Improving acute kidney injury diagnostic precision using biomarkers

Current applications and research trends of ultrasound examination in acute kidney injury assessment: a bibliometric analysis

BACKGROUND

Acute kidney injury (AKI) is a significant clinical condition, and ultrasound examination has emerged as a crucial non-invasive imaging method for assessing kidney status, especially in its diagnosis and management. This study aims to perform a bibliometric analysis to clarify current research trends in ultrasound assessment of AKI.

METHODS

We conducted a literature search in the Web of Science database using keywords related to ultrasound examinations of acute kidney injury, up to November 15, 2023. The results were analyzed using the bibliometric software package in R. Relevant literature information was analyzed.

RESULTS

A total of 1109 articles were included in the study. Research papers published between 2019 and 2024 demonstrated a significant upward trend. The United States, China, and Italy ranked as the top three countries in terms of publication volume. Among the top 10 research institutions with the highest number of publications, 6 are in the United States, with Université de Montréal being the institution with the most publications. Keyword trends focused on: resistive index, risk factors, therapy, glomerular filtration rate, survival, etc. CONCLUSION: This bibliometric study highlights the advancements in ultrasound examination for AKI and underscores the importance of such analyses in determining research trends. Future research should emphasize the integration of various imaging techniques to improve diagnostic accuracy and clinical management of AKI.

Development and validation of an early acute kidney injury risk prediction model for patients with sepsis in emergency departments

In this study, we aimed to develop and validate a nomogram to predicting the risk of sepsis-associated acute kidney injury (SA-AKI) in patients admitted to emergency departments (EDs). We randomly divided a retrospective dataset of 391 patients with sepsis into a 294-person training cohort and a 97-person validation cohort, and developed three predictive models using multivariate logistic regression analysis and clinical insight. No difference was observed between the three models using the DeLong test and Model 3 was selected as the risk prediction model based on the principle of least inclusion indicators. The use of vasopressor drugs, patient age, platelet count, procalcitonin, and D-dimer levels were included. The training and validation cohorts had a consistency index of 0.832 and 0.866, respectively, indicating high accuracy and stability in predicting SA-AKI risk. The area under the receiver operating characteristic curve was 0.832, showing excellent discrimination. The calibration curves for the training and validation cohorts showed excellent calibration. The decision curve and clinical impact curve analyses showed that the net clinical benefit of using the nomogram was greatest over a probability threshold of 0.05-0.90. In addition, the model showed moderate validity in predicting the 30-day survival and the incidence of major adverse renal events within 30 days. The nomogram developed for SA-AKI risk assessment in patients in EDs showed good discriminability and clinical utility. It can provide a theoretical basis for emergency physicians to prevent SA-AKI.

Diagnostic value of multi-parameter ultrasound evaluation in sepsis complicated by acute kidney injury

BACKGROUND

This study aimed to discuss the diagnostic value of multi-parameter ultrasound evaluation in sepsis complicated with acute kidney injury (AKI).

METHODS

Patients were divided into an AKI group (n = 50) and a non-injury group (n = 50) based on the presence of AKI. The clinical characteristics were collected, and renal function parameters between the two groups were compared, including 24-h urine volume, serum creatinine, urea, serum cystatin C (CysC), renal parenchymal thickness (RPT), renal artery resistance index (RI), and multi-parameter ultrasound scoring (MPUS). Additionally, logistic regression analysis was conducted to determine the influencing factors of sepsis complicated with AKI. The prediction value was evaluated using a receiver operating characteristic (ROC) curve.

RESULTS

In the AKI group, creatinine, CysC, urea, MPUS score, RPT, and RI values were significantly higher, while the 24-h urine volume was lower than those in the non-injury group (p < 0.01). Moreover, multivariate logistic analysis indicated that high CysC and RI values were independent risk factors, whereas high 24-h urine volume and low MPUS were independent protective factors for sepsis-induced AKI. The ROC curve demonstrated that RI (AUC = 0.906) was more effective than 24-h urine volume (AUC = 0.797), CysC (AUC = 0.730), and MPUS (AUC = 0.794) in identifying sepsis-induced AKI.

CONCLUSION

High RI values increase the risk of sepsis-induced AKI, whereas low MPUS may reduce it. RI showed high diagnosis values for sepsis complicated with AKI.

Neutrophil Gelatinase-Associated Lipocalin (NGAL) as a potential early biomarker for diabetic nephropathy: a meta-analysis

BACKGROUND

Diabetic nephropathy (DN) is a prevalent and chronic, severe complication of diabetes, representing a serious global health concern. Early detection of DN is essential for initiating timely and effective therapeutic interventions and accurately assessing prognosis. Neutrophil Gelatinase-Associated Lipocalin (NGAL), a low molecular weight protein, has emerged as a potential biomarker for DN due to its association with renal injury and its ability to provide early indications of kidney damage. NGAL levels in both serum and urine are elevated in individuals with renal damage, making it a valuable biomarker for detecting early signs of kidney impairment in the context of diabetes. This study aims to investigate the utility of NGAL as an early biomarker for DN and explore its correlation with various clinical parameters associated with the disease. Understanding the relationship between NGAL levels and clinical parameters such as glycemic control, renal function, blood pressure, and duration of diabetes is crucial for comprehensively evaluating the potential of NGAL as a diagnostic and prognostic tool for DN. Furthermore, assessing the sensitivity and specificity of NGAL in detecting early-stage DN will provide valuable insights into its clinical applicability and reliability.

