Developing risk prediction models for kidney injury and assessing incremental value for novel biomarkers

Serum Matrix Metalloproteinase 7 and accelerated GFR decline in a general non-diabetic population

BACKGROUND

Age-related reduction of glomerular filtration rate (GFR) is a major contributor to the global chronic kidney disease (CKD) epidemic. We investigated whether baseline serum levels of the pro-fibrotic matrix metalloproteinase 2 (MMP2), MMP7 and their inhibitor, tissue inhibitor of metalloproteinase 1 (TIMP1), which mediates fibrosis development in aging animals, were associated with GFR decline in a general nondiabetic population.

METHODS

In the Renal Iohexol Clearance Survey (RENIS), we measured GFR using iohexol clearance in 1627 subjects aged 50-64 without self-reported diabetes, kidney or cardiovascular disease. After a median of 5.6 years, 1324 had follow-up GFR measurements. Using linear mixed models and logistic regression analyses, we evaluated the association of MMP7, MMP2 and TIMP1 with the mean GFR decline rate, risk of accelerated GFR decline (defined as subjects with the 10% steepest GFR slopes: ≥1.8 ml/min/1.73 m2/year) and incident CKD (GFR <60 ml/min/1.73 m2 and/or urinary albumin to creatinine ratio (ACR) ≥3.0 mg/mmol).

RESULTS

Higher MMP7 levels (per SD increase of MMP7) were associated with steeper GFR decline rates (-0.23 ml/min/1.73m2/year [95% confidence interval, -0.34 to -0.12]) and increased risk of accelerated GFR decline and incident CKD, (odds ratios; 1.58 (1.30-1.93) and 1.45 (1.05-2.01), respectively, in a model adjusted for age, sex, baseline GFR, ACR and cardiovascular risk factors). MMP2 and TIMP1 showed no association with GFR decline or incident CKD.

CONCLUSION

The pro-fibrotic biomarker MMP7, but not MMP2 or TIMP1, is associated with increased risk of accelerated GFR decline and incident CKD in middle-aged persons from the general population.

Predictive Value of Plasma NGAL:Hepcidin-25 for Major Adverse Kidney Events After Cardiac Surgery with Cardiopulmonary Bypass: A Pilot Study

Background

Neutrophil gelatinase-associated lipocalin (NGAL) and hepcidin-25 are involved in catalytic iron-related kidney injury after cardiac surgery with cardiopulmonary bypass. We explored the predictive value of plasma NGAL, plasma hepcidin-25, and the plasma NGAL:hepcidin-25 ratio for major adverse kidney events (MAKE) after cardiac surgery.

Methods

We compared the predictive value of plasma NGAL, hepcidin-25, and plasma NGAL:hepcidin-25 with that of serum creatinine (Cr) and urinary output and protein for primary-endpoint MAKE (acute kidney injury [AKI] stages 2 and 3, persistent AKI >48 hours, acute dialysis, and in-hospital mortality) and secondary-endpoint AKI in 100 cardiac surgery patients at intensive care unit (ICU) admission. We performed ROC curve, logistic regression, and reclassification analyses.

Results

At ICU admission, plasma NGAL, plasma NGAL:hepcidin-25, plasma interleukin-6, and Cr predicted MAKE (area under the ROC curve [AUC]: 0.77, 0.79, 0.74, and 0.74, respectively) and AKI (0.73, 0.89, 0.70, and 0.69). For AKI prediction, plasma NGAL:hepcidin-25 had a higher discriminatory power than Cr (AUC difference 0.26 [95% CI 0.00-0.53]). Urinary output and protein, plasma lactate, C-reactive protein, creatine kinase myocardial band, and brain natriuretic peptide did not predict MAKE or AKI (AUC <0.70). Only plasma NGAL:hepcidin-25 correctly reclassified patients according to their MAKE and AKI status (category-free net reclassification improvement: 0.82 [95% CI 0.12-1.52], 1.03 [0.29-1.77]). After adjustment to the Cleveland risk score, plasma NGAL:hepcidin-25 ≥0.9 independently predicted MAKE (adjusted odds ratio 16.34 [95% CI 1.77-150.49], P=0.014).

