Acute kidney injury prediction in cardiac surgery patients by a urinary peptide pattern: a case-control validation study

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Characterization of Risk Prediction Models for Acute Kidney Injury: A Systematic Review and Meta-analysis

Importance

Despite the expansion of published prediction models for acute kidney injury (AKI), there is little evidence of uptake of these models beyond their local derivation nor data on their association with patient outcomes.

Objective

To systematically review published AKI prediction models across all clinical subsettings.

Data Sources

MEDLINE via PubMed (January 1946 to April 2021) and Embase (January 1947 to April 2021) were searched using medical subject headings and text words related to AKI and prediction models.

Study Selection

All studies that developed a prediction model for AKI, defined as a statistical model with at least 2 predictive variables to estimate future occurrence of AKI, were eligible for inclusion. There was no limitation on study populations or methodological designs.

Data Extraction and Synthesis

Two authors independently searched the literature, screened the studies, and extracted and analyzed the data following the Preferred Reporting Items for Systematic Review and Meta-analyses guideline. The data were pooled using a random-effects model, with subgroups defined by 4 clinical settings. Between-study heterogeneity was explored using multiple methods, and funnel plot analysis was used to identify publication bias.

Main Outcomes and Measures

C statistic was used to measure the discrimination of prediction models.

Results

Of the 6955 studies initially identified through literature searching, 150 studies, with 14.4 million participants, met the inclusion criteria. The study characteristics differed widely in design, population, AKI definition, and model performance assessments. The overall pooled C statistic was 0.80 (95% CI, 0.79-0.81), with pooled C statistics in different clinical subsettings ranging from 0.78 (95% CI, 0.75-0.80) to 0.82 (95% CI, 0.78-0.86). Between-study heterogeneity was high overall and in the different clinical settings (eg, contrast medium-associated AKI: I2 = 99.9%; P < .001), and multiple methods did not identify any clear sources. A high proportion of models had a high risk of bias (126 [84.4%]) according to the Prediction Model Risk Of Bias Assessment Tool.

Conclusions and Relevance

In this study, the discrimination of the published AKI prediction models was good, reflected by high C statistics; however, the wide variation in the clinical settings, populations, and predictive variables likely drives the highly heterogenous findings that limit clinical utility. Standardized procedures for development and validation of prediction models are urgently needed.

A universal predictive and mechanistic urinary peptide signature in acute kidney injury

BACKGROUND

The delayed diagnosis of acute kidney injury (AKI) episodes and the lack of specificity of current single AKI biomarkers hamper its management. Urinary peptidome analysis may help to identify early molecular changes in AKI and grasp its complexity to identify potential targetable molecular pathways.

METHODS

In derivation and validation cohorts totalizing 1170 major cardiac bypass surgery patients and in an external cohort of 1569 intensive care unit (ICU) patients, a peptide-based score predictive of AKI (7-day KDIGO classification) was developed, validated, and compared to the reference biomarker urinary NGAL and NephroCheck and clinical scores.

RESULTS

A set of 204 urinary peptides derived from 48 proteins related to hemolysis, inflammation, immune cells trafficking, innate immunity, and cell growth and survival was identified and validated for the early discrimination (&lt; 4 h) of patients according to their risk to develop AKI (OR 6.13 [3.96-9.59], p &lt; 0.001) outperforming reference biomarkers (urinary NGAL and [IGFBP7].[TIMP2] product) and clinical scores. In an external cohort of 1569 ICU patients, performances of the signature were similar (OR 5.92 [4.73-7.45], p &lt; 0.001), and it was also associated with the in-hospital mortality (OR 2.62 [2.05-3.38], p &lt; 0.001).

CONCLUSIONS

An overarching AKI physiopathology-driven urinary peptide signature shows significant promise for identifying, at an early stage, patients who will progress to AKI and thus to develop tailored treatments for this frequent and life-threatening condition. Performance of the urine peptide signature is as high as or higher than that of single biomarkers but adds mechanistic information that may help to discriminate sub-phenotypes of AKI offering new therapeutic avenues.

CE-MS for Proteomics and Intact Protein Analysis

This chapter aims to explore various parameters involved in achieving high-end capillary electrophoresis hyphenated to mass spectrometry (CE-MS) analysis of proteins, peptides, and their posttranslational modifications. The structure of the topics discussed in this book chapter is conveniently mapped on the scheme of the CE-MS system itself, starting from sample preconcentration and injection techniques and finishing with mass analyzer considerations. After going through the technical considerations, a variety of relevant applications for this analytical approach are presented, including posttranslational modifications analysis, clinical biomarker discovery, and its growing use in the biotechnological industry.

