The impact of biomarkers of acute kidney injury on individual patient care

Plasma NGAL levels in stable kidney transplant recipients and the risk of allograft loss

BACKGROUND

The objective of this study was to investigate the utility of neutrophil gelatinase-associated lipocalin (NGAL) and calprotectin (CPT) to predict long-term graft survival in stable kidney transplant recipients (KTR).

METHODS

A total of 709 stable outpatient KTR were enrolled >2 months post-transplant. The utility of plasma and urinary NGAL (pNGAL, uNGAL) and plasma and urinary CPT at enrollment to predict death-censored graft loss was evaluated during a 58-month follow-up.

RESULTS

Among biomarkers, pNGAL showed the best predictive ability for graft loss and was the only biomarker with an area under the curve (AUC) > 0.7 for graft loss within 5 years. Patients with graft loss within 5 years (n = 49) had a median pNGAL of 304 [interquartile range (IQR) 235-358] versus 182 (IQR 128-246) ng/mL with surviving grafts (P < .001). Time-dependent receiver operating characteristic analyses at 58 months indicated an AUC for pNGAL of 0.795, serum creatinine-based Chronic Kidney Disease Epidemiology Collaboration estimated glomerular filtration rate (eGFR) had an AUC of 0.866. pNGAL added to a model based on conventional risk factors for graft loss with death as competing risk (age, transplant age, presence of donor-specific antibodies, presence of proteinuria, history of delayed graft function) had a strong independent association with graft loss {subdistribution hazard ratio (sHR) for binary log-transformed pNGAL [log2(pNGAL)] 3.4, 95% confidence interval (CI) 2.24-5.15, P < .0001}. This association was substantially attenuated when eGFR was added to the model [sHR for log2(pNGAL) 1.63, 95% CI 0.92-2.88, P = .095]. Category-free net reclassification improvement of a risk model including log2(pNGAL) in addition to conventional risk factors and eGFR was 54.3% (95% CI 9.2%-99.3%) but C-statistic did not improve significantly.

CONCLUSIONS

pNGAL was an independent predictor of renal allograft loss in stable KTR from one transplant center but did not show consistent added value when compared with baseline predictors including the conventional marker eGFR. Future studies in larger cohorts are warranted.

Performance of urinary C-C motif chemokine ligand 14 for the prediction of persistent acute kidney injury: a systematic review and meta-analysis

BACKGROUND

Urinary C-C motif chemokine ligand 14 (CCL14) has been described as an effective marker for delayed recovery of acute kidney injury (AKI), yet its efficacy has been found to vary between different trials. The goal of this research was to assess the predictive performance of urinary CCL14 as a marker for persistent AKI.

METHODS

In accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we searched the PubMed, Embase, and Cochrane databases up to April 2023 for studies of adults (> 18 years) that reported the diagnostic performance of urinary CCL14. The sensitivity, specificity, number of events, true positive, and false positive results were extracted and evaluated. 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 included six studies with 952 patients in this meta-analysis. The occurrence of persistent AKI among these patients was 39.6% (377/952). The pooled sensitivity and specificity results of urinary CCL14 in predicting persistent AKI were 0.81 (95% CI 0.72-0.87) and 0.71 (95% CI 0.53-0.84), respectively. The pooled positive likelihood ratio (LR) was 2.75 (95% CI 1.63-4.66), and the negative LR was 0.27 (95% CI 0.18-0.41). The HSROC with pooled diagnostic accuracy was 0.84.

CONCLUSION

Our results suggest that urinary CCL14 can be used as an effective marker for predicting persistent AKI.