Conclusions

Plasma NGAL:hepcidin-25 is a promising marker for predicting postoperative MAKE.

miRNA biomarkers in renal disease

Chronic kidney disease (CKD), which is characterized by the gradual loss of kidney function, is a growing worldwide problem due to CKD-related morbidity and mortality. There are no reliable and early biomarkers enabling the monitoring, the stratification of CKD progression and the estimation of the risk of CKD-related complications, and therefore, the search for such molecules is still going on. Numerous studies have provided evidence that miRNAs are potentially important particles in the CKD field. Studies indicate that some miRNA levels can be increased in patients with CKD stages III–V and hemodialysis and decreased in renal transplant recipients (miR-143, miR-145 and miR-223) as well as elevated in patients with CKD stages III–V, decreased in hemodialysis patients and even more markedly decreased in renal transplant recipients (miR-126 and miR-155). miRNA have great potential of being sensitive and specific biomarkers in kidney diseases as they are tissue specific and stable in various biological materials. Some promising non-invasive miRNA biomarkers have already been recognized in renal disease with the potential to enhance diagnostic accuracy, predict prognosis and monitor the course of disease. However, large-scale clinical trials enrolling heterogeneous patients are required to evaluate the clinical value of miRNAs.

Urine Metabolite Levels, Adverse Kidney Outcomes, and Mortality in CKD Patients: A Metabolome-wide Association Study

RATIONALE & OBJECTIVE

Mechanisms underlying the variable course of disease progression in patients with chronic kidney disease (CKD) are incompletely understood. The aim of this study was to identify novel biomarkers of adverse kidney outcomes and overall mortality, which may offer insights into pathophysiologic mechanisms.

STUDY DESIGN

Metabolome-wide association study.

SETTING & PARTICIPANTS

5,087 patients with CKD enrolled in the observational German Chronic Kidney Disease Study.

EXPOSURES

Measurements of 1,487 metabolites in urine.

OUTCOMES

End points of interest were time to kidney failure (KF), a combined end point of KF and acute kidney injury (KF+AKI), and overall mortality.

ANALYTICAL APPROACH

Statistical analysis was based on a discovery-replication design (ratio 2:1) and multivariable-adjusted Cox regression models.

RESULTS

After a median follow-up of 4 years, 362 patients died, 241 experienced KF, and 382 experienced KF+AKI. Overall, we identified 55 urine metabolites whose levels were significantly associated with adverse kidney outcomes and/or mortality. Higher levels of C-glycosyltryptophan were consistently associated with all 3 main end points (hazard ratios of 1.43 [95% CI, 1.27-1.61] for KF, 1.40 [95% CI, 1.27-1.55] for KF+AKI, and 1.47 [95% CI, 1.33-1.63] for death). Metabolites belonging to the phosphatidylcholine pathway showed significant enrichment. Members of this pathway contributed to the improvement of the prediction performance for KF observed when multiple metabolites were added to the well-established Kidney Failure Risk Equation.

LIMITATIONS

Findings among patients of European ancestry with CKD may not be generalizable to the general population.

CONCLUSIONS

Our comprehensive screen of the association between urine metabolite levels and adverse kidney outcomes and mortality identifies metabolites that predict KF and represents a valuable resource for future studies of biomarkers of CKD progression.

Uromodulin to Osteopontin Ratio in Deceased Donor Urine Is Associated With Kidney Graft Outcomes

BACKGROUND

Deceased-donor kidneys experience extensive injury, activating adaptive and maladaptive pathways therefore impacting graft function. We evaluated urinary donor uromodulin (UMOD) and osteopontin (OPN) in recipient graft outcomes.

METHODS

Primary outcomes: all-cause graft failure (GF) and death-censored GF (dcGF). Secondary outcomes: delayed graft function (DGF) and 6-month estimated glomerular filtration rate (eGFR). We randomly divided our cohort of deceased donors and recipients into training and test datasets. We internally validated associations between donor urine UMOD and OPN at time of procurement, with our primary outcomes. The direction of association between biomarkers and GF contrasted. Subsequently, we evaluated UMOD:OPN ratio with all outcomes. To understand these mechanisms, we examined the effect of UMOD on expression of major histocompatibility complex II in mouse macrophages.

RESULTS

Doubling of UMOD increased dcGF risk (adjusted hazard ratio [aHR], 1.1; 95% confidence interval [CI], 1.02-1.2), whereas OPN decreased dcGF risk (aHR, 0.94; 95% CI, 0.88-1). UMOD:OPN ratio ≤3 strengthened the association, with reduced dcGF risk (aHR, 0.57; 0.41-0.80) with similar associations for GF, and in the test dataset. A ratio ≤3 was also associated with lower DGF (aOR, 0.73; 95% CI, 0.60-0.89) and higher 6-month eGFR (adjusted β coefficient, 3.19; 95% CI, 1.28-5.11). UMOD increased major histocompatibility complex II expression elucidating a possible mechanism behind UMOD's association with GF.

