Performance of the Kidney Failure Risk Equation by Disease Etiology in Advanced CKD

Development of Predictive Models for Progression from Diabetic Kidney Disease to End-Stage Renal Disease in Type 2 Diabetes Mellitus: A Retrospective Cohort Study

Aim

The aim of this study was to develop a predictive model for the progression of diabetic kidney disease (DKD) to end-stage renal disease (ESRD) and to evaluate the effectiveness of renal pathology and the kidney failure risk equation (KFRE) in this context.

Methods

The study comprised two parts. The first part involved 555 patients with clinically diagnosed DKD, while the second part focused on 85 patients with biopsy-proven DKD. Cox regression analysis and competing risk regression were employed to identify independent predictors. Time-dependent receiver operating characteristic (ROC) was used to evaluate prediction performance, and the area under the curve (AUC) was calculated to assess the model's accuracy.

Results

The Cox regression model developed for the 555 patients clinically diagnosed with DKD identified 5 predictors (body mass index (BMI), estimated glomerular filtration rate (eGFR), 24-hour urinary total protein (UTP), systemic immune-inflammatory index (SII), and controlling nutritional status (CONUT), whereas the Competing risks model included 4 predictors (BMI, eGFR, UTP, CONUT). Among 85 patients with biopsy-proven diabetic DKD, the combined prognostic model integrating KFRE, interstitial fibrosis and tubular atrophy (IFTA), SII and BMI demonstrated enhanced predictive ability at 5 years. The developed models offer improved accuracy over existing methods by incorporating renal pathology and novel inflammatory indices, making them more applicable in clinical settings.

Conclusion

The predictive model proved to be effective in assessing the progression of DKD to ESRD. Additionally, the combined model of KFRE, IFTA, SII, and BMI demonstrates high predictive performance. Future studies should validate these models in larger cohorts and explore their integration into routine clinical practice to enhance personalized risk assessment and management.

Severity of Chronic Kidney Disease as Assessed by the Kidney Failure Risk Equation Is Associated With Incident Acute Ischemic Stroke

AIM

Stroke is a leading cause of death and disability, with substantial healthcare implications. Chronic kidney disease (CKD) is similarly impactful, and emerging evidence links CKD to a higher stroke risk. Despite this, stroke risk assessment in CKD patients remains limited. This study explores the kidney failure risk equation (KFRE) as a predictive tool for ischaemic stroke in CKD patients.

METHODS

This retrospective cohort study analysed CKD patients from a healthcare registry, excluding those with prior stroke, end-stage kidney disease, or kidney transplants. Acute ischemic stroke was the primary outcome, with deaths censored. Cox proportional hazards models evaluated associations between the 2-year and 5-year 4-variable KFRE scores and stroke risk.

RESULTS

A total of 14,794 consecutive patients were included, with a median follow-up of 509 days. The median age of the cohort was 73 years (IQR:14 years), with 6251 females(42.3%), and the majority being of Chinese ethnicity (n = 10 759,73.5%). During the follow-up period, 155 patients (1.05%) experienced an ischemic stroke event, with a median time to stroke of 265 days (IQR:242 days). The 2-year (HR: 1.38 per 10% increase, 95% CI: [1.17-1.63], p < 0.001) and 5-year (HR:1.20 per 10% increase, 95% CI: [1.10-1.31], p < 0.001) 4-variable KFRE scores were significantly associated with an increased risk of ischemic stroke. These associations remained significant after adjusting for patient demographics, comorbidities, advanced CKD stage, glycated haemoglobin and lipid parameters.

CONCLUSION

CKD patients at elevated risk of kidney failure also face a significantly increased risk of acute ischaemic stroke. The KFRE could potentially be integrated into CKD management to assess this risk. Future large prospective cohort studies are necessary to validate these findings.

Recalibrating the kidney failure risk equation for a Mediterranean European population: reducing age and sex inequality

Introduction

Chronic kidney disease (CKD) patients may develop kidney failure (KF), receiving renal replacement therapy (RRT) in some cases. The Kidney Failure Risk Equation (KFRE-4), predicting RRT risk, is widely validated but not in a primary care Mediterranean European population. We aim to recalibrate KFRE-4 accordingly, considering death as a competing risk, to improve performance. Additionally, we recalibrate KFRE-4 for predicting KF, including all patients reaching CKD stage 5, not just those on RRT.

Methods

Retrospective cohort study including individuals aged ≥50 years with confirmed glomerular filtration rate (eGFR) <60 mL/min/1.73m2 and measured albumin-to-creatinine ratio (ACR). Dataset was split into training and test sets. New KFRE-4 models were developed in the training set and performance was evaluated in the test set: Base hazard adapted-KFRE (Basic-RRT), Cox reestimation (Cox- RRT), Fine and Gray RRT reestimation (FG-RRT), and Fine and Gray KF reestimation (FG-KF).

Results

Among 165,371 primary care patients (58.1% female; mean age 78.1 years; mean eGFR 47.3 mL/min/1.73m2, median ACR 10.1 mg/g), original KFRE-4 showed good discrimination but poor calibration, overestimating RRT risk. Basic-RRT showed poorer performance. Cox-RRT and FG-RRT, enhancing the influence of old age and female sex, diminished overprediction. FG-RRT, considering death as a competing risk, resulted the best RRT model. Age and sex had less impact on KF prediction.

Conclusion

A fully tailored recalibration model diminished RRT overprediction. Considering death as a competing event optimizes performance. Recalibrating for KF prediction offers a more inclusive approach in primary care, addressing the needs of women and elderly.

The Management of Chronic Kidney Disease not Requiring Renal Replacement Therapy in General Practice

BACKGROUND

Chronic kidney disease (CKD) is common in the German adult population, with a prevalence of 10%. This guideline, updated on the basis of current scientific evidence, contains recommendations for the management of CKD in general practice.

METHODS

The updated guideline is based on a review and assessment of source guidelines and systematic reviews concerning individual questions. The recommendations were agreed upon in a moderated two-stage nominal group process by the mandate holders of the participating specialist societies, along with patient representatives.

RESULTS

The risk of progression to renal failure requiring renal replacement therapy should be assessed with a risk score. Assessing this risk and determining the indication for treatment with SGLT2 inhibitors both require measurement of the urinary albumin-tocreatinine ratio. Pharmacotherapy is not recommended for asymptomatic hyperuricemia. An initial ultras - onographic examination of the kidneys and urogenital system is now recommended for all patients. The vaccination recommendations that differ for people with CKD have been integrated into the guideline.