Improving awareness of kidney function through electronic urine output monitoring: a comparative study

BACKGROUND

The current classification for acute kidney injury (AKI) according to the Kidney Disease: Improving Global Outcomes (KDIGO) criteria integrates both serum creatinine (SCr) and urine output (UO). Most reports on AKI claim to use KDIGO guidelines but fail to include the UO criterion. It has been shown that patients who had intensive UO monitoring, with or without AKI, had significantly less cumulative fluid volume and fluid overload, reduced vasopressor use, and improved 30-day mortality. We examined whether real-time monitoring of this simple, sensitive, and easy-to-use biomarker in the ICU led to more appropriate intervention by healthcare providers and better outcomes.  METHODS: RenalSense Clarity RMS Consoles were installed in the General ICU at the Hadassah Medical Center, Israel, from December 2019 to November 2020. The Clarity RMS system continuously and electronically monitors UO in real-time. 100 patients were randomly selected from this period as the study group (UO_elec) and compared to a matched control group (UO_manual) from the same period two years earlier. To test whether there was an association between oliguric hours and fluid treatment in each group, the correlation was calculated and analyzed for each of the different UO monitoring methods.

RESULTS

Therapeutic intervention: The correlation of the sum of all oliguric hours on Day 1 and 2 with the sum of any therapeutic intervention (fluid bolus or furosemide) showed a significant correlation for the study group UO_elec (P = 0.017). The matched control group UO_manual showed no such correlation (P = 0.932). Length of Stay (LOS): Median LOS [IQR] in the ICU of UO_elec versus UO_manual was 69.46 [44.7, 125.9] hours and 116.5 [62.46, 281.3] hours, respectively (P = 0.0002).

CONCLUSIONS

The results of our study strongly suggest that ICU patients had more meaningful and better medical intervention, and improved outcomes, with electronic UO monitoring than with manual monitoring.

Burn-Induced Acute Kidney Injury-Two-Lane Road: From Molecular to Clinical Aspects

Severe burn injuries lead to acute kidney injury (AKI) development, increasing the mortality risk up to 28-100%. In addition, there is an increase in hospitalization days and complications appearance. Various factors are responsible for acute or late AKI debut, like hypovolemia, important inflammatory response, excessive load of denatured proteins, sepsis, and severe organic dysfunction. The main measure to improve the prognosis of these patients is rapidly recognizing this condition and reversing the underlying events. For this reason, different renal biomarkers have been studied over the years for early identification of burn-induced AKI, like neutrophil gelatinase-associated lipocalin (NGAL), cystatin C, kidney injury molecule-1 (KIM-1), tissue inhibitor of metalloproteinase-2 (TIMP-2), interleukin-18 (IL-18), and insulin-like growth factor-binding protein 7 (IGFBP7). The fundamental purpose of these studies is to find a way to recognize and prevent acute renal injury progression early in order to decrease the risk of mortality and chronic kidney disease (CKD) onset.

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

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

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.

Plasma Endogenous Sulfur Dioxide: A Novel Biomarker to Predict Acute Kidney Injury in Critically Ill Patients

Purpose

Sulfur dioxide (SO₂) is a novel gaseous signaling molecule that plays an important role in inflammation, which contributes the pathogenesis of acute kidney injury (AKI). The aim of this study was to explore the predictive value of plasma SO₂ for AKI in high-risk patients.

Patients and Methods

A prospective cohort of 167 patients who underwent major noncardiac surgery was enrolled in the study. Plasma SO₂, urine neutrophil gelatinase-associated lipocalin (NGAL), tissue inhibitor of metalloproteinase-2 (TIMP-2), and insulin-like growth factor-binding protein 7 (IGFBP7) levels were detected immediately after the operation. The primary endpoint was new-onset AKI within 72 h after admission. The ability of biomarkers including SO₂ and a clinical risk model to predict AKI was compared by receiver operator characteristic (ROC) curve analysis and decision curve analysis (DCA), additional contributions were evaluated by integrated discrimination improvement (IDI) and net reclassification improvement (NRI) analyses.

Results

A total of 61 (36.5%) patients developed AKI within 72 h of surgery. Compared to NGAL and [TIMP-2]·[IGFBP7], SO₂ showed better predictive ability for new-onset AKI with an area under the ROC curve of 0.771 (95% confidence interval: 0.700–0.832, p<0.001). The improvement in predictive value by including SO₂ in the clinical risk model was supported by NRI (0.28; P=0.04) and IDI (0.15; P<0.001) analyses. The net benefit of the combination of SO₂ and clinical variables was the max in DCA.