CONCLUSIONS

UMOD:OPN ratio ≤3 was protective, with lower risk of DGF, higher 6-month eGFR, and improved graft survival. This ratio may supplement existing strategies for evaluating kidney quality and allocation decisions regarding deceased-donor kidney transplantation.

Controversies in acute kidney injury: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Conference

In 2012, Kidney Disease: Improving Global Outcomes (KDIGO) published a guideline on the classification and management of acute kidney injury (AKI). The guideline was derived from evidence available through February 2011. Since then, new evidence has emerged that has important implications for clinical practice in diagnosing and managing AKI. In April of 2019, KDIGO held a controversies conference entitled Acute Kidney Injury with the following goals: determine best practices and areas of uncertainty in treating AKI; review key relevant literature published since the 2012 KDIGO AKI guideline; address ongoing controversial issues; identify new topics or issues to be revisited for the next iteration of the KDIGO AKI guideline; and outline research needed to improve AKI management. Here, we present the findings of this conference and describe key areas that future guidelines may address.

Urine NGAL as a biomarker for septic AKI: a critical appraisal of clinical utility-data from the observational FINNAKI study

Background

Neutrophil gelatinase-associated lipocalin (NGAL) is released from kidney tubular cells under stress as well as from neutrophils during inflammation. It has been suggested as a biomarker for acute kidney injury (AKI) in critically ill patients with sepsis. To evaluate clinical usefulness of urine NGAL (uNGAL), we post-hoc applied recently introduced statistical methods to a sub-cohort of septic patients from the prospective observational Finnish Acute Kidney Injury (FINNAKI) study. Accordingly, in 484 adult intensive care unit patients with sepsis by Sepsis-3 criteria, we calculated areas under the receiver operating characteristic curves (AUCs) for the first available uNGAL to assess discrimination for four outcomes: AKI defined by Kidney Disease: Improving Global Outcomes (KDIGO) criteria, severe (KDIGO 2–3) AKI, and renal replacement therapy (RRT) during the first 3 days of intensive care, and mortality at day 90. We constructed clinical prediction models for the outcomes and used risk assessment plots and decision curve analysis with predefined threshold probabilities to test whether adding uNGAL to the models improved reclassification or decision making in clinical practice.

Results

Incidences of AKI, severe AKI, RRT, and mortality were 44.8% (217/484), 27.7% (134/484), 9.5% (46/484), and 28.1% (136/484). Corresponding AUCs for uNGAL were 0.690, 0.728, 0.769, and 0.600. Adding uNGAL to the clinical prediction models improved discrimination of AKI, severe AKI, and RRT. However, the net benefits for the new models were only 1.4% (severe AKI and RRT) to 2.5% (AKI), and the number of patients needed to be tested per one extra true-positive varied from 40 (AKI) to 74 (RRT) at the predefined threshold probabilities.

Conclusions

The results of the recommended new statistical methods do not support the use of uNGAL in critically ill septic patients to predict AKI or clinical outcomes.

Developing Biomarker Panels to Predict Progression of Acute Kidney Injury After Cardiac Surgery

Introduction

Acute kidney injury (AKI) is a frequent complication of cardiac surgery, but only a fraction of cardiac surgery patients that experience postoperative AKI have progression to more severe stages. Biomarkers that can distinguish patients that will experience progression of AKI are potentially useful for clinical care and/or the development of therapies.

Methods

Data come from a prospective cohort study of cardiac surgery patients; the analytic dataset contained data from 354 cardiac surgery patients meeting criteria for AKI following surgery. Candidate predictors were 38 biomarkers of kidney function, insult, or injury measured at the time of AKI diagnosis. The outcome was AKI progression, defined as worsening of AKI Network stage. We investigated combining biomarkers with Bayesian model averaging (BMA) and random forests of classification trees, with and without center transformation. For both approaches, we used resampling-based methods to avoid optimistic bias in our assessment of model performance.

Results

BMA yielded a combination of 3 biomarkers and an optimism-corrected estimated area under the receiver operating characteristic curve (AUC) of 0.75 (95% confidence interval [CI]: 0.68, 0.82). The random forests approach, which nominally uses all biomarkers, had an estimated AUC of 0.74 (95% CI: 0.66, 0.82). A second application of random forests applied to biomarker values after a center-specific transformation had an estimated AUC of 0.80 (95% CI: 0.72, 0.88).