CONCLUSION

The risk assessment of CKD and the treatment options have been expanded. The updated guideline can improve primary care for patients with CKD and the selection of patients for interdisciplinary care.

Clinical impact of the Kidney Failure Risk Equation for vascular access planning

BACKGROUND

Risk-based thresholds for arteriovenous (AV) access creation has been proposed to aid vascular access planning. We aimed to assess the clinical impact of implementing the Kidney Failure Risk Equation (KFRE) for vascular access referral.

METHODS

A total of 16 102 nephrology-referred chronic kidney disease (CKD) patients from the Swedish Renal Registry 2008-18 were included. The KFRE was calculated repeatedly, and the timing was identified for when the KFRE risk exceeded several pre-defined thresholds and/or the estimated glomerular filtration rate was <15 mL/min/1.73 m2 (eGFR15). To assess the utility of the KFRE/eGFR thresholds, cumulative incidence curves of kidney replacement therapy (KRT) or death, and decision-curve analyses were computed at 6 and 12 months, and 2 years. The potential impact of using the different thresholds was illustrated by an example from the Swedish access registry.

RESULTS

The 12-month specificity for KRT initiation was highest for KFRE >50% {94.5 [95% confidence interval (CI) 94.3-94.7]} followed by KFRE >40% [90.0 (95% CI 89.7-90.3)], while sensitivity was highest for KFRE >30% [79.3 (95% CI 78.2-80.3)] and eGFR <15 mL/min/1.73 m2 [81.2 (95% CI 80.2-82.2)]. The 2-year positive predictive value was 71.5 (95% CI 70.2-72.8), 61.7 (95% CI 60.4-63.0) and 47.2 (95% CI 46.1-48.3) for KFRE >50%, KFRE >40% and eGFR <15, respectively. Decision curve analyses suggested the largest net benefit for KFRE >40% over 2 years and KFRE >50% over 12 months when it is important to avoid the harm of possibly unnecessary surgery. In Sweden, 54% of nephrology-referred patients started hemodialysis in a central venous catheter (CVC), of whom only 5% had AV access surgery >6 months before initiation. Sixty percent of the CVC patients exceeded KFRE >40% a median of 0.8 years (interquartile range 0.4-1.5) before KRT initiation.

CONCLUSIONS

The utility of using KFRE >40% and KFRE >50% is higher compared with the more traditionally used eGFR threshold <15 mL/min/1.73 m2 for vascular access planning.

Representation of multimorbidity and frailty in the development and validation of kidney failure prognostic prediction models: a systematic review

BACKGROUND

Prognostic models that identify individuals with chronic kidney disease (CKD) at greatest risk of developing kidney failure help clinicians to make decisions and deliver precision medicine. It is recognised that people with CKD usually have multiple long-term health conditions (multimorbidity) and often experience frailty. We undertook a systematic review to evaluate the representation and consideration of multimorbidity and frailty within CKD cohorts used to develop and/or validate prognostic models assessing the risk of kidney failure.

METHODS

We identified studies that described derivation, validation or update of kidney failure prognostic models in MEDLINE, CINAHL Plus and the Cochrane Library-CENTRAL. The primary outcome was representation of multimorbidity or frailty. The secondary outcome was predictive accuracy of identified models in relation to presence of multimorbidity or frailty.

RESULTS

Ninety-seven studies reporting 121 different kidney failure prognostic models were identified. Two studies reported prevalence of multimorbidity and a single study reported prevalence of frailty. The rates of specific comorbidities were reported in a greater proportion of studies: 67.0% reported baseline data on diabetes, 54.6% reported hypertension and 39.2% reported cardiovascular disease. No studies included frailty in model development, and only one study considered multimorbidity as a predictor variable. No studies assessed model performance in populations in relation to multimorbidity. A single study assessed associations between frailty and the risks of kidney failure and death.

CONCLUSIONS

There is a paucity of kidney failure risk prediction models that consider the impact of multimorbidity and/or frailty, resulting in a lack of clear evidence-based practice for multimorbid or frail individuals. These knowledge gaps should be explored to help clinicians know whether these models can be used for CKD patients who experience multimorbidity and/or frailty.

SYSTEMATIC REVIEW REGISTRATION

This review has been registered on PROSPERO (CRD42022347295).

Investigation of end-stage kidney disease risk prediction in an ethnically diverse cohort of people with type 2 diabetes: use of kidney failure risk equation

INTRODUCTION

The four variable kidney failure (KF) risk equation (KFRE) is recommended to estimate KF risk (ie, need for dialysis or kidney transplantation). Earlier referral to clinical kidney services for people with high-risk of kidney failure can ensure appropriate care, education and support are in place pre-emptively. There are limited data on investigating the performance of KFRE in estimating risk of end-stage kidney disease (ESKD) in people with type 2 diabetes mellitus (T2DM) and chronic kidney disease (CKD). The primary ESKD endpoint event was defined as estimated glomerular filtration rate (eGFR) <10 mL/min/1.73 m2 and secondary endpoint eGFR <15 mL/min/1.73 m2.

RESEARCH DESIGN AND METHODS

We studied 7296 people (30% women, 41% African-Caribbean, 45% Caucasian) with T2DM and CKD (eGFR median (range) 48 (15-59) mL/min/1.73 m2) were included at two hospitals in London (median follow-up 10.2 years). Time to ESKD event was the endpoint and Concordance index (C-index) was used to assess KFRE's discrimination of those experiencing ESKD from those who did not. Mean (integrated calibration index (ICI)) and 90th percentile (E90) of the difference between observed and predicted risks were used as calibration metrics.

RESULTS

Of the cohort 746 (10.2%) reached ESKD primary event (135 (1.9%) and 339 (4.5%) over 2 and 5 years, respectively). Similarly, 1130 (15.5%) reached the secondary endpoint (270 (3.7%) and 547 (7.5%) over 2 and 5 years, respectively). The C-index for the primary endpoint was 0.842 (95% CI 0.836 to 0.848) and 0.816 (95% CI 0.812 to 0.820) for 2 and 5 years, respectively. KFRE 'under-predicted' ESKD risk overall and by ethnic group. Likewise, the C-index for secondary endpoint was 0.843 (0.839-0.847) and 0.801 (0.798-0.804) for 2 and 5 years, respectively. KFRE performance analysis performed more optimally with the primary endpoint with 50% enhancement of the calibration metrics than with the secondary endpoint. KFRE recalibration improved ICI by 50% and E90 by more than 78%.