Urinary Metabolome Analyses of Patients with Acute Kidney Injury Using Capillary Electrophoresis-Mass Spectrometry

Acute kidney injury (AKI) is defined as a rapid decline in kidney function. The associated syndromes may lead to increased morbidity and mortality, but its early detection remains difficult. Using capillary electrophoresis time-of-flight mass spectrometry (CE-TOFMS), we analyzed the urinary metabolomic profile of patients admitted to the intensive care unit (ICU) after invasive surgery. Urine samples were collected at six time points: before surgery, at ICU admission and 6, 12, 24 and 48 h after. First, urine samples from 61 initial patients (non-AKI: 23, mild AKI: 24, severe AKI: 14) were measured, followed by the measurement of urine samples from 60 additional patients (non-AKI: 40, mild AKI: 20). Glycine and ethanolamine were decreased in patients with AKI compared with non-AKI patients at 6-24 h in the two groups. The linear statistical model constructed at each time point by machine learning achieved the best performance at 24 h (median AUC, area under the curve: 89%, cross-validated) for the 1st group. When cross-validated between the two groups, the AUC showed the best value of 70% at 12 h. These results identified metabolites and time points that show patterns specific to subjects who develop AKI, paving the way for the development of better biomarkers.

CD99 and polymeric immunoglobulin receptor peptides deregulation in critical COVID-19: A potential link to molecular pathophysiology?

Identification of significant changes in urinary peptides may enable improved understanding of molecular disease mechanisms. We aimed towards identifying urinary peptides associated with critical course of COVID-19 to yield hypotheses on molecular pathophysiological mechanisms in disease development. In this multicentre prospective study urine samples of PCR-confirmed COVID-19 patients were collected in different centres across Europe. The urinary peptidome of 53 patients at WHO stages 6-8 and 66 at WHO stages 1-3 COVID-19 disease was analysed using capillary electrophoresis coupled to mass spectrometry. 593 peptides were identified significantly affected by disease severity. These peptides were compared with changes associated with kidney disease or heart failure. Similarities with kidney disease were observed, indicating comparable molecular mechanisms. In contrast, convincing similarity to heart failure could not be detected. The data for the first time showed deregulation of CD99 and polymeric immunoglobulin receptor peptides and of known peptides associated with kidney disease, including collagen and alpha-1-antitrypsin. Peptidomic findings were in line with the pathophysiology of COVID-19. The clinical corollary is that COVID-19 induces specific inflammation of numerous tissues including endothelial lining. Restoring these changes, especially in CD99, PIGR and alpha-1-antitripsin, may represent a valid and effective therapeutic approach in COVID-19, targeting improvement of endothelial integrity. This article is protected by copyright. All rights reserved.

Free Hemoglobin Ratio as a Novel Biomarker of Acute Kidney Injury After On-Pump Cardiac Surgery: Secondary Analysis of a Randomized Controlled Trial

BACKGROUND

Cardiac surgery with cardiopulmonary bypass (CPB) is associated with a high risk of postoperative acute kidney injury (AKI). Due to limitations of current diagnostic strategies, we sought to determine whether free hemoglobin (fHb) ratio (ie, levels of fHb at the end of CPB divided by baseline fHb) could predict AKI after on-pump cardiac surgery.

METHODS

This is a secondary analysis of a randomized controlled trial comparing the effect of nitric oxide (intervention) versus nitrogen (control) on AKI after cardiac surgery (NCT01802619). A total of 110 adult patients in the control arm were included. First, we determined whether fHb ratio was associated with AKI via multivariable analysis. Second, we verified whether fHb ratio could predict AKI and incorporation of fHb ratio could improve predictive performance at an early stage, compared with prediction using urinary biomarkers alone. We conducted restricted cubic spline in logistic regression for model development. We determined the predictive performance, including area under the receiver-operating-characteristics curve (AUC) and calibration (calibration plot and accuracy, ie, number of correct predictions divided by total number of predictions). We also used AUC test, likelihood ratio test, and net reclassification index (NRI) to compare the predictive performance between competing models (ie, fHb ratio versus neutrophil gelatinase-associated lipocalin [NGAL], N-acetyl-β-d-glucosaminidase [NAG], and kidney injury molecule-1 [KIM-1], respectively, and incorporation of fHb ratio with NGAL, NAG, and KIM-1 versus urinary biomarkers alone), if applicable.