Conclusion

These findings suggest that the application of advanced statistical techniques to combine biomarkers offers only modest improvements over use of single biomarkers alone. This exemplifies a common experience in biomarker research: combinations of modestly performing biomarkers often also have modest performance.

Accounting for established predictors with the multistep elastic net

Multivariable models for prediction or estimating associations with an outcome are rarely built in isolation. Instead, they are based upon a mixture of covariates that have been evaluated in earlier studies (eg, age, sex, or common biomarkers) and covariates that were collected specifically for the current study (eg, a panel of novel biomarkers or other hypothesized risk factors). For that context, we present the multistep elastic net (MSN), which considers penalized regression with variables that can be qualitatively grouped based upon their degree of prior research support: established predictors vs unestablished predictors. The MSN chooses between uniform penalization of all predictors (the standard elastic net) and weaker penalization of the established predictors in a cross-validated framework and includes the option to impose zero penalty on the established predictors. In simulation studies that reflect the motivating context, we show the comparability or superiority of the MSN over the standard elastic net, the Integrative LASSO with Penalty Factors, the sparse group lasso, and the group lasso, and we investigate the importance of not penalizing the established predictors at all. We demonstrate the MSN to update a prediction model for pediatric ECMO patient mortality.

Early prediction of acute kidney injury following ICU admission using a multivariate panel of physiological measurements

BACKGROUND

The development of acute kidney injury (AKI) during an intensive care unit (ICU) admission is associated with increased morbidity and mortality.

METHODS

Our objective was to develop and validate a data driven multivariable clinical predictive model for early detection of AKI among a large cohort of adult critical care patients. We utilized data form the Medical Information Mart for Intensive Care III (MIMIC-III) for all patients who had a creatinine measured for 3 days following ICU admission and excluded patients with pre-existing condition of Chronic Kidney Disease and Acute Kidney Injury on admission. Data extracted included patient age, gender, ethnicity, creatinine, other vital signs and lab values during the first day of ICU admission, whether the patient was mechanically ventilated during the first day of ICU admission, and the hourly rate of urine output during the first day of ICU admission.

RESULTS

Utilizing the demographics, the clinical data and the laboratory test measurements from Day 1 of ICU admission, we accurately predicted max serum creatinine level during Day 2 and Day 3 with a root mean square error of 0.224 mg/dL. We demonstrated that using machine learning models (multivariate logistic regression, random forest and artificial neural networks) with demographics and physiologic features can predict AKI onset as defined by the current clinical guideline with a competitive AUC (mean AUC 0.783 by our all-feature, logistic-regression model), while previous models aimed at more specific patient cohorts.

CONCLUSIONS

Experimental results suggest that our model has the potential to assist clinicians in identifying patients at greater risk of new onset of AKI in critical care setting. Prospective trials with independent model training and external validation cohorts are needed to further evaluate the clinical utility of this approach and potentially instituting interventions to decrease the likelihood of developing AKI.

Statistical methods for building better biomarkers of chronic kidney disease

The last two decades have witnessed an explosion in research focused on the development and assessment of novel biomarkers for improved prognosis of diseases. As a result, best practice standards guiding biomarker research have undergone extensive development. Currently, there is great interest in the promise of biomarkers to enhance research efforts and clinical practice in the setting of chronic kidney disease, acute kidney injury, and glomerular disease. However, some have questioned whether biomarkers currently add value to the clinical practice of nephrology. The current state of the art pertaining to statistical analyses regarding the use of such measures is critical. In December 2014, the National Institute of Diabetes and Digestive and Kidney Diseases convened a meeting, "Toward Building Better Biomarker Statistical Methodology," with the goals of summarizing the current best practice recommendations and articulating new directions for methodological research. This report summarizes its conclusions and describes areas that need attention. Suggestions are made regarding metrics that should be commonly reported. We outline the methodological issues related to traditional metrics and considerations in prognostic modeling, including discrimination and case mix, calibration, validation, and cost-benefit analysis. We highlight the approach to improved risk communication and the value of graphical displays. Finally, we address some "new frontiers" in prognostic biomarker research, including the competing risk framework, the use of longitudinal biomarkers, and analyses in distributed research networks.

Prevalence and risk factors for acute kidney injury among trauma patients: a multicenter cohort study

Background

Organ failure, including acute kidney injury (AKI), is the third leading cause of death after bleeding and brain injury in trauma patients. We sought to assess the prevalence, the risk factors and the impact of AKI on outcome after trauma.