CONCLUSIONS

Although derived for predicting KF, KFRE also demonstrated good discrimination for ESKD outcome. Further studies are needed to identify variables/biomarkers that may improve KFRE's performance/calibration and to aid the development of other predictive models to enable early identification of people at risk of advanced stages of CKD prior to onset of KF.

Enhancing vascular access planning in CKD: validating the 40% KFRE threshold for predicting ESKD in a French retrospective cohort study

Background

Establishing the optimal timing for creating vascular access in patients with chronic kidney disease (CKD) is a critical and challenging aspect of patient management. The Kidney Disease: Improving Global Outcomes guidelines propose using a 40% 2-year threshold based on the Kidney Failure Risk Equation (KFRE) for this purpose. However, the effectiveness of this threshold compared with traditional methods, such as estimated glomerular filtration rate (eGFR), is not well-established.

Methods

In this monocentric retrospective cohort study, we analyzed data from patients referred for vascular mapping before arteriovenous fistula (AVF) creation between April 2013 and June 2023. The study aimed to compare the ≥40% 2-year KFRE threshold with a <15 mL/min/1.73 m² eGFR threshold for predicting end-stage kidney disease (ESKD). We assessed the probability of ESKD, considering death before AVF creation as a competing risk. Discrimination between KFRE and eGFR was evaluated using C-statistics.

Results

The study included 238 patients with a mean age of 65.2 years and a mean eGFR of 13.3 mL/min/1.73 m². Over a median follow-up of 10.7 months, 178 patients developed ESKD, and 21 died before ESKD. Probability of ESKD at 1 year was 77.6% (95% CI 69.9%-85.3%) using a ≥40% 4-variable KFRE threshold versus 65.8% (95% CI 58.3%-73.3%) using a <15 mL/min/1.73 m² eGFR threshold. The C-statistics indicated better predictive ability for the 8-variable KFRE at 6 months [0.82 (95% CI 0.76-0.88)], while both 4- and 8-variable KFRE models were effective for 1-year predictions [0.835 (95% CI 0.78-0.89) and 0.82 (95% CI 0.76-0.875), respectively]. Sensitivity and specificity analyses favored the ≥40% KFRE threshold over the eGFR threshold.

Conclusions

This study suggests that using a ≥40% 2-year KFRE threshold for planning vascular access in CKD patients is promising and potentially superior to the traditional <15 mL/min/1.73 m² eGFR threshold. This approach may offer a balance between minimizing premature AVF creation and the risk of starting dialysis via a central venous catheter.

Use of kidney failure risk equation as a tool to evaluate referrals from primary care to specialist nephrology care

BACKGROUND

With rising costs and burden of chronic kidney disease (CKD), timely referral of patients to a kidney specialist is crucial. Currently, Kidney Health Australia (KHA) uses a 'heat map' based on severity and not future risk of kidney failure, whereas the kidney failure risk equation (KFRE) score predicts future risk of progression.

AIMS

Evaluate whether a KFRE score assists with timing of CKD referrals.

METHODS

Retrospective cohort of 2137 adult patients, referred to tertiary hospital outpatient nephrologist between 2012 and 2020, were analysed. Referrals were analysed for concordance with the KHA referral guidelines and, with the KFRE score, a recommended practice.

RESULTS

Of 2137 patients, 626 (29%) did not have urine albumin-to-creatinine ratio (UACR) measurement at referral. For those who had a UACR, the number who met KFRE preferred referral criteria was 36% less than KHA criteria. If the recommended KFRE score was used, then fewer older patients (≥40 years) needed referral. Positively, many diabetes patients were referred, even if their risk of kidney failure was low, and 29% had a KFRE over 3%. For patients evaluated meeting KFRE criteria, a larger proportion (76%) remained in follow-up, with only 8% being discharged.

CONCLUSIONS

KFRE could reduce referrals and be a useful tool to assist timely referrals. Using KFRE for triage may allow those patients with very low risk of future kidney failure not be referred, remaining longer in primary care, saving health resources and reducing patients' stress and wait times. Using KFRE encourages albuminuria measurement.

Risk Prediction and Management of Chronic Kidney Disease in People Living with Type 2 Diabetes Mellitus

People with type 2 diabetes mellitus have increased risk of chronic kidney disease and atherosclerotic cardiovascular disease. Improved care delivery and implementation of guideline-directed medical therapy have contributed to the declining incidence of atherosclerotic cardiovascular disease in high-income countries. By contrast, the global incidence of chronic kidney disease and associated mortality is either plateaued or increased, leading to escalating direct and indirect medical costs. Given limited resources, better risk stratification approaches to identify people at risk of rapid progression to end-stage kidney disease can reduce therapeutic inertia, facilitate timely interventions and identify the need for early nephrologist referral. Among people with chronic kidney disease G3a and beyond, the kidney failure risk equations (KFRE) have been externally validated and outperformed other risk prediction models. The KFRE can also guide the timing of preparation for kidney replacement therapy with improved healthcare resources planning and may prevent multiple complications and premature mortality among people with chronic kidney disease with and without type 2 diabetes mellitus. The present review summarizes the evidence of KFRE to date and call for future research to validate and evaluate its impact on cardiovascular and mortality outcomes, as well as healthcare resource utilization in multiethnic populations and different healthcare settings.

External validation of the KFRE and Grams prediction models for kidney failure and death in a Spanish cohort of patients with advanced chronic kidney disease

BACKGROUND

The Kidney Failure Risk Equation (KFRE) is a 2- and 5-year kidney failure prediction model that is applied in chronic kidney disease (CKD) G3 + . The Grams model predicts kidney failure and death at 2 and 4 years in CKD G4 + . There are limited external validations of the Grams model, especially for predicting mortality before kidney failure.

METHODS

We performed an external validation of the Grams and Kidney Failure Risk Equation prediction models in incident patients with CKD G4 + at Hospital Universitario Fundación Alcorcón, Spain, between 1/1/2014 and 31/12/2018, ending follow-up on 30/09/2023. Discrimination was performed calculating the area under the receiver-operating characteristic curve. Calibration was assessed using the Hosmer-Lemeshow test and the Brier score.