METHODOLOGY

A planned meta-analysis was conducted following PRISMA and MOOSE guidelines. The PubMed database was searched from January 2016 to June 2023 for English-language studies on DN and NGAL. Fifteen eligible studies were included as per the criteria. Data on serum NGAL levels in DN patients and healthy controls were analyzed using Stata 16.0 software.

RESULT

The study revealed a significantly higher mean serum NGAL level in DN patients (168.08 ng/ml, 95% CI: 105.50-230.67) compared to healthy controls (75.02 ng/ml, 95% CI: 43.02-107.03), demonstrating NGAL's potential as a biomarker (P=0.01).

CONCLUSION

NGAL offers a powerful tool for DN diagnosis, staging, and monitoring, surpassing traditional markers in sensitivity. Challenges include defining universal threshold values and ensuring consistent test performance across diverse clinical settings. The study underscores NGAL's potential in transforming DN diagnosis and management.

The Utility of Urinary NGAL as an Alternative for Serum Creatinine to Detect Acute Kidney Injury in Infants Exposed to Nephrotoxic Medications in the Neonatal Intensive Care Unit

INTRODUCTION

Nephrotoxic medication (NTM) exposure is commonly associated with acute kidney injury (AKI) in the neonatal intensive care unit (NICU). Baby Nephrotoxic Injury Negated by Just-in-Time Action (NINJA) is a quality improvement program that assesses for AKI in those exposed to NTM with daily serum creatinine (SCr) levels. However, blood draws for SCr are invasive and have clinical disadvantages. Urinary neutrophil gelatinase-associated lipocalin (uNGAL) is a promising indicator of AKI. We tested the hypothesis that uNGAL could reliably screen for NTM-AKI in the Baby NINJA program.

METHODS

This two-center prospective study screened 174 NICU subjects, of whom 148 met screening criteria from January 29, 2019, to September 18, 2020. Daily SCr and urine samples were obtained for up to 7 days of NTM exposure plus 2 days after exposure ended or end of AKI. AKI was defined by a SCr rise of 50% from baseline. The highest uNGAL obtained was evaluated to determine its relationship to the diagnosis of AKI. Logistic regression models were used to determine optimal uNGAL cutoffs.

RESULTS

The negative predictive value of a uNGAL value ≥250 ng/mL was 96.8% (95% CI = 93.3-100%). Urine NGAL ≥400 ng/mL demonstrated the highest ROC-AUC value of 0.72 with a positive likelihood risk for AKI of 2.76 (1.39-4.13).

DISCUSSION/CONCLUSION

We propose that uNGAL could be used to screen for NTM-AKI and thus replace many blood draws needed in those exposed to NTM. The ideal uNGAL threshold requires further investigation in infants.

Proenkephalin as a biomarker correlates with acute kidney injury: a systematic review with meta-analysis and trial sequential analysis

BACKGROUND

Proenkephalin A 119-159 (PENK) is freely filtered in the glomerulus with plasma levels correlating with glomerular filtration rate. Therefore, PENK has been proposed as an early indicator of acute kidney injury (AKI) although its performance is dependent on the clinical setting. This meta-analysis aimed to investigate the correlation between PENK levels and the development of AKI.

METHODS

We conducted a comprehensive search on the PubMed, Embase, Cochrane databases, the website ClinicalTrials.gov and Cnki.net until June 26, 2023. Summary receiver operating characteristic (SROC) curves were used to amalgamate the overall test performance. Diagnostic odds ratio (DOR) was employed to compare the diagnostic accuracy of PENK with other biomarkers. Quality of the evidence was assessed using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) criteria.

RESULTS

We incorporated 11 observational studies with 3969 patients with an incidence of AKI of 23.4% (929 out of 3969 patients) with the best optimal cutoff value of PENK for early detection of AKI being 57.3 pmol/L. The overall sensitivity and specificity of PENK in identifying AKI were 0.69 (95% CI 0.62-0.75) and 0.76 (95% CI 0.68-0.82), respectively. The combined positive likelihood ratio (LR) stood at 2.83 (95% CI 2.06-3.88), and the negative LR was 0.41 (95% CI 0.33-0.52). The SROC curve showcased pooled diagnostic accuracy of 0.77 (95% CI 0.73-0.81). Interestingly, patients with a history of hypertension or heart failure demonstrated a lower specificity of PENK in correlating the development of AKI.

CONCLUSION

Our results indicate that PENK possesses significant potential as a biomarker for the early detection of the development of AKI, using a cutoff point of 57.3 pmol/L for PENK.