Methods

We performed a retrospective analysis of prospectively collected data from a multicenter trauma registry. AKI was defined according to the risk, injury, failure, loss of kidney function and end-stage kidney disease (RIFLE) classification from serum creatinine only. Prehospital and early hospital risk factors for AKI were identified using logistic regression analysis. The predictive models were internally validated using bootstrapping resampling technique.

Results

We included 3111 patients in the analysis. The incidence of AKI was 13% including 7% stage R, 3.7% stage I and 2.3% stage F. AKI incidence rose to 42.5% in patients presenting with hemorrhagic shock; 96% of AKI occurred within the 5 first days after trauma. In multivariate analysis, prehospital variables including minimum prehospital mean arterial pressure, maximum prehospital heart rate, secondary transfer to the trauma center and data early collected after hospital admission including injury severity score, renal trauma, blood lactate and hemorrhagic shock were independent risk factors in the models predicting AKI. The model had good discrimination with area under the receiver operating characteristic curve of 0.85 (0.82–0.88) to predict AKI stage I or F and 0.80 (0.77–0.83) to predict AKI of all stages. Rhabdomyolysis severity, assessed by the creatine kinase peak, was an additional independent risk factor for AKI when it was forced into the model (OR 1.041 (1.015–1.069) per step of 1000 U/mL, p < 0.001). AKI was independently associated with a twofold increase in ICU mortality.

Conclusions

AKI has an early onset and is independently associated with mortality in trauma patients. Its prevalence varies by a factor 3 according to the severity of injuries and hemorrhage. Prehospital and early hospital risk factors can provide good performance for early prediction of AKI after trauma. Hence, studies aiming to prevent AKI should target patients at high risk of AKI and investigate therapies early in the course of trauma care.

Electronic supplementary material

The online version of this article (10.1186/s13054-018-2265-9) contains supplementary material, which is available to authorized users.

Utility of Biomarkers to Improve Prediction of Readmission or Mortality After Cardiac Surgery

BACKGROUND

Hospital readmission within 30 days is associated with higher risks of complications, death, and increased costs. Accurate statistical models to stratify the risk of 30-day readmission or death after cardiac surgery could help clinical teams focus care on those patients at highest risk. We hypothesized biomarkers could improve prediction for readmission or mortality.

METHODS

Levels of ST2, galectin-3, N-terminal pro-brain natriuretic peptide, cystatin C, interleukin-6, and interleukin-10 were measured in samples from 1,046 patients discharged after isolated coronary artery bypass graft surgery from eight medical centers, with external validation in 1,194 patients from five medical centers. Thirty-day readmission or mortality were ascertained using Medicare, state all-payer claims, and the National Death Index. We tested and externally validated the clinical models and the biomarker panels using area under the receiver-operating characteristics (AUROC) statistics.

RESULTS

There were 112 patients (10.7%) who were readmitted or died within 30 days after coronary artery bypass graft surgery. The Society of Thoracic Surgeons augmented clinical model resulted in an AUROC of 0.66 (95% confidence interval: 0.61 to 0.71). The biomarker panel with The Society of Thoracic Surgeons augmented clinical model resulted in an AUROC of 0.74 (bootstrapped 95% confidence interval: 0.69 to 0.79, p < 0.0001). External validation of the model showed limited improvement with the addition of a biomarker panel, with an AUROC of 0.51 (95% confidence interval: 0.45 to 0.56).

CONCLUSIONS

Although biomarkers significantly improved prediction of 30-day readmission or mortality in our derivation cohort, the external validation of the biomarker panel was poor. Biomarkers perform poorly, much like other efforts to improve prediction of readmission, suggesting there are many other factors yet to be explored to improve prediction of readmission.

Hematuria as a risk factor for progression of chronic kidney disease and death: findings from the Chronic Renal Insufficiency Cohort (CRIC) Study

BACKGROUND

Hematuria is associated with chronic kidney disease (CKD), but has rarely been examined as a risk factor for CKD progression. We explored whether individuals with hematuria had worse outcomes compared to those without hematuria in the CRIC Study.

METHODS

Participants were a racially and ethnically diverse group of adults (21 to 74 years), with moderate CKD. Presence of hematuria (positive dipstick) from a single urine sample was the primary predictor. Outcomes included a 50% or greater reduction in eGFR from baseline, ESRD, and death, over a median follow-up of 7.3 years, analyzed using Cox Proportional Hazards models. Net reclassification indices (NRI) and C statistics were calculated to evaluate their predictive performance.