RESULTS

Data stratified by median fHb ratio showed that subjects with an fHb ratio >2.23 presented higher incidence of AKI (80.0% vs 49.1%; P = .001), more need of renal replacement therapy (10.9% vs 0%; P = .036), and higher in-hospital mortality (10.9% vs 0%; P = .036) than subjects with an fHb ratio ≤2.23. fHb ratio was associated with AKI after adjustment for preestablished factors. fHb ratio outperformed urinary biomarkers with the highest AUC of 0.704 (95% confidence interval [CI], 0.592-0.804) and accuracy of 0.714 (95% CI, 0.579-0.804). Incorporation of fHb ratio achieved better discrimination (AUC test, P = .012), calibration (likelihood ratio test, P < .001; accuracy, 0.740 [95% CI, 0.617-0.832] vs 0.632 [95% CI, 0.477-0.748]), and significant prediction increment (NRI, 0.638; 95% CI, 0.269-1.008; P < .001) at an early stage, compared with prediction using urinary biomarkers alone.

CONCLUSIONS

Results from this exploratory, hypothesis-generating retrospective, observational study shows that fHb ratio at the end of CPB might be used as a novel, widely applicable biomarker for AKI. The use of fHb ratio might help for an early detection of AKI, compared with prediction based only on urinary biomarkers.

Beta-Trace Protein as a Potential Marker of Acute Kidney Injury: A Pilot Study

INTRODUCTION

Acute kidney injury (AKI) is a frequent complication among patients in the intensive care unit (ICU). The limitations of serum Cr (sCr) in timely detecting AKI are well known. Beta-trace protein (BTP) is emerging as a novel endogenous glomerular filtration rate marker. The aim of this study was to explore the role of BTP as a marker of AKI.

METHODS

Patients admitted to the ICU undergoing surgery were included. BTP, sCr, Cystatin C (CysC), and neutrophil gelatinase-associated lipocalin (NGAL) were measured preoperatively, postoperatively (post-op), and at the first (D1) and second (D2) post-op day. AKI was defined as an increase of sCr to ≥1.5-fold from baseline within 2 days after surgery.

RESULTS

Of the 52 patients studied, 10 patients (19%) developed AKI. Patients with AKI were older (69.6 ± 10.7 vs. 58.1 ± 16.7 years, p = 0.043) and had a longer length of ICU stay (13 [IQR 6-49] vs. 6 [IQR 5-8] days, p = 0.032). Between the 2 groups, the evolution of BTP, sCr, CysC, and NGAL over time differed significantly, with overall higher values in the AKI group. ROC analysis for the detection of AKI within 2 days after surgery showed a great accuracy for BTP. The area under the curve (AUC) for BTP post-op; D1; and D2 was, respectively, 0.869 ± 0.049; 0.938 ± 0.035; and 0.943 ± 0.032. The discriminative power of a BTP measurement on D1 was superior in detecting AKI compared to NGAL (adjusted p value = 0.027). We could not detect a significant difference between the AUCs of other biomarkers (NGAL, sCr, and CysC).

CONCLUSION

Serum BTP is a promising marker for diagnosing AKI in ICU patients undergoing surgery.

Value of Urine Peptides in Assessing Kidney and Cardiovascular Disease

Urinary peptides gained significant attention as potential biomarkers especially in the context of kidney and cardiovascular disease. In this manuscript the recent literature since 2015 on urinary peptide investigation in human kidney and cardiovascular disease is reviewed. The technology most commonly used in this context is capillary electrophoresis coupled mass spectrometry, in part owed to the large database available and the well-defined dataspace. Several studies based on over 1000 subjects are reported in the recent past, especially examining CKD273, a classifier for assessment of chronic kidney disease based on 273 urine peptides. Interestingly, the most abundant urinary peptides are generally collagen fragments, which may have gone undetected for some time as they are typically modified via proline hydroxylation. The data available suggest that urinary peptides specifically depict inflammation and fibrosis, and may serve as a non-invasive tool to assess fibrosis, which appears to be a key driver in kidney and cardiovascular disease. The recent successful completion of the first urinary peptide guided intervention trial, PRIORITY, is expected to further spur clinical application of urinary peptidomics, aiming especially at early detection of chronic diseases, prediction of progression, and prognosis of drug response.