RESULTS

The study included 339 patients (mean age 72.2 ± 12.7 years and baseline estimated glomerular filtration rate 20.6 ± 5.0 ml/min). Both models showed excellent discrimination. The area under the curve (AUC) for Kidney Failure Risk Equation-2 and Grams-2 were 0.894 (95% CI 0.857-0.931) and 0.897 (95%CI 0.859-0.935), respectively. For Grams-4 the AUC was 0.841 (95%CI 0.798-0.883), and for Kidney Failure Risk Equation-5 it was 0.823 (95% CI 0.779-0.867). For death before kidney failure, the Grams model showed acceptable discrimination (AUC 0.708 (95% CI 0.626-0.790) and 0.744 (95% CI 0.683-0.804) for Grams-2 and Grams-4, respectively). Both models presented excellent calibration for predicting kidney failure. Grams model calibration to estimate mortality before kidney failure was also excellent. In all cases, Hosmer-Lemeshow test resulted in a p-value greater than 0.05, and the Brier score was less than 0.20.

CONCLUSIONS

In a cohort of patients with CKD G4 + from southern Europe, both the Grams and Kidney Failure Risk Equation models are accurate in estimating the risk of kidney failure. Additionally, the Grams model provides a reliable estimate of the risk of mortality before kidney failure.

Multicentre external validation of the prognostic model kidney failure risk equation in patients with CKD stages 3 and 4 in Peru: a retrospective cohort study

OBJECTIVES

To externally validate the four-variable kidney failure risk equation (KFRE) in the Peruvian population for predicting kidney failure at 2 and 5 years.

DESIGN

A retrospective cohort study.

SETTING

17 primary care centres from the Health's Social Security of Peru.

PARTICIPANTS

Patients older than 18 years, diagnosed with chronic kidney disease stage 3a-3b-4 and 3b-4, between January 2013 and December 2017. Patients were followed until they developed kidney failure, died, were lost, or ended the study (31 December 2019), whichever came first.

PRIMARY AND SECONDARY OUTCOME MEASURES

Performance of the KFRE model was assessed based on discrimination and calibration measures considering the competing risk of death.

RESULTS

We included 7519 patients in stages 3a-4 and 2798 patients in stages 3b-4. The estimated cumulative incidence of kidney failure, accounting for competing event of death, at 2 years and 5 years, was 1.52% and 3.37% in stages 3a-4 and 3.15% and 6.86% in stages 3b-4. KFRE discrimination at 2 and 5 years was high, with time-dependent area under the curve and C-index >0.8 for all populations. Regarding calibration in-the-large, the observed to expected ratio and the calibration intercept indicated that KFRE underestimates the overall risk at 2 years and overestimates it at 5 years in all populations.

CONCLUSIONS

The four-variable KFRE models have good discrimination but poor calibration in the Peruvian population. The model underestimates the risk of kidney failure in the short term and overestimates it in the long term. Further research should focus on updating or recalibrating the KFRE model to better predict kidney failure in the Peruvian context before recommending its use in clinical practice.

Real world evaluation of kidney failure risk equations in predicting progression from chronic kidney disease to kidney failure in an Australian cohort

BACKGROUND

Chronic kidney disease progression to kidney failure is diverse, and progression may be different according to genetic aspects and settings of care. We aimed to describe kidney failure risk equation prognostic accuracy in an Australian population.

METHODS

A retrospective cohort study was undertaken in a public hospital community-based chronic kidney disease service in Brisbane, Australia, which included a cohort of 406 adult patients with chronic kidney disease Stages 3-4 followed up over 5 years (1/1/13-1/1/18). Risk of progression to kidney failure at baseline using Kidney Failure Risk Equation models with three (eGFR/age/sex), four (add urinary-ACR) and eight variables (add serum-albumin/phosphate/bicarbonate/calcium) at 5 and 2 years were compared to actual patient outcomes.

RESULTS

Of 406 patients followed up over 5 years, 71 (17.5%) developed kidney failure, while 112 died before reaching kidney failure. The overall mean difference between observed and predicted risk was 0.51% (p = 0.659), 0.93% (p = 0.602), and - 0.03% (p = 0.967) for the three-, four- and eight-variable models, respectively. There was small improvement in the receiver operating characteristic-area under the curve from three-variable to four-variable models: 0.888 (95%CI = 0.819-0.957) versus 0.916 (95%CI = 0.847-0.985). The eight-variable model showed marginal receiver operating characteristic-area under the curve improvement: 0.916 (95%CI = 0.847-0.985) versus 0.922 (95%CI = 0.853-0.991). The results were similar in predicting 2 year risk of kidney failure.

CONCLUSIONS

The kidney failure risk equation accurately predicted progression to kidney failure in an Australian chronic kidney disease population. Younger age, male sex, lower estimated glomerular filtration rate, higher albuminuria, diabetes mellitus, tobacco smoking and non-Caucasian ethnicity were associated with increased risk of kidney failure. Cause-specific cumulative incidence function for progression to kidney failure or death, stratified by chronic kidney disease stage, demonstrated differences within different chronic kidney disease stages, highlighting the interaction between comorbidity and outcome.

Preparing for kidney replacement therapy in pediatric advanced CKD: a review of literature and defining a multi-disciplinary clinical approach to patient-caregiver education

Pediatric patients with progressive chronic kidney disease (CKD) approaching kidney replacement therapy (KRT) make up a small population but carry significant morbidity and mortality. Patients and caregivers require comprehensive kidney failure education to ensure a smooth start to KRT. Choice of KRT modality can be influenced by medical comorbidities, patient/caregiver comprehension, and comfort with a particular modality, social and economic factors, and/or implicit bias of the health care team. As KRT modality can influence morbidity, mortality, and quality of life, we created a pediatric advanced CKD clinic to provide comprehensive KRT education and to promote informed decision-making for our advanced CKD patients and their caregivers.

Implementation of the Kidney Failure Risk Equation in a United States Nephrology Clinic

Introduction

The kidney failure risk equation (KFRE) estimates a person's risk of kidney failure and has great potential utility in clinical care.

Methods

We used mixed methods to explore implementation of the KFRE in nephrology clinics.