RESULTS

Hematuria was observed in 1145 (29%) of a total of 3272 participants at baseline. Individuals with hematuria were more likely to be Hispanic (22% vs. 9.5%, respectively), have diabetes (56% vs. 48%), lower mean eGFR (40.2 vs. 45.3 ml/min/1.73 m2), and higher levels of urinary albumin > 1.0 g/day (36% vs. 10%). In multivariable-adjusted analysis, individuals with hematuria had a greater risk for all outcomes during the first 2 years of follow-up: Halving of eGFR or ESRD (HR Year 1: 1.68, Year 2: 1.36), ESRD (Year 1: 1.71, Year 2: 1.39) and death (Year 1:1.92, Year 2: 1.77), and these associations were attenuated, thereafter. Based on NRIs and C-statistics, no clear improvement in the ability to improve prediction of study outcomes was observed when hematuria was included in multivariable models.

CONCLUSION

In a large adult cohort with CKD, hematuria was associated with a significantly higher risk of CKD progression and death in the first 2 years of follow-up but did not improve risk prediction.

Value of adding the renal pathological score to the kidney failure risk equation in advanced diabetic nephropathy

BACKGROUND

There have been a limited number of biopsy-based studies on diabetic nephropathy, and therefore the clinical importance of renal biopsy in patients with diabetes in late-stage chronic kidney disease (CKD) is still debated. We aimed to clarify the renal prognostic value of pathological information to clinical information in patients with diabetes and advanced CKD.

METHODS

We retrospectively assessed 493 type 2 diabetics with biopsy-proven diabetic nephropathy in four centers in Japan. 296 patients with stage 3-5 CKD at the time of biopsy were identified and assigned two risk prediction scores for end-stage renal disease (ESRD): the Kidney Failure Risk Equation (KFRE, a score composed of clinical parameters) and the Diabetic Nephropathy Score (D-score, a score integrated pathological parameters of the Diabetic Nephropathy Classification by the Renal Pathology Society (RPS DN Classification)). They were randomized 2:1 to development and validation cohort. Hazard Ratios (HR) of incident ESRD were reported with 95% confidence interval (CI) of the KFRE, D-score and KFRE+D-score in Cox regression model. Improvement of risk prediction with the addition of D-score to the KFRE was assessed using c-statistics, continuous net reclassification improvement (NRI), and integrated discrimination improvement (IDI).

RESULTS

During median follow-up of 1.9 years, 194 patients developed ESRD. The cox regression analysis showed that the KFRE,D-score and KFRE+D-score were significant predictors of ESRD both in the development cohort and in the validation cohort. The c-statistics of the D-score was 0.67. The c-statistics of the KFRE was good, but its predictive value was weaker than that in the miscellaneous CKD cohort originally reported (c-statistics, 0.78 vs. 0.90) and was not significantly improved by adding the D-score (0.78 vs. 0.79, p = 0.83). Only continuous NRI was positive after adding the D-score to the KFRE (0.4%; CI: 0.0-0.8%).

CONCLUSIONS

We found that the predict values of the KFRE and the D-score were not as good as reported, and combining the D-score with the KFRE did not significantly improve prediction of the risk of ESRD in advanced diabetic nephropathy. To improve prediction of renal prognosis for advanced diabetic nephropathy may require different approaches with combining clinical and pathological parameters that were not measured in the KFRE and the RPS DN Classification.

Evaluating biomarkers for prognostic enrichment of clinical trials

BACKGROUND/AIMS

A potential use of biomarkers is to assist in prognostic enrichment of clinical trials, where only patients at relatively higher risk for an outcome of interest are eligible for the trial. We investigated methods for evaluating biomarkers for prognostic enrichment.

METHODS

We identified five key considerations when considering a biomarker and a screening threshold for prognostic enrichment: (1) clinical trial sample size, (2) calendar time to enroll the trial, (3) total patient screening costs and the total per-patient trial costs, (4) generalizability of trial results, and (5) ethical evaluation of trial eligibility criteria. Items (1)-(3) are amenable to quantitative analysis. We developed the Biomarker Prognostic Enrichment Tool for evaluating biomarkers for prognostic enrichment at varying levels of screening stringency.

RESULTS

We demonstrate that both modestly prognostic and strongly prognostic biomarkers can improve trial metrics using Biomarker Prognostic Enrichment Tool. Biomarker Prognostic Enrichment Tool is available as a webtool at http://prognosticenrichment.com and as a package for the R statistical computing platform.

CONCLUSION

In some clinical settings, even biomarkers with modest prognostic performance can be useful for prognostic enrichment. In addition to the quantitative analysis provided by Biomarker Prognostic Enrichment Tool, investigators must consider the generalizability of trial results and evaluate the ethics of trial eligibility criteria.