Will urinary biomarkers provide a breakthrough in diagnosing cardiac surgery-associated AKI? - A systematic review

Introduction: Acute kidney injury following cardiac surgery is a dreaded complication contributing to early mortality. Diagnosing AKI using serum creatinine usually results in a delay. To combat this, certain kidney damage specific biomarkers were investigated to identify if they can serve as early predictors of cardiac surgery-associated AKI (CSA-AKI). This study systematically reviews three such biomarkers; NGAL, tissue inhibitor of matrix metalloproteinase-2 (TIMP-2) and insulin-like growth factor binding protein-7 (IGFBP7) to identify if they can serve as early predictors of CSA-AKI.Methods: Systematic search was carried out on literature reporting the diagnostic ability of the three biomarkers from databases in accordance with PRISMA guidelines.Results: We found 43 articles reporting urinary-NGAL levels (n = 34 in adults, n = 9 in children) and 10 studies reporting TIMP-2 and IGFBP7 levels among adults. Interestingly, NGAL showed high diagnostic value in predicting AKI in children (seven among nine studies with AUROC > 0.8). The cell cycle arrest biomarkers, namely TIMP-2 and IGFBP7, showed high diagnostic value in predicting AKI in adults (five among ten studies with AUROC > 0.8).Conclusion: In predicting CSA-AKI; the diagnostic value of NGAL is high in the paediatric population while the diagnostic value of TIMP-2 and IGFBP7 is high in adults.

Urinary Peptidomic Biomarkers in Kidney Diseases

In order to effectively develop personalized medicine for kidney diseases we urgently need to develop highly accurate biomarkers for use in the clinic, since current biomarkers of kidney damage (changes in serum creatinine and/or urine albumin excretion) apply to a later stage of disease, lack accuracy, and are not connected with molecular pathophysiology. Analysis of urine peptide content (urinary peptidomics) has emerged as one of the most attractive areas in disease biomarker discovery. Urinary peptidome analysis allows the detection of short and long-term physiological or pathological changes occurring within the kidney. Urinary peptidomics has been applied extensively for several years now in renal patients, and may greatly improve kidney disease management by supporting earlier and more accurate detection, prognostic assessment, and prediction of response to treatment. It also promises better understanding of kidney disease pathophysiology, and has been proposed as a "liquid biopsy" to discriminate various types of renal disorders. Furthermore, proteins being the major drug targets, peptidome analysis may allow one to evaluate the effects of therapies at the protein signaling pathway level. We here review the most recent findings on urinary peptidomics in the setting of the most common kidney diseases.

Serum Lactate As Reliable Biomarker of Acute Kidney Injury in Low-risk Cardiac Surgery Patients

Background

Cardiac surgery-associated acute kidney injury (CSA-AKI) frequently occurs in patients assessed as low-risk for developing CSA-AKI. Neutrophil Gelatinase-Associated Lipocalin (NGAL), Kidney Injury Molecule-1 (KIM-1) and lactate are promising biomarkers of CSA-AKI but have not yet been explored in low-risk patients.

Aim

To evaluate urinary NGAL (uNGAL), KIM-1 and lactate as biomarkers of CSA-AKI in patients with low-risk for developing CSA-AKI.

Methods

This prospective, observational study included 100 adult elective cardiac surgery patients assessed as low-risk for developing CSA-AKI. UNGAL, KIM-1 and lactate were measured preoperatively, at the end of cardiopulmonary bypass (CPB) and 3, 12, 24 and 48 h later.

Results

Fifteen patients developed CSA-AKI. Patients with CSA-AKI had significantly higher lactate but similar uNGAL and KIM-1 levels compared to patients without CSA-AKI. Unlike uNGAL and KIM-1, postoperative lactate was good biomarker of CSA-AKI with the highest odds ratio (OR) 2.7 [1.4–4.9] 24 h after CPB. Peak lactate concentration ≥ 4 mmol/L carried dramatically higher risk for developing CSA-AKI (OR 6.3 [1.9–20.5]).

Conclusions

Unlike uNGAL and KIM-1, postoperative lactate was significant independent predictor of CSA-AKI with the highest odds ratio 24 h after CPB.