Results

KFRE scores were integrated into the electronic health record at Johns Hopkins Medicine and were displayed to nephrology providers. Documentation of KFRE scores increased over time, reaching 25% of eligible outpatient nephrology clinic notes at month 11. Three providers documented KFRE scores in >75% of notes, whereas 25 documented scores in <10% of notes. Surveys and focus groups of nephrology providers were conducted to probe provider views on the KFRE. Survey respondents (n = 25) reported variability in use of KFRE for decisions such as maintaining nephrology care, referring for transplant evaluation, or providing dialysis modality education. Provider perspectives on the use of KFRE, assessed in 2 focus groups of 4 providers each, included 3 common themes as follows: (i) KFRE scores may be most impactful in the care of specific subsets of people with chronic kidney disease (CKD); (ii) there is uncertainty about KFRE risk-based thresholds to guide clinical care; and (iii) education of patients, nephrology providers, and non-nephrology providers on appropriate interpretations of KFRE scores may help maximize their utility.

Conclusion

Implementation of the KFRE was limited by non-uniform provider adoption of its use, and limited knowledge about utilization of the KFRE in clinical decisions.

Precision Medicine in Diabetic Kidney Disease: A Narrative Review Framed by Lived Experience

Purpose of review

Diabetic kidney disease (DKD) is a leading cause of chronic kidney disease (CKD) for which many treatments exist that have been shown to prevent CKD progression and kidney failure. However, DKD is a complex and heterogeneous etiology of CKD with a spectrum of phenotypes and disease trajectories. In this narrative review, we discuss precision medicine approaches to DKD, including genomics, metabolomics, proteomics, and their potential role in the management of diabetes mellitus and DKD. A patient and caregivers of patients with lived experience with CKD were involved in this review.

Sources of information

Original research articles were identified from MEDLINE and Google Scholar using the search terms "diabetes," "diabetic kidney disease," "diabetic nephropathy," "chronic kidney disease," "kidney failure," "dialysis," "nephrology," "genomics," "metabolomics," and "proteomics."

Methods

A focused review and critical appraisal of existing literature regarding the precision medicine approaches to the diagnosis, prognosis, and treatment of diabetes and DKD framed by a patient partner's/caregiver's lived experience.

Key findings

Distinguishing diabetic nephropathy from CKD due to other types of DKD and non-DKD is challenging and typically requires a kidney biopsy for a diagnosis. Biomarkers have been identified to assist with the prediction of the onset and progression of DKD, but they have yet to be incorporated and evaluated relative to clinical standard of care CKD and kidney failure risk prediction tools. Genomics has identified multiple causal genetic variants for neonatal diabetes mellitus and monogenic diabetes of the young that can be used for diagnostic purposes and to specify antiglycemic therapy. Genome-wide-associated studies have identified genes implicated in DKD pathophysiology in the setting of type 1 and 2 diabetes but their translational benefits are lagging beyond polygenetic risk scores. Metabolomics and proteomics have been shown to improve diagnostic accuracy in DKD, have been used to identify novel pathways involved in DKD pathogenesis, and can be used to improve the prediction of CKD progression and kidney failure as well as predict response to DKD therapy.

Limitations

There are a limited number of large, high-quality prospective observational studies and no randomized controlled trials that support the use of precision medicine based approaches to improve clinical outcomes in adults with or at risk of diabetes and DKD. It is unclear which patients may benefit from the clinical use of genomics, metabolomics and proteomics along the spectrum of DKD trajectory.

Implications

Additional research is needed to evaluate the role of the use of precision medicine for DKD management, including diagnosis, differentiation of diabetic nephropathy from other etiologies of DKD and CKD, short-term and long-term risk prognostication kidney outcomes, and the prediction of response to and safety of disease-modifying therapies.

Early diagnosis of chronic kidney disease in patients with diabetes in France: multidisciplinary expert opinion, prevention value and practical recommendations

Diabetes is the leading cause of end-stage kidney disease (ESKD), accounting for approximately 50% of patients starting dialysis. However, the management of these patients at the stage of chronic kidney disease (CKD) remains poor, with fragmented care pathways among healthcare professionals (HCPs). Diagnosis of CKD and most of its complications is based on laboratory evidence. This article provides an overview of critical laboratory evidence of CKD and their limitations, such as estimated glomerular filtration rate (eGFR), urine albumin-to-creatinine ratio (UACR), Kidney Failure Risk Equation (KFRE), and serum potassium. eGFR is estimated using the CKD-EPI 2009 formula, more relevant in Europe, from the calibrated dosage of plasma creatinine. The estimation formula and the diagnostic thresholds have been the subject of recent controversies. Recent guidelines emphasized the combined equation using both creatinine and cystatin for improved estimation of GFR. UACR on a spot urine sample is a simple method that replaces the collection of 24-hour urine. Albuminuria is the preferred test because of increased sensitivity but proteinuria may be appropriate in some settings as an alternative or in addition to albuminuria testing. KFRE is a new tool to estimate the risk of progression to ESKD. This score is now well validated and may improve the nephrology referral strategy. Plasma or serum potassium is an important parameter to monitor in patients with CKD, especially those on renin-angiotensin-aldosterone system (RAAS) inhibitors or diuretics. Pre-analytical conditions are essential to exclude factitious hyperkalemia. The current concept is to correct hyperkalemia using pharmacological approaches, resins or diuretics to be able to maintain RAAS blockers at the recommended dose and discontinue them at last resort. This paper also suggests expert recommendations to optimize the healthcare pathway and the roles and interactions of the HCPs involved in managing CKD in patients with diabetes.

CKD Progression Prediction in a Diverse US Population: A Machine-Learning Model

Rationale & Objective

Chronic kidney disease (CKD) is a major cause of morbidity and mortality. To date, there are no widely used machine-learning models that can predict progressive CKD across the entire disease spectrum, including the earliest stages. The objective of this study was to use readily available demographic and laboratory data from Sonic Healthcare USA laboratories to train and test the performance of machine learning-based predictive risk models for CKD progression.

Study Design

Retrospective observational study.

Setting & Participants

The study population was composed of deidentified laboratory information services data procured from a large US outpatient laboratory network. The retrospective data set included 110,264 adult patients over a 5-year period with initial estimated glomerular filtration rate (eGFR) values between 15-89 mL/min/1.73 m2.

Predictors

Patient demographic and laboratory characteristics.

Outcomes

Accelerated (ie, >30%) eGFR decline associated with CKD progression within 5 years.