Statistical Methods for Cohort Studies of CKD: Prediction Modeling

Prediction models are often developed in and applied to CKD populations. These models can be used to inform patients and clinicians about the potential risks of disease development or progression. With increasing availability of large datasets from CKD cohorts, there is opportunity to develop better prediction models that will lead to more informed treatment decisions. It is important that prediction modeling be done using appropriate statistical methods to achieve the highest accuracy, while avoiding overfitting and poor calibration. In this paper, we review prediction modeling methods in general from model building to assessing model performance as well as the application to new patient populations. Throughout, the methods are illustrated using data from the Chronic Renal Insufficiency Cohort Study.

Methodological issues in current practice may lead to bias in the development of biomarker combinations for predicting acute kidney injury

Individual biomarkers of renal injury are only modestly predictive of acute kidney injury (AKI). Using multiple biomarkers has the potential to improve predictive capacity. In this systematic review, statistical methods of articles developing biomarker combinations to predict acute kidney injury were assessed. We identified and described three potential sources of bias (resubstitution bias, model selection bias and bias due to center differences) that may compromise the development of biomarker combinations. Fifteen studies reported developing kidney injury biomarker combinations for the prediction of AKI after cardiac surgery (8 articles), in the intensive care unit (4 articles) or other settings (3 articles). All studies were susceptible to at least one source of bias and did not account for or acknowledge the bias. Inadequate reporting often hindered our assessment of the articles. We then evaluated, when possible (7 articles), the performance of published biomarker combinations in the TRIBE-AKI cardiac surgery cohort. Predictive performance was markedly attenuated in six out of seven cases. Thus, deficiencies in analysis and reporting are avoidable and care should be taken to provide accurate estimates of risk prediction model performance. Hence, rigorous design, analysis and reporting of biomarker combination studies are essential to realizing the promise of biomarkers in clinical practice.

microRNAs in the pathophysiology of CKD-MBD: Biomarkers and innovative drugs

microRNAs comprise a novel class of endogenous small non-coding RNAs that have been shown to be implicated in both vascular damage and bone pathophysiology. Chronic kidney disease-mineral bone disorder (CKD-MBD) is characterized by vessel and bone damage secondary to progressive loss of kidney function. In this review, we will describe how several microRNAs have been implicated, in recent years, in cellular and animal models of CKD-MBD, and have been very recently shown to be deregulated in patients with CKD. Particular emphasis has been placed on the endothelial-specific miR-126, a potential biomarker of endothelial dysfunction, and miR-155 and miR-223, which play a role in both vascular smooth muscle cells and osteoclasts, with an impact on the vascular calcification and osteoporosis process. Finally, as these microRNAs may constitute useful targets to prevent or treat complications of CKD-MBD, we will discuss their potential as innovative drugs, describe how they could be delivered in a timely and specific way to vessels and bone by using the most recent techniques such as nanotechnology, viral vectors or CRISPR gene targeting.

Serum microRNAs are altered in various stages of chronic kidney disease: a preliminary study

Background

MicroRNAs (miRNAs) are innovative and informative blood-based biomarkers involved in numerous pathophysiological processes. In this study and based on our previous experimental data, we investigated miR-126, miR-143, miR-145, miR-155 and miR-223 as potential circulating biomarkers for the diagnosis and prognosis of patients with chronic kidney disease (CKD). The primary objective of this study was to assess the levels of miRNA expression at various stages of CKD.

Methods

RNA was extracted from serum, and RT-qPCR was performed for the five miRNAs and cel-miR-39 (internal control).

Results

Serum levels of miR-143, -145 and -223 were elevated in patients with CKD compared with healthy controls. They were further increased in chronic haemodialysis patients, but were below control levels in renal transplant recipients. In contrast, circulating levels of miR-126 and miR-155 levels, which were also elevated in CKD patients, were lower in the haemodialysis group and even lower in the transplant group. Four of the five miRNA species were correlated with estimated glomerular filtration rate, and three were correlated with circulating uraemic toxins.

Conclusions

This exploratory study suggests that specific miRNAs could be biomarkers for complications of CKD, justifying further studies to link changes of miRNA levels with outcomes in CKD patients.