Proteomics and Metabolomics for AKI Diagnosis

Acute kidney injury (AKI) is a severe and frequent condition in hospitalized patients. Currently, no efficient therapy of AKI is available. Therefore, efforts focus on early prevention and potentially early initiation of renal replacement therapy to improve the outcome in AKI. The detection of AKI in hospitalized patients implies the need for early, accurate, robust, and easily accessible biomarkers of AKI evolution and outcome prediction because only a narrow window exists to implement the earlier-described measures. Even more challenging is the multifactorial origin of AKI and the fact that the changes of molecular expression induced by AKI are difficult to distinguish from those of the diseases associated or causing AKI as shock or sepsis. During the past decade, a considerable number of protein biomarkers for AKI have been described and we expect from recent advances in the field of omics technologies that this number will increase further in the future and be extended to other sorts of biomolecules, such as RNAs, lipids, and metabolites. However, most of these biomarkers are poorly defined by their AKI-associated molecular context. In this review, we describe the state-of-the-art tissue and biofluid proteomic and metabolomic technologies and new bioinformatics approaches for proteomic and metabolomic pathway and molecular interaction analysis. In the second part of the review, we focus on AKI-associated proteomic and metabolomic biomarkers and briefly outline their pathophysiological context in AKI.

Novel Urinary Biomarkers For Improved Prediction Of Progressive Egfr Loss In Early Chronic Kidney Disease Stages And In High Risk Individuals Without Chronic Kidney Disease

Chronic kidney disease is associated with increased risk of CKD progression and death. Therapeutic approaches to limit progression are limited. Developing tools for the early identification of those individuals most likely to progress will allow enriching clinical trials in high risk early CKD patients. The CKD273 classifier is a panel of 273 urinary peptides that enables early detection of CKD and prognosis of progression. We have generated urine capillary electrophoresis-mass spectrometry-based peptidomics CKD273 subclassifiers specific for CKD stages to allow the early identification of patients at high risk of CKD progression. In the validation cohort, the CKD273 subclassifiers outperformed albuminuria and CKD273 classifier for predicting rapid loss of eGFR in individuals with baseline eGFR > 60 ml/min/1.73 m². In individuals with eGFR > 60 ml/min/1.73 m² and albuminuria <30 mg/day, the CKD273 subclassifiers predicted rapid eGFR loss with AUC ranging from 0.797 (0.743-0.844) to 0.736 (0.689-0.780). The association between CKD273 subclassifiers and rapid progression remained significant after adjustment for age, sex, albuminuria, DM, baseline eGFR, and systolic blood pressure. Urinary peptidomics CKD273 subclassifiers outperformed albuminuria and CKD273 classifier for predicting the risk of rapid CKD progression in individuals with eGFR > 60 ml/min/1.73 m². These CKD273 subclassifiers represented the earliest evidence of rapidly progressive CKD in non-albuminuric individuals with preserved renal function.

Renal Angina Is a Sensitive, but Nonspecific Identifier of Postcardiac Surgery Acute Kidney Injury

OBJECTIVES

Acute kidney injury (AKI) is a common complication of cardiac surgery, and early detection is difficult. This study was performed to determine the sensitivity, specificity, positive predictive value, negative predictive value, and statistical performance of renal angina (RA) as an early predictor of AKI in an adult cardiac surgical patient population.

DESIGN

Retrospective, nonrandomized, observational study.

SETTING

A single, university-affiliated, quaternary medical center.

PARTICIPANTS

The study comprised 324 consecutive patients undergoing coronary artery bypass grafting or cardiac valvular surgery from February 1 through July 30, 2014.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

One hundred-seven patients at moderate or high risk of developing postoperative renal injury were identified, 82 of whom met criteria for RA. The occurrence of RA was found to have an 80.9% sensitivity and 30.8% specificity for the prediction of AKI using Acute Kidney Injury Network criteria and 89.3% sensitivity and 27.8% specificity when paired with the Risk, Injury, Failure, Loss, End Stage Renal Disease criteria. A receiver operating characteristic area under the curve analysis revealed a nonsignificant predictive ability of 55.8% (95% confidence interval 0.47-0.65) when RA was paired with Acute Kidney Injury Network criteria; however, the receiver operating characteristic area under the curve was significant when paired with Risk, Injury, Failure, Loss, End Stage Renal Disease criteria, with a predictive ability of 0.586 (0.509-0.662).