Analytical Approach

Machine-learning models were developed using random forest survival methods, with laboratory-based risk factors analyzed as potential predictors of significant eGFR decline.

Results

The 7-variable risk classifier model accurately predicted an eGFR decline of >30% within 5 years and achieved an area under the curve receiver-operator characteristic of 0.85. The most important predictor of progressive decline in kidney function was the eGFR slope. Other key contributors to the model included initial eGFR, urine albumin-creatinine ratio, serum albumin (initial and slope), age, and sex.

Limitations

The cohort study did not evaluate the role of clinical variables (eg, blood pressure) on the performance of the model.

Conclusions

Our progressive CKD classifier accurately predicts significant eGFR decline in patients with early, mid, and advanced disease using readily obtainable laboratory data. Although prospective studies are warranted, our results support the clinical utility of the model to improve timely recognition and optimal management for patients at risk for CKD progression.

Plain-Language Summary

Defined by a significant decrease in estimated glomerular filtration rate (eGFR), chronic kidney disease (CKD) progression is strongly associated with kidney failure. However, to date, there are no broadly used resources that can predict this clinically significant event. Using machine-learning techniques on a diverse US population, this cohort study aimed to address this deficiency and found that a 5-year risk prediction model for CKD progression was accurate. The most important predictor of progressive decline in kidney function was the eGFR slope, followed by the urine albumin-creatinine ratio and serum albumin slope. Although further study is warranted, the results showed that a machine-learning model using readily obtainable laboratory information accurately predicts CKD progression, which may inform clinical diagnosis and management for this at-risk population.

AACC/NKF Guidance Document on Improving Equity in Chronic Kidney Disease Care

BACKGROUND

Kidney disease (KD) is an important health equity issue with Black, Hispanic, and socioeconomically disadvantaged individuals experiencing a disproportionate disease burden. Prior to 2021, the commonly used estimated glomerular filtration rate (eGFR) equations incorporated coefficients for Black race that conferred higher GFR estimates for Black individuals compared to non-Black individuals of the same sex, age, and blood creatinine concentration. With a recognition that race does not delineate distinct biological categories, a joint task force of the National Kidney Foundation and the American Society of Nephrology recommended the adoption of the CKD-EPI 2021 race-agnostic equations.

CONTENT

This document provides guidance on implementation of the CKD-EPI 2021 equations. It describes recommendations for KD biomarker testing, and opportunities for collaboration between clinical laboratories and providers to improve KD detection in high-risk populations. Further, the document provides guidance on the use of cystatin C, and eGFR reporting and interpretation in gender-diverse populations.

SUMMARY

Implementation of the CKD-EPI 2021 eGFR equations represents progress toward health equity in the management of KD. Ongoing efforts by multidisciplinary teams, including clinical laboratorians, should focus on improved disease detection in clinically and socially high-risk populations. Routine use of cystatin C is recommended to improve the accuracy of eGFR, particularly in patients whose blood creatinine concentrations are confounded by processes other than glomerular filtration. When managing gender-diverse individuals, eGFR should be calculated and reported with both male and female coefficients. Gender-diverse individuals can benefit from a more holistic management approach, particularly at important clinical decision points.

Combining robust urine biomarkers to assess chronic kidney disease progression

BACKGROUND

Urinary biomarkers may improve the prediction of chronic kidney disease (CKD) progression. Yet, data reporting the applicability of most commercial biomarker assays to the detection of their target analyte in urine together with an evaluation of their predictive performance are scarce.

METHODS

30 commercial assays (ELISA) were tested for their ability to quantify the target analyte in urine using strict (FDA-approved) validation criteria. In an exploratory analysis, LASSO (Least Absolute Shrinkage and Selection Operator) logistic regression analysis was used to identify potentially complementary biomarkers predicting fast CKD progression, determined as the ⁵¹CrEDTA clearance-based measured glomerular filtration rate (mGFR) decline (>10% per year) in a subsample of 229 CKD patients (mean age, 61 years; 66% men; baseline mGFR, 38 mL/min) from the NephroTest prospective cohort.

FINDINGS

Among the 30 assays, directed against 24 candidate biomarkers, encompassing different pathophysiological mechanisms of CKD progression, 16 assays fulfilled the FDA-approved criteria. LASSO logistic regressions identified a combination of five biomarkers including CCL2, EGF, KIM1, NGAL, and TGF-α that improved the prediction of fast mGFR decline compared to the kidney failure risk equation variables alone: age, gender, mGFR, and albuminuria. Mean area under the curves (AUC) estimated from 100 re-samples was higher in the model with than without these biomarkers, 0.722 (95% confidence interval 0.652-0.795) vs. 0.682 (0.614-0.748), respectively. Fully-adjusted odds-ratios (95% confidence interval) for fast progression were 1.87 (1.22, 2.98), 1.86 (1.23, 2.89), 0.43 (0.25, 0.70), 1.10 (0.71, 1.83), 0.55 (0.33, 0.89), and 2.99 (1.89, 5.01) for albumin, CCL2, EGF, KIM1, NGAL, and TGF-α, respectively.

INTERPRETATION

This study provides a rigorous validation of multiple assays for relevant urinary biomarkers of CKD progression which combination may improve the prediction of CKD progression.

FUNDING

This work was supported by Institut National de la Santé et de la Recherche Médicale, Université de Paris, Assistance Publique Hôpitaux de Paris, Agence Nationale de la Recherche, MSDAVENIR, Pharma Research and Early Development Roche Laboratories (Basel, Switzerland), and Institut Roche de Recherche et Médecine Translationnelle (Paris, France).

The kidney failure risk equation predicts kidney failure: Validation in an Australian cohort

AIMS

Predicting progression to kidney failure for patients with chronic kidney disease is essential for patient and clinicians' management decisions, patient prognosis, and service planning. The Tangri et al Kidney Failure Risk Equation (KFRE) was developed to predict the outcome of kidney failure. The KFRE has not been independently validated in an Australian Cohort.

METHODS

Using data linkage of the Tasmanian Chronic Kidney Disease study (CKD.TASlink) and the Australia and New Zealand Dialysis and Transplant Registry (ANZDATA), we externally validated the KFRE. We validated the 4, 6, and 8-variable KFRE at both 2 and 5 years. We assessed model fit (goodness of fit), discrimination (Harell's C statistic), and calibration (observed vs predicted survival).