Conclusion

Plasma SO₂ shows a useful value for predicting new-onset AKI, and improved AKI prediction based on clinical variables, which can guide the implementation of preventive measures for high-risk patients.

Acute kidney injury after lung transplantation: a narrative review

Acute kidney injury (AKI) is a commonly recognized complication after lung transplantation (LT) and is related to increased mortality and morbidity. With the improvement of survival after LT and the increasing number of lung transplant recipients, the detrimental impact of current management on renal function has become increasingly apparent. Multifarious risk factors in the perioperative setting contribute to the development of AKI, including the preoperative status and complications of the recipient, complex perioperative problems especially hemodynamic fluctuation, and exposure to nephrotoxic agents, mainly calcineurin inhibitors (CNIs) and antimicrobial drugs. Identification and minimization of the effects of these risk factors can relieve AKI severity and incidence in high-risk patients. Close monitoring of urine output and serum creatinine (sCr) levels and of specific biomarkers may promote early recognition of AKI and rapid nephrology intervention to improve outcomes. This review summarizes advances in the epidemiology, diagnostic criteria, biological markers of AKI, and further recommends appropriate treatment strategies for the long-term management of AKI related manifestations in lung transplant recipients. Future work will need to focus on developing more accurate measures of renal function and identifying patients before the occurrence of early renal damage. Combining renal protection strategies with the use of new biomarkers to develop early kidney risk identification and protection protocols is a promising idea that requires further investigation.

Novel predictive biomarkers for acute injury superimposed on chronic kidney disease

INTRODUCTION AND OBJECTIVES

Chronic kidney disease (CKD) is a risk factor for the development of acute kidney injury (AKI). Recent studies have revealed numerous biomarkers eligible for AKI prediction. However, the expression and performance of AKI biomarkers in acute injury superimposed on preexisting CKD (AonC) remain elusive. The aim of this study was to evaluate whether biomarkers which robustly expressed in acute kidney injury could predict acute injury based on CKD.

MATERIALS AND METHODS

Mice were classified into cohorts: AKI, CKD, AonC and sham. The AonC model mice were subjected to renal bilateral ischemia/reperfusion (I/R) injury fourteen days after intraperitoneally administrated with 20mg/kg aristolochic acid. Severity of acute ischemic injury was stratified by clamping the dissected bilateral renal arteries with non-traumatic microvascular clips for 20 or 35min. The AKI mice were induced with renal bilateral I/R injury and CKD mice were crafted with 20mg/kg aristolochic acid administrated intraperitoneally. Histology, genetic and protein expression of biomarkers were measured in three cohorts.

RESULTS

We found that serum creatinine dramatically increased in severe (sAonC) but not in moderate (mAonC) injury mice. Upregulation of Kidney injury molecule-1 (KIM-1) mRNA, tissue inhibitor of metalloproteinase-2 (TIMP-2), Syndecan-1 (SDC-1) mRNA and insulin-like growth factor binding protein-7 (IGFBP7) protein indicated the onset of mAonC. An increase in neutrophil gelatinase-associated lipocalin (NGAL), rhomboid-like protein 2 (RHBDL2), Syndecan-1 (SDC-1) mRNA and protein, and a decrease in IGFBP7 protein were associated with sAonC.

CONCLUSIONS

Our study revealed the variational expression of AKI biomarkers in AonC kidneys, and uncovered IGFBP7 protein can be used as a sensitive biomarker to predict and differentiate AonC severity. The performance of RHBDL2 and SDC-1 in predicting severe AonC was promising, providing new biomarkers for predicting AonC.