Association of Renal Stress/Damage and Filtration Biomarkers with Subsequent AKI during Hospitalization among Patients Presenting to the Emergency Department

BACKGROUND AND OBJECTIVES

Emergency departments (EDs) have a growing role in hospital admissions, but few studies address AKI biomarkers in the ED.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

Patients admitted to the internal medicine service were enrolled during initial workup in the ED at Robert-Bosch-Hospital, Stuttgart, Germany. Daily serum creatinine (sCr) and urine output (UO) were recorded for AKI classification by Kidney Disease Improving Global Outcomes (KDIGO) criteria. Cystatin C, kidney injury molecule-1, liver-type fatty acid-binding protein, and neutrophil gelatinase-associated lipocalin were measured in blood and urine, and IL-18, insulin-like growth factor-binding protein 7 (IGFBP7), tissue inhibitor of metalloproteinases-2 (TIMP-2) and [TIMP-2]⋅[IGFBP7] were measured in urine collected at enrollment, after 6 hours, and the following morning. Association between these biomarkers and the end point of moderate-severe AKI (KDIGO stage 2-3) occurring within 12 hours of each sample collection was examined using generalized estimating equation logistic regression. Performance for prediction of the AKI end point using two previously validated [TIMP-2]-[IGFBP7] cutoffs was also tested.

RESULTS

Of 400 enrolled patients, 298 had sufficient sCr and UO data for classification by KDIGO AKI criteria: AKI stage 2 developed in 37 patients and AKI stage 3 in nine patients. All urinary biomarkers, sCr, and plasma cystatin C had statistically significant (P<0.05) odds ratios (ORs) for the AKI end point. In a multivariable model of the urine biomarkers and sCr, only [TIMP-2]⋅[IGFBP7] and sCr had statistically significant ORs. Compared with [TIMP-2]⋅[IGFBP7]<0.3 (ng/ml)(2)/1000, values between 0.3 and 2.0 (ng/ml)(2)/1000 indicated 2.5 (95% confidence interval [95% CI], 1.1 to 5.2) times the odds for the AKI end point and values >2.0 (ng/ml)(2)/1000 indicated 11.0 (95% CI, 4.4 to 26.9) times the odds. Addition of [TIMP-2]⋅[IGFBP7] to a clinical model significantly improved area under the receiver-operating characteristic curve from 0.67 (95% CI, 0.61 to 0.78) to 0.77 (95% CI, 0.72 to 0.86) (P<0.001); however, including both markers in the model was not significantly different from including either marker alone.

CONCLUSIONS

Urinary [TIMP-2]⋅[IGFBP7] with pre-established cutoffs provides valuable information about risk for imminent AKI in the ED that is complementary to sCr and clinical risk factors.

Will the future lie in multitude? A critical appraisal of biomarker panel studies on prediction of diabetic kidney disease progression

Diabetic kidney disease is diagnosed and staged by albuminuria and estimated glomerular filtration rate. Although albuminuria has strong predictive power for renal function decline, there is still variability in the rate of renal disease progression across individuals that are not fully captured by the level of albuminuria. Therefore, research focuses on discovering and validating additional biomarkers that improve risk stratification for future renal function decline and end-stage renal disease in patients with diabetes, on top of established biomarkers. Most studies address the value of single biomarkers to predict progressive renal disease and aim to understand the mechanisms that underlie accelerated renal function decline. Since diabetic kidney disease is a disease encompassing several pathophysiological processes, a combination of biomarkers may be more likely to improve risk prediction than a single biomarker. In this review, we provide an overview of studies on the use of multiple biomarkers and biomarker panels, appraise their study design, discuss methodological pitfalls and make recommendations for future biomarker panel studies.

What can we expect from biomarkers for acute kidney injury?

Biomarkers for acute kidney injury have numerous potential roles to play both at the bedside and in the design and conduct of clinical trials. Given the heterogeneous nature of this disease and the difficulty, so far, in developing effective therapies, a strategy that deploys all of our available tools in the treatment and in study of treatments would seem prudent. In this review, we discuss how biomarkers will change the way we do we take care of patients with and do clinical trials in acute kidney injury and why, in fact, biomarkers are necessary.

RiGoR: reporting guidelines to address common sources of bias in risk model development

Reviewing the literature in many fields on proposed risk models reveals problems with the way many risk models are developed. Furthermore, papers reporting new risk models do not always provide sufficient information to allow readers to assess the merits of the model. In this review, we discuss sources of bias that can arise in risk model development. We focus on two biases that can be introduced during data analysis. These two sources of bias are sometimes conflated in the literature and we recommend the terms resubstitution bias and model-selection bias to delineate them. We also propose the RiGoR reporting standard to improve transparency and clarity of published papers proposing new risk models.