measured on days 1, 3, and 7 respectively.

Results

S. cystatin C value was significantly higher(>3times) in AKI patients (14.07±4.8 mcg/ml) as compared to those who did not develop AKI (4.28±3.27 mcg/ml) (p<0.001). After ROC analysis it was found that day1, S. cystatin C, at cut off value of ≥9.29 mcg/ml had diagnostic accuracy 90% with sensitivity 91%, specificity89% and PPV 95.5%. While RRI value on day 7, at cut-off value of ≥0.72, had diagnostic accuracy 98%, sensitivity (98.6%) and specificity (96.7%) for AKI with 98.6% PPV, 96.7% NPV.

Conclusion

Serum cystatin C appears to be a promising bio- markers for early diagnosis of AKI in critically ill patients. Whereas, RRI although non-invasive had good diagnostic accuracy but it diagnosed AKI after few days thus diagnosis of kidney injury delayed.

Sepsis-mediated renal dysfunction: Pathophysiology, biomarkers and role of phytoconstituents in its management

Sepsis has evolved as an enormous health issue amongst critically ill patients. It is a major risk factor that results in multiple organ failure and shock. Acute kidney injury (AKI) is one of the most frequent complications underlying sepsis, which portends a heavy burden of mortality and morbidity. Thus, the present review is aimed to provide an insight into the recent progression in the molecular mechanisms targeting dysregulated immune response and cellular dysfunction involved in the development of sepsis-associated AKI, accentuating the phytoconstituents as eligible candidates for attenuating the onset and progression of sepsis-associated AKI. The pathogenesis of sepsis-mediated AKI entails a complicated mechanism and is likely to involve a distinct constellation of hemodynamic, inflammatory, and immune mechanisms. Novel biomarkers like neutrophil gelatinase-associated lipocalin, soluble triggering receptor expressed on myeloid cells 1, procalcitonin, alpha-1-microglobulin, and presepsin can help in a more sensitive diagnosis of sepsis-associated AKI. Many bioactive compounds like curcumin, resveratrol, baicalin, quercetin, and polydatin are reported to play an important role in the prevention and management of sepsis-associated AKI by decreasing serum creatinine, blood urea nitrogen, cystatin C, lipid peroxidation, oxidative stress, IL-1β, TNF-α, NF-κB, and increasing the activity of antioxidant enzymes and level of PPARγ. The plant bioactive compounds could be developed into a drug-developing candidate in managing sepsis-mediated acute kidney injury after detailed follow-up studies. Lastly, the gut-kidney axis may be a more promising therapeutic target against the onset of septic AKI, but a deeper understanding of the molecular pathways is still required.

Linalool prevents kidney damage by inhibiting rifampicin-induced oxidative stress and apoptosis

Today, kidney diseases are increasing day by day and life quality is decreasing. In hospitalized patients of all ages, acute kidney injury (AKI) is commonly observed and associated with high rates of morbidity and mortality. Rifampicin (RF) or rifampin is an antibiotic drug from the rifamycin group with a bactericidal effect. RF causes acute kidney injury, often anemia, thrombocytopenia, liver damage and side effect such as cell death. RF causes tissue damage by means of oxidative stress and apoptosis. Thus, in this study, it was examined whether linalool (LN) which had antinociceptive, antimicrobial, antioxidant and anti-inflammatory effects, was beneficial for kidney damage in order to eliminate the side effects of RF. NGAL mRNA, creatinine (Cr), blood urea nitrogen (BUN), Caspase 9 (CAS-9) and nuclear factor-κB (NF-κB) levels increased in the group treated with RF compared to the control group, while the levels of albumin, uric acid and total protein were decreased in the RF-treated group. NGAL mRNA, BUN, Cr, CAS-9 and NF-κB levels decreased significantly in RF+LN administered rats, while it was observed that there was an increase in the levels of albumin, uric acid and total protein. From the results obtained, it was observed that LN was determined to be very effective in preventing tissue damage in kidneys caused by oxidative stress by RF.

Acute kidney injury in diabetes mellitus: Epidemiology, diagnostic, and therapeutic concepts

Acute kidney injury (AKI) and diabetes mellitus (DM) are public health problems that cause a high socioeconomic burden worldwide. In recent years, the landscape of AKI etiology has shifted: Emerging evidence has demonstrated that DM is an independent risk factor for the onset of AKI, while an alternative perspective considers AKI as a bona fide complication of DM. Therefore, it is necessary to systematically characterize the features of AKI in DM. In this review, we summarized the epidemiology of AKI in DM. While focusing on circulation- and tissue-specific microenvironment changes after DM, we described the active cellular and molecular mechanisms of increased kidney susceptibility to AKI under DM stress. We also reviewed the current diagnostic and therapeutic strategies for AKI in DM recommended in the clinic. Updated recognition of the epidemiology, pathophysiology, diagnosis, and medications of AKI in DM is believed to reveal a path to mitigate the frequency of AKI and DM comorbidity that will ultimately improve the quality of life in DM patients.

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

Background

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

Methods

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

Results

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

Conclusions

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

Trial registrationCRD42020207883, October 06, 2020.

Supplementary Information

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

Developing an ensemble machine learning model for early prediction of sepsis-associated acute kidney injury

Sepsis-associated acute kidney injury (S-AKI) is very common and early prediction is beneficial. This study aiming to develop an accurate ensemble model to predict the risk of S-AKI based on easily available clinical information. Patients with sepsis from the United States (US) database Medical Information Mart for Intensive Care-IV were used as a modeling cohort to predict the occurrence of AKI by combining Support Vector Machine, Random Forest, Neural Network, and Extreme Gradient Boost as four first-level learners via stacking algorithm. The external validation databases were the eICU Collaborative Research Database from US and Critical Care Database comprising infection patients at Zigong Fourth People's Hospital from China, whose AUROC values for the ensemble model 48-12 h before the onset of AKI were 0.774-0.788 and 0.756-0.813, respectively. In this study, an ensemble model for early prediction of S-AKI onset was developed and it demonstrated good performance in multicenter external datasets.