Rational selection of a biomarker panel targeting unmet clinical needs in kidney injury

The pipeline of biomarker translation from bench to bedside is challenging and limited biomarkers have been adopted to routine clinical care. Ideally, biomarker research and development should be driven by unmet clinical needs in health care. To guide researchers, clinical chemists and clinicians in their biomarker research, the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) has developed a structured questionnaire in which the clinical gaps in current clinical pathways are identified and desirable performance specifications are predefined. In kidney injury, the high prevalence of the syndrome acute kidney injury (AKI) in the hospital setting has a significant impact on morbidity, patient survival and health care costs, but the use of biomarkers indicating early kidney injury in daily patient care remains limited. Routinely, medical labs measure serum creatinine, which is a functional biomarker, insensitive for detecting early kidney damage and cannot distinguish between renal and prerenal AKI. The perceived unmet clinical needs in kidney injury were identified through the EFLM questionnaire. Nephrologists within our tertiary care hospital emphasized that biomarkers are needed for (1) early diagnosis of in-hospital AKI after a medical insult and in critically ill patients, (2) risk stratification for kidney injury prior to a scheduled (elective) intervention, (3) kidney injury monitoring in patients scheduled to receive nephrotoxic medication and after kidney transplantation and (4) differentiation between prerenal AKI and structural kidney damage. The biomarker search and selection strategy resulted in a rational selection of an eleven-protein urinary panel for kidney injury that target these clinical needs. To assess the clinical utility of the proposed biomarker panel in kidney injury, a multiplexed LC-MS test is now in development for the intended translational research.

Artificial Intelligence in Acute Kidney Injury: From Static to Dynamic Models

Artificial intelligence (AI) is the development of computer systems that normally require human intelligence. In the field of acute kidney injury (AKI) AI has led to an evolution of risk prediction models. In the past, static prediction models were developed using baseline (eg, preoperative) data to evaluate AKI risk. Newer models which incorporated baseline as well as evolving data collected during a hospital admission have shown improved predicative abilities. In this review, we will summarize the advances made in AKI risk prediction over the last several years, including a shift toward more dynamic, real-time, electronic medical record-based models. In addition, we will be discussing the role of electronic AKI alerts and decision support tools. Recent studies have demonstrated improved patient outcomes through the use of these tools which monitor for nephrotoxin medication exposures as well as provide kidney focused care bundles for patients at high risk for severe AKI. Finally, we will briefly discuss the pitfalls and implications of implementing these scores, alerts, and support tools.

Clinical phenotypes of acute kidney injury are associated with unique outcomes in critically ill septic children

BACKGROUND

Assessment of acute kidney injury (AKI) in septic patients remains imprecise. In adults, the classification of septic patients by clinical AKI phenotypes (severity and timing) demonstrates unique associations with patient outcome vs. broadly defined AKI.

METHODS

In a multinational prospective observational study, AKI diagnosis in critically ill septic children was stratified by duration (transient vs. persistent) and severity (mild vs. severe by creatinine change and urine output). The outcomes of interest were mortality and intensive care unit resource complexity at 28 days.

RESULTS

Seven hundred and fifty-seven septic children were studied (male 52.7%, age 4.6 years (1.5-11.9)). Mortality (overall 12.1%) was different between severe AKI and mild AKI (18.3 vs. 4.4%, p < 0.001) as well as intensive care unit (ICU) complexity (overall 34.5%, 45 vs. 21.7%, p < 0.001). Patients with Persistent AKI had fewer ICU-free days (17 (7, 21) vs. 24 (17, 26), p < 0.001) and higher ICU complexity (52.8 vs. 22.9%, p = 0.002) than transient AKI, even after exclusion of patients with early mortality. AKI phenotypes incorporating temporal and severity data correlate with unique survival (range 4.4-21.6%) and ICU-free days (range of 15-25 days) CONCLUSIONS: The outcome of septic children with AKI changes by clinical phenotype. Our findings underscore the importance of prognostic enrichment in sepsis and AKI for the purpose of trial design and patient management.