CONCLUSIONS

RA is a sensitive, but nonspecific, predictor of postcardiac surgery AKI, with clinical utility most suited as a screening tool.

Diagnosis of cardiac surgery-associated acute kidney injury from functional to damage biomarkers

PURPOSE OF REVIEW

Acute kidney injury (AKI) occurs in up to 30% after cardiac surgery and is associated with adverse outcome. Currently, cardiac surgery-associated acute kidney injury (CSA-AKI) is diagnosed by Kidney Disease: Improving Global Outcomes criteria based on creatinine and urine output. To detect and treat AKI earlier, various biomarkers have been evaluated. This review addresses the current position of the two damage biomarkers neutrophil gelatinase-associated lipocalin (NGAL) and [TIMP-2] [IGFBP7] in clinical practice.

RECENT FINDINGS

We present an updated review on the use of blood and urinary NGAL in CSA-AKI. NGAL is a good predictor, and performs better in children than adults. There is a large variation in predictive ability, possibly caused by diversity of AKI definitions used, different time of measurement of NGAL, and lack of specificity of NGAL assays.Similarly, there are conflicting data on the predictive ability of urinary [TIMP-2] [IGFBP7] for CSA-AKI.Recently, both for NGAL and for urinary [TIMP-2] [IGFBP7], a set of actions, based on pretest assessment of risk for CSA-AKI and biomarker test results, was developed. These scores should be evaluated in prospective trials.

SUMMARY

NGAL and urinary [TIMP-2] [IGFBP7], in combination with pretest assessment, are promising tools for early detection and treatment in CSA-AKI.

Neutrophil gelatinase-associated lipocalin reflects inflammation and is not a reliable renal biomarker in neonates and infants after cardiopulmonary bypass: a prospective case-control study

Introduction Acute kidney injury is a frequent complication after cardiac surgery with cardiopulmonary bypass in infants. Neutrophil gelatinase-associated lipocalin has been suggested to be a promising early biomarker of impending acute kidney injury. On the other hand, neutrophil gelatinase-associated lipocalin has been shown to be elevated in systemic inflammatory diseases without renal impairment. In this secondary analysis of data from our previous study on acute kidney injury after infant cardiac surgery, our hypothesis was that neutrophil gelatinase-associated lipocalin may be associated with surgery-related inflammation.

METHODS

We prospectively enrolled 59 neonates and infants undergoing cardiopulmonary bypass surgery for CHD and measured neutrophil gelatinase-associated lipocalin in plasma and urine and interleukin-6 in the plasma. Values were correlated with postoperative acute kidney injury according to the paediatric Renal-Injury-Failure-Loss-Endstage classification.

RESULTS

Overall, 48% (28/59) of patients developed acute kidney injury. Of these, 50% (14/28) were classified as injury and 11% (3/28) received renal replacement therapy. Both plasma and urinary neutrophil gelatinase-associated lipocalin values were not correlated with acute kidney injury occurrence. Plasma neutrophil gelatinase-associated lipocalin showed a strong correlation with interleukin-6. Urinary neutrophil gelatinase-associated lipocalin values correlated with cardiopulmonary bypass time.

CONCLUSION

Our results suggest that plasma and urinary neutrophil gelatinase-associated lipocalin values are not reliable indicators of impending acute kidney injury in neonates and infants after cardiac surgery with cardiopulmonary bypass. Inflammation may have a major impact on plasma neutrophil gelatinase-associated lipocalin values in infant cardiac surgery. Urinary neutrophil gelatinase-associated lipocalin may add little prognostic value over cardiopulmonary bypass time.

Recent advances in CE and microchip-CE in clinical applications: 2014 to mid-2017

CE and microchip CE (ME) are powerful tools for the analysis of a number of different analytes and have been applied to a variety of clinical fields and human samples. This review will present an overview of the most recent applications of these techniques to different areas of clinical medicine during the period of 2014 to mid-2017. CE and ME have been applied to clinical chemistry, drug detection and monitoring, hematology, infectious diseases, oncology, endocrinology, neonatology, nephrology, and genetic screening. Samples examined range from serum, plasma, and urine to lest utilized materials such as tears, cerebral spinal fluid, sweat, saliva, condensed breath, single cells, and biopsy tissue. Examples of clinical applications will be given along with the various detection systems employed.