RESULTS

There were 18 170 in the cohort with 12 861 participants with 2 years and 8182 with 5 years outcomes. Of these 2607 people died and 285 progressed to kidney replacement therapy. The KFRE has excellent discrimination with C statistics of 0.96-0.98 at 2 years and 0.95-0.96 at 5 years. The calibration was adequate with well-performing Brier scores (0.004-0.01 at 2 years, 0.01-0.03 at 5 years) however the calibration curves, whilst adequate, indicate that predicted outcomes are systematically worse than observed.

CONCLUSION

This external validation study demonstrates the KFRE performs well in an Australian population and can be used by clinicians and service planners for individualised risk prediction.

Bridging Gaps in Diabetic Nephropathy Care: A Narrative Review Guided by the Lived Experiences of Patient Partners

Purpose of review

Diabetes affects almost a 10th of the Canadian population, and diabetic nephropathy is one of its main complications. It remains a leading cause of kidney failure despite the availability of effective treatments.

Sources of information

The sources of information are iterative discussions between health care professionals and patient partners and literature collected through the search of multiple databases.

Methods

Major pitfalls related to optimal diabetic nephropathy care were identified through discussions between patient partners and clinician researchers. We identified underlying factors that were common between pitfalls. We then conducted a narrative review of strategies to overcome them, with a focus on Canadian initiatives.

Key findings

We identified 5 pitfalls along the diabetic nephropathy trajectory, including a delay in diabetes diagnosis, suboptimal glycemic control, delay in the detection of kidney involvement, suboptimal kidney protection, and deficient management of advanced chronic kidney disease. Several innovative care models and approaches have been proposed to address these pitfalls; however, they are not consistently applied. To improve diabetic nephropathy care in Canada, we recommend focusing initiatives on improving awareness of diabetic nephropathy, improving access to timely evidence-based care, fostering inclusive patient-centered care environment, and generating new evidence that supports complex disease management. It is imperative that patients and their families are included at the center of these initiatives.

Limitations

This review was limited to research published in peer-reviewed journals. We did not perform a systematic review of the literature; we included articles that were relevant to the major pitfalls identified by our patient partners. Study quality was also not formally assessed. The combination of these factors limits the scope of our conclusions.

A prediction model to assess the risk of egfr loss in patients with type 2 diabetes and preserved kidney function: The amd annals initiative

OBJECTIVE

To develop and validate a model for predicting 5-year eGFR-loss in type 2 diabetes mellitus (T2DM) patients with preserved renal function at baseline.

RESEARCH DESIGN AND METHODS

A cohort of 504.532 T2DM outpatients participating to the Medical Associations of Diabetologists (AMD) Annals Initiative was splitted into the Learning and Validation cohorts, in which the predictive model was respectively developed and validated. A multivariate Cox proportional hazard regression model including all baseline characteristics was performed to identify predictors of eGFR-loss. A weight derived from regression coefficients was assigned to each variable and the overall sum of weights determined the 0 to 8-risk score.

RESULTS

A set of demographic, clinical and laboratory parameters entered the final model. The eGFR-loss score showed a good performance in the Validation cohort. Increasing score values progressively identified a higher risk of GFR loss: a score ≥ 8 was associated with a HR of 13.48 (12.96-14.01) in the Learning and a HR of 13.45 (12.93-13.99) in the Validation cohort. The 5 years-probability of developing the study outcome was 55.9% higher in subjects with a score ≥ 8.

CONCLUSIONS

In the large AMD Annals Initiative cohort, we developed and validated an eGFR-loss prediction model to identify T2DM patients at risk of developing clinically meaningful renal complications within a 5-years time frame.

Death and Dialysis Following Discharge From Chronic Kidney Disease Clinic: A Retrospective Cohort Study

Background:

Multidisciplinary care is recommended for patients with advanced chronic kidney disease (CKD). A formalized, risk-based approach to CKD management is being adopted in some jurisdictions. In Ontario, Canada, the eligibility criteria for multidisciplinary CKD care funding were revised between 2016 and 2018 to a 2 year risk of kidney replacement therapy (KRT) greater than 10% calculated by the 4-variable Kidney Failure Risk Equation (KFRE). Implementation of the risk-based approach has led to the discharge of prevalent CKD patients.

Objective:

The primary objective of this study was to determine the frequency of occurrence of death and KRT initiation in patients discharged from CKD clinic.

Design:

Retrospective cohort study

Setting:

Single center multidisciplinary CKD clinic in Ontario, Canada

Patients:

Four hundred and twenty five patients seen at least once in 2013 at the multidisciplinary CKD clinic

Measurements:

Outcomes included discharge status, death, re-referral and KRT initiation. Reasons for discharge were recorded.

Methods:

Outcomes were extracted from available electronic medical records and the provincial death registry between the patient’s initial clinic visit in 2013 and January 1, 2020. KFRE-2 scores were calculated using the 4-variable KFRE equation. The hazard rates of death and KRT after discharge due to stable eGFR/low KFRE were compared to patients who remained in the clinic.

Results:

Of the 425 CKD patients, 69 (16%) and 19 (4%) were discharged to primary care and general nephrology, respectively. Of those discharged, 7 (8%) were re-referred to nephrology or CKD clinic, while only 2 (2%) discharged patients required subsequent KRT. The hazard of mortality was reduced after discharge from the clinic due to stable eGFR/low KFRE (adjusted HR = 0.45 [95% CI, 0.25-0.78, P = .005]).

Limitations:

Single center, observational retrospective study design and unknown kidney function over time post discharge for most patients

Conclusions:

Discharge of low risk patients from multidisciplinary CKD clinic appears feasible and safe, with fewer than 1 in 40 discharged patients subsequently initiated on KRT over the following 7 years.

Contemporary risk prediction models in chronic kidney disease: when less is more

PURPOSE OF REVIEW

Clinicians have an ever-increasing number of prediction tools at their disposal for estimating the risk of kidney failure in their patients. This review aims to summarize contemporary evidence for chronic kidney disease (CKD) risk prediction models across the spectrum of kidney function, and explore nuances in the interpretation of risk estimates.