IMPACT

Although AKI occurs commonly in patients with sepsis (S-AKI), outcomes for children with S-AKI varies based on the severity and timing of the AKI. Existing S-AKI pediatric data utilize a broad singular definition of kidney injury. Increasing the precision of AKI classification results in a new understanding of how S-AKI associates with patient outcome. A refined classification of S-AKI identifies subgroups of children, making possible a targeted and a personalized medicine approach to S-AKI study and management.

Clinical adoption of Nephrocheck® in the early detection of acute kidney injury

Acute kidney injury is a common complication of acute illnesses and is associated with increased morbidity and mortality. Over the past years several acute kidney injury biomarkers for diagnostication, decision-making processes, and prognosis of acute kidney injury and its outcomes have been developed and validated. Among these biomarkers, tissue inhibitor of metalloproteinase-2 (TIMP-2) and insulin-like growth factor-binding protein 7 (IGFBP7), the so-called cell cycle arrest biomarkers, showed a superior profile of accuracy and stability even in patients with substantial comorbidities. Therefore, in 2014, the US Food and Drug Administration approved the use of the product of TIMP-2 and IGFBP7 ([TIMP-2] × [IGFBP7]), known as cell cycle arrest biomarkers, to aid critical care physicians and nephrologists in the early prediction of acute kidney injury in the critical care setting. To date, Nephrocheck® is the only commercially available test for [TIMP-2] × [IGFBP7]. In this narrative review, we describe the growing clinical and investigational momentum of biomarkers, focusing on [TIMP-2] × [IGFBP7], as one of the most promising candidate biomarkers. Additionally, we review the current state of clinical implementation of Nephrocheck®.

Prognostic association of routinely measured biomarkers in patients admitted to critical care: a systematic review

PURPOSE

To examine reported prognostic associations of routine blood measurements in the intensive care unit.

MATERIALS AND METHODS

We searched PubMed, EMBASE through 28^th May 2020 to identify all studies in adult critical care investigating associations between parameters measured routinely in whole blood, plasma or serum, and length of stay or mortality. Registration: PROSPERO; CRD42019122058.

RESULTS

A total of 128 studies, reporting 28 different putative prognostic biomarkers, met eligibility criteria. Those most frequently examined were red cell distribution width, neutrophil-to-lymphocyte ratio, C-reactive protein, and platelet count. A higher red cell distribution width, a lower platelet count, and a higher neutrophil-to-lymphocyte ratio were consistently associated with both increased mortality and length of stay. A lower level of albumin was consistently associated with greater mortality. C-reactive protein was inconsistent. Most studies (n = 110) used regression modelling with wide variation in variable selection and covariate-adjustment; none externally validated the proposed predictive models.

CONCLUSIONS

Simple regression models have so far proved inadequate for the complexity of data available from routine blood sampling in critical care. Adoption of a direct causal framework may help better assess mechanistic processes, aid design of future studies, and guide clinical decision making using routine data.

Internal and External Validation of a Machine Learning Risk Score for Acute Kidney Injury

IMPORTANCE

Acute kidney injury (AKI) is associated with increased morbidity and mortality in hospitalized patients. Current methods to identify patients at high risk of AKI are limited, and few prediction models have been externally validated.

OBJECTIVE

To internally and externally validate a machine learning risk score to detect AKI in hospitalized patients.

DESIGN, SETTING, AND PARTICIPANTS

This diagnostic study included 495 971 adult hospital admissions at the University of Chicago (UC) from 2008 to 2016 (n = 48 463), at Loyola University Medical Center (LUMC) from 2007 to 2017 (n = 200 613), and at NorthShore University Health System (NUS) from 2006 to 2016 (n = 246 895) with serum creatinine (SCr) measurements. Patients with an SCr concentration at admission greater than 3.0 mg/dL, with a prior diagnostic code for chronic kidney disease stage 4 or higher, or who received kidney replacement therapy within 48 hours of admission were excluded. A simplified version of a previously published gradient boosted machine AKI prediction algorithm was used; it was validated internally among patients at UC and externally among patients at NUS and LUMC.