The diagnostic value of serum insulin-like growth factor binding protein 7 in gastric cancer

Backgrounds

Early detection might help in reducing the burden and promoting the survival rate of gastric cancers. Herein, we tried to explore the diagnostic value of insulin-like growth factor binding protein 7 (IGFBP7) in gastric cancers.

Methods

In this study, we first analyzed the expression levels and prognostic value of IGFBP7 mRNA in gastric cancers from The Cancer Genome Atlas (TCGA) database. Then, we recruited 169 gastric cancer patients and 100 normal controls as training cohort, and 55 gastric cancer patients and 55 normal controls as independent validation cohort. Enzyme-linked immunosorbent assay was applied to test the serum levels of IGFBP7. The receiver operating characteristic curve (ROC) and the area under the curve (AUC) were applied to evaluation the diagnostic value.

Results

TCGA showed that IGFBP7 mRNA was dysregulated and associated with prognosis in gastric cancer patients. Then, we examined the expression of serum IGFBP7 and found that serum IGFBP7 expressed lower in gastric cancer patients than normal controls both in training and independent validation cohorts (p < 0.0001). In training cohort, with the cutoff value of 1.515 ng/ml, the AUC for distinguishing gastric cancer patients was 0.774 (95% CI [0.713-0.836]) with sensitivity of 36.7% (95% CI [29.5-44.5]) and specificity of 90.0% (95% CI [82.0-94.8]). As for early-stage EJA, the AUC was 0.773 (95% CI [0.701-0.845]) with the sensitivity of 33.3% (95% CI [14.4-58.8]). In independent validation cohort, with the same cutoff value, the AUC reached to 0.758 (95% CI [0.664-0.852]). Similarly, for early-stage gastric cancer diagnosis in the independent validation cohort, the AUC value was 0.778 (95% CI [0.673-0.882]).

Conclusions

This study indicated that serum IGFBP7 might act as a potential early diagnostic marker for gastric cancers.

Optimizing Vancomycin Therapy in Critically Ill Children: A Population Pharmacokinetics Study to Inform Vancomycin Area under the Curve Estimation Using Novel Biomarkers

Area under the curve (AUC)-directed vancomycin therapy is recommended, but Bayesian AUC estimation in critically ill children is difficult due to inadequate methods for estimating kidney function. We prospectively enrolled 50 critically ill children receiving IV vancomycin for suspected infection and divided them into model training (n = 30) and testing (n = 20) groups. We performed nonparametric population PK modeling in the training group using Pmetrics, evaluating novel urinary and plasma kidney biomarkers as covariates on vancomycin clearance. In this group, a two-compartment model best described the data. During covariate testing, cystatin C-based estimated glomerular filtration rate (eGFR) and urinary neutrophil gelatinase-associated lipocalin (NGAL; full model) improved model likelihood when included as covariates on clearance. We then used multiple-model optimization to define the optimal sampling times to estimate AUC24 for each subject in the model testing group and compared the Bayesian posterior AUC24 to AUC24 calculated using noncompartmental analysis from all measured concentrations for each subject. Our full model provided accurate and precise estimates of vancomycin AUC (bias 2.3%, imprecision 6.2%). However, AUC prediction was similar when using reduced models with only cystatin C-based eGFR (bias 1.8%, imprecision 7.0%) or creatinine-based eGFR (bias -2.4%, imprecision 6.2%) as covariates on clearance. All three model(s) facilitated accurate and precise estimation of vancomycin AUC in critically ill children.

Novel Biomarkers of Diabetic Kidney Disease

Diabetic kidney disease (DKD) is a highly prevalent condition worldwide. It represents one of the most common complications arising from diabetes mellitus (DM) and is the leading cause of end-stage kidney disease (ESKD). Its development involves three fundamental components: the hemodynamic, metabolic, and inflammatory axes. Clinically, persistent albuminuria in association with a progressive decline in glomerular filtration rate (GFR) defines this disease. However, as these alterations are not specific to DKD, there is a need to discuss novel biomarkers arising from its pathogenesis which may aid in the diagnosis, follow-up, therapeutic response, and prognosis of the disease.

Progress and Challenges of Understanding Cardiorenal Syndrome Type 3

The pathologies of the kidney and heart have instigated a large number of researchers around the world to try to better understand what the exact connectors responsible for the emergence and establishment of these diseases are. The classification of these pathologies into different types of cardiorenal syndromes (CRSs) over the last 15 years has greatly contributed to understanding pathophysiological and diagnostic aspects, as well as treatment strategies. However, with the advent of new technologies classified as "Omics", a new range of knowledge and new possibilities have opened up in order to effectively understand the intermediaries between the kidney-heart axis. The universe of micro-RNAs (miRNAs), epigenetic factors, and components present in extracellular vesicles (EVs) have been protagonists in studying different types of CRSs. Thus, the new challenge that is imposed is to select and link the large amount of information generated from the use of large-scale analysis techniques. The present review seeks to present some of the future perspectives related to understanding CRSs, with an emphasis on CRS type 3.

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.