RECENT FINDINGS

A European study using predominantly laboratory data has extended kidney failure prediction to patients with more preserved estimated glomerular filtration rate. For older patients with advanced CKD, prediction tools that censor for death (such as the Kidney Failure Risk Equation) overestimate the risk of kidney failure, especially over time horizons longer than 2 years. This problem can be addressed by accounting for the competing risk of death, as shown in well designed validation studies. The clinical utility of kidney failure risk prediction tools is being increasingly tested at a population level to inform policy and referral guidelines.

SUMMARY

There is welcome trend to validate existing prediction tools in diverse clinical settings and identify their role in clinical practice. Clinicians should be cognizant of overestimating kidney failure risk in older patients with advanced CKD due to the competing risk of death. For moderate CKD and for short-term predictions, the Kidney Failure Risk Equation remains the most widely validated prediction tool.

Independent External Validation and Comparison of Death and Kidney Replacement Therapy Prediction Models in Advanced CKD

Rationale & Objective

Study Design

Setting & Participants

Outcomes & Analytical Approach

Results

Limitations

Conclusions

The Effect of Age on Performance of the Kidney Failure Risk Equation in Advanced CKD

Introduction

The Kidney Failure Risk Equation (KFRE) is a clinical tool widely used to predict progression from chronic kidney disease (CKD) to kidney failure. This study aimed to evaluate the effect of age on KFRE performance in advanced CKD.

Methods

We conducted a retrospective cohort study among 1701 consecutive patients referred to an advanced CKD clinic in Ottawa, Canada, between 2010 and 2018. Patients were categorized by age as follows: <60, 60 to 69, 70 to 79, and ≥80 years. Calibration plots compared the predicted (through the KFRE) and observed incidence of kidney failure. Concordance statistic (C-statistic) evaluated discrimination. Cumulative incidence of kidney failure was compared between models that accounted for the competing risk of death and those that did not.

Results

We found that the KFRE overestimated the risk of kidney failure among the oldest subset of patients (≥80 years) with absolute and relative differences of 7.6% and 22.8%, respectively, over 2 years (P = 0.047), and 24.7% and 40.4%, respectively, over 5 years (P < 0.001). The degree of overestimation in the elderly was most pronounced among those with the highest predicted risks for kidney failure. KFRE discrimination was acceptable (C-statistic 0.70–0.79) across all age categories. The cumulative incidence of kidney failure was overestimated in models that did not account for the competing risk of death, and this overestimation was more pronounced with older age.

Conclusion

The KFRE overestimates kidney failure risk among elderly patients with advanced CKD. This overestimation relates to the increasing competing risk of death with older age, particularly over longer time horizons.

A validation study of the kidney failure risk equation in advanced chronic kidney disease according to disease aetiology with evaluation of discrimination, calibration and clinical utility

BACKGROUND

The Kidney Failure Risk Equation (KFRE) predicts the 2- and 5-year risk of end-stage renal disease (ESRD) in patients with chronic kidney disease (CKD) stages 3a-5. Its predictive performance in advanced CKD and in specific disease aetiologies requires further exploration. This study validates the 4- and 8-variable KFREs in an advanced CKD population in the United Kingdom by evaluating discrimination, calibration and clinical utility.

METHODS

Patients enrolled in the Salford Kidney Study who were referred to the Advanced Kidney Care Service (AKCS) clinic at Salford Royal NHS Foundation Trust between 2011 and 2018 were included. The 4- and 8-variable KFREs were calculated on the first AKCS visit and the observed events of ESRD (dialysis or pre-emptive transplantation) within 2- and 5-years were the primary outcome. The area under the receiver operator characteristic curve (AUC) and calibration plots were used to evaluate discrimination and calibration respectively in the whole cohort and in specific disease aetiologies: diabetic nephropathy, hypertensive nephropathy, glomerulonephritis, autosomal dominant polycystic kidney disease (ADPKD) and other diseases. Clinical utility was assessed with decision curve analyses, comparing the net benefit of using the KFREs against estimated glomerular filtration rate (eGFR) cut-offs of < 20 ml/min/1.73m2 and < 15 ml/min/1.73m2 to guide further treatment.

RESULTS

A total of 743 patients comprised the 2-year analysis and 613 patients were in the 5-year analysis. Discrimination was good in the whole cohort: the 4-variable KFRE had an AUC of 0.796 (95% confidence interval [CI] 0.762-0.831) for predicting ESRD at 2-years and 0.773 (95% CI 0.736-0.810) at 5-years, and there was good-to-excellent discrimination across disease aetiologies. Calibration plots revealed underestimation of risk at 2-years and overestimation of risk at 5-years, especially in high-risk patients. There was, however, underestimation of risk in patients with ADPKD for all KFRE calculations. The predictive accuracy was similar between the 4- and 8-variable KFREs. Finally, compared to eGFR-based thresholds, the KFRE was the optimal tool to guide further care based on decision curve analyses.

CONCLUSIONS

The 4- and 8-variable KFREs demonstrate adequate discrimination and calibration for predicting ESRD in an advanced CKD population and, importantly, can provide better clinical utility than using an eGFR-based strategy to inform decision-making.

Is It Time for Machine Learning Algorithms to Predict the Risk of Kidney Failure in Patients with Chronic Kidney Disease?

Chronic kidney disease (CKD) is a common clinical problem affecting more than 800 million people with different kidney diseases [...].

Albuminuria, proteinuria, and dipsticks: novel relationships and utility in risk prediction

PURPOSE OF REVIEW

Albuminuria is associated with progression of kidney disease and is the accepted gold standard for screening, staging, and prognostication of chronic kidney disease. This review focuses on current literature that has explored applications of albuminuria as a surrogate outcome, variable used in kidney failure risk prediction for novel populations, and variable that may be predicted by other proteinuria measures.

RECENT FINDINGS

Change in albuminuria shows promise as a surrogate outcome for kidney failure, which may have major implications for trial design and conduct. The kidney failure risk equation (KFRE) has been validated extensively to date and has now been applied to pediatric patients with kidney disease, advanced age, different causes of kidney disease, various countries, and those with prior kidney transplants. As albumin-to-creatinine ratios (ACRs) are not always available to clinicians and researchers, two recent studies have independently developed equations to estimate ACR from other proteinuria measures.

SUMMARY

The utility of albuminuria and the KFRE continues to grow in novel populations. With the ability to convert more widely available (and inexpensive) proteinuria measures to ACR estimates, the prospect of incorporating kidney failure risk prediction into routine care within economically challenged healthcare jurisdictions may finally be realized.