MAIN OUTCOMES AND MEASURES

Prediction of Kidney Disease Improving Global Outcomes SCr-defined stage 2 AKI within a 48-hour interval was the primary outcome. Discrimination was assessed by the area under the receiver operating characteristic curve (AUC).

RESULTS

The study included 495 971 adult admissions (mean [SD] age, 63 [18] years; 87 689 [17.7%] African American; and 266 866 [53.8%] women) across 3 health systems. The development of stage 2 or higher AKI occurred in 15 664 of 48 463 patients (3.4%) in the UC cohort, 5711 of 200 613 (2.8%) in the LUMC cohort, and 3499 of 246 895 (1.4%) in the NUS cohort. In the UC cohort, 332 patients (0.7%) required kidney replacement therapy compared with 672 patients (0.3%) in the LUMC cohort and 440 patients (0.2%) in the NUS cohort. The AUCs for predicting at least stage 2 AKI in the next 48 hours were 0.86 (95% CI, 0.86-0.86) in the UC cohort, 0.85 (95% CI, 0.84-0.85) in the LUMC cohort, and 0.86 (95% CI, 0.86-0.86) in the NUS cohort. The AUCs for receipt of kidney replacement therapy within 48 hours were 0.96 (95% CI, 0.96-0.96) in the UC cohort, 0.95 (95% CI, 0.94-0.95) in the LUMC cohort, and 0.95 (95% CI, 0.94-0.95) in the NUS cohort. In time-to-event analysis, a probability cutoff of at least 0.057 predicted the onset of stage 2 AKI a median (IQR) of 27 (6.5-93) hours before the eventual doubling in SCr concentrations in the UC cohort, 34.5 (19-85) hours in the NUS cohort, and 39 (19-108) hours in the LUMC cohort.

CONCLUSIONS AND RELEVANCE

In this study, the machine learning algorithm demonstrated excellent discrimination in both internal and external validation, supporting its generalizability and potential as a clinical decision support tool to improve AKI detection and outcomes.

Erythrocyte transfusion limits the role of elevated red cell distribution width on predicting cardiac surgery associated acute kidney injury

BACKGROUND

Acute kidney injury (AKI) is one of the more serious complications after cardiac surgery. Elevated red cell distribution width (RDW) was reported as a predictor for cardiac surgery associated acute kidney injury (CSAKI). However, the increment of RDW by erythrocyte transfusion makes its prognostic role doubtful. The aim of this study is to elucidate the impact of erythrocyte transfusion on the prognostic role of elevated RDW for predicting CSAKI.

METHODS

A total of 3207 eligible patients who underwent cardiac surgery during 2016-2017 were enrolled. Changes of RDW was defined as the difference between preoperative RDW and RDW measured 24 h after cardiac surgery. The primary outcome was CSAKI which was defined by the Kidney Disease: Improving Global Outcomes Definition and Staging (KDIGO) criteria. Univariate and multivariate analysis were performed to identify predictors for CSAKI.

RESULTS

The incidence of CSAKI was 38.07% and the mortality was 1.18%. CSAKI patients had higher elevated RDW than those without CSAKI (0.65% vs. 0.39%, p < 0.001). Multivariate regression showed that male, age, New York Heat Association classification 3-4, elevated RDW, estimated glomerular filtration rate < 60 mL/min/1.73 m2, cardiopulmonary bypass time > 120 min and erythrocyte transfusion were associated with CSAKI. Subgroup analysis showed elevated RDW was an independent predictor for CSAKI in the non-transfused subset (adjusted odds ratio: 1.616, p < 0.001) whereas no significant association between elevated RDW and CSAKI was found in the transfused patients (odds ratio: 1.040, p = 0.497).

CONCLUSIONS

Elevated RDW is one of the independent predictors of CSAKI in the absence of erythrocyte transfusion, which limits the prognostic role of the former on predicting CSAKI.