Validation of the kidney failure risk equation for end-stage kidney disease in Southeast Asia

Evaluating a multicomponent intervention for managing kidney outcomes among patients with moderate or advanced chronic kidney disease (CKD): protocol for the Strategies for Kidney Outcomes Prevention and Evaluation (SKOPE) randomized controlled trial

BACKGROUND

Chronic kidney disease (CKD) poses a global health challenge with high morbidity and mortality rates. Early detection and prompt intervention are critical in preventing progression to end-stage kidney disease (ESKD) and cardiovascular complications. Effective CKD management requires comprehensive care packages that integrate both pharmacological and non-pharmacological interventions within collaborative, team-based models, aiming to enhance patient outcomes and overall quality of life. The goal of the Strategies for Kidney Outcomes Prevention and Evaluation (SKOPE) study is to establish effective multicomponent intervention (MCI) strategies for evaluating and preventing kidney outcomes in patients with moderate to advanced CKD within primary care settings in Singapore.

METHODS

This study is a 3-year randomized controlled trial among 896 participants aged between 40 and 80 years with moderate or advanced CKD in five government-subsidized polyclinics in Singapore. The components of the MCI are (1) nurses/service coordinators trained as health coaches for motivational conversation and CKD-specific lifestyle counseling on diet and exercise, using a hybrid follow-up approach of in-person, telephone, and secure video meetings; (2) training physicians in algorithm-based standardized management of CKD; (3) subsidy on SGLT2i medications for CKD; and (4) regular CKD case review meetings. The primary outcome is the estimated glomerular filtration rate (eGFR) total slope from randomization to final follow-up at 36 months.

DISCUSSION

If shown to be effective, cost-effective, and acceptable, SKOPE should be considered for scaling country-wide and in similar regional healthcare systems.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05295368. Registered on March 25, 2022.

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).

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.

Predictive models in chronic kidney disease: essential tools in clinical practice

PURPOSE OF REVIEW

The integration of risk prediction in managing chronic kidney disease (CKD) is universally considered a key point of routine clinical practice to guide time-sensitive choices, such as dialysis access planning or counseling on kidney transplant options. Several prognostic models have been developed and validated to provide individualized evaluation of kidney failure risk in CKD patients. This review aims to analyze the current evidence on existing predictive models and evaluate the different advantages and disadvantages of these tools.

RECENT FINDINGS

Since Tangri et al. introduced the Kidney Failure Risk Equation in 2011, the nephrological scientific community focused its interest in enhancing available algorithms and finding new prognostic equations. Although current models can predict kidney failure with high discrimination, different questions remain unsolved. Thus, this field is open to new possibilities and discoveries.

SUMMARY

Accurately informing patients of their prognoses can result in tailored therapy with important clinical and psychological implications. Over the last 5 years, the number of disease-modifying therapeutic options has considerably increased, providing possibilities to not only prevent the kidney failure onset in patients with advanced CKD but also delay progression from early stages in at-risk individuals.

Utilization of the corticomedullary difference in magnetic resonance imaging-derived apparent diffusion coefficient for noninvasive assessment of chronic kidney disease in type 2 diabetes

BACKGROUNDS

Accumulating data demonstrated that the cortico-medullary difference in apparent diffusion coefficient (ΔADC) of diffusion-weighted magnetic resonance imaging (DWI) was a better correlation with kidney fibrosis, tubular atrophy progression, and a predictor of kidney function evolution in chronic kidney disease (CKD).

OBJECTIVES

We aimed to assess the value of ΔADC in evaluating disease severity, differential diagnosis, and the prognostic risk stratification for patients with type 2 diabetes (T2D) and CKD.

METHODS

Total 119 patients with T2D and CKD who underwent renal MRI were prospectively enrolled. Of them, 89 patients had performed kidney biopsy for pathological examination, including 38 patients with biopsy-proven diabetic kidney disease (DKD) and 51 patients with biopsy-proven non-diabetic kidney disease (NDKD) and Mix (DKD + NDKD). Clinicopathological characteristics were compared according to different ΔADC levels. Moreover, univariate and multivariate-linear regression analyses were performed to explore whether ΔADC was independently associated with estimated glomerular filtration rate (eGFR) and urinary albumin creatinine ratio (UACR). The diagnostic performance of ΔADC for discriminating DKD from NDKD + Mix was evaluated by receiver operating characteristic (ROC) analysis. In addition, an individual's 2- or 5-year risk probability of progressing to end-stage kidney disease (ESKD) was calculated by the kidney failure risk equation (KFRE). The effect of ΔADC on prognostic risk stratification was assessed. Additionally, net reclassification improvement (NRI) was used to evaluate the model performance.

RESULTS

All enrolled patients had a median ΔADC level of 86 (IQR 28, 155) × 10-6 mm2/s. ΔADC significantly decreased across the increasing staging of CKD (P < 0.001). Moreover, those with pathological-confirmed DKD has a significantly lower level of ΔADC than those with NDKD and Mix (P < 0.001). It showed that ΔADC was independently associated with eGFR (β = 1.058, 95% CI = [1.002,1.118], P = 0.042) and UACR (β = -3.862, 95% CI = [-7.360, -0.365], P = 0.031) at multivariate linear regression analyses. Besides, ΔADC achieved an AUC of 0.707 (71% sensitivity and 75% specificity) and AUC of 0.823 (94% sensitivity and 67% specificity) for discriminating DKD from NDKD + Mix and higher ESKD risk categories (≥50% at 5 years; ≥10% at 2 years) from lower risk categories (<50% at 5 years; <10% at 2 years). Accordingly, the optimal cutoff value of ΔADC for higher ESKD risk categories was 66 × 10-6 mm2/s, and the group with the low-cutoff level of ΔADC group was associated with 1.232 -fold (95% CI 1.086, 1.398) likelihood of higher ESKD risk categories as compared to the high-cutoff level of ΔADC group in the fully-adjusted model. Reclassification analyses confirmed that the final adjusted model improved NRI.

CONCLUSIONS

ΔADC was strongly associated with eGFR and UACR in patients with T2D and CKD. More importantly, baseline ΔADC was predictive of higher ESKD risk, independently of significant clinical confounding. Specifically, ΔADC <78 × 10-6 mm2/s and <66 × 10-6 mm2/s would help to identify T2D patients with the diagnosis of DKD and higher ESKD risk categories, respectively.

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.

Clinical Decision Support Tools in the Electronic Medical Record

The integration of clinical decision support (CDS) tools into electronic medical record (EMR) systems has become common. Although there are many benefits for both patients and providers from successful integration, barriers exist that prevent consistent and effective use of these tools. Such barriers include tool alert fatigue, lack of interoperability between tools and medical record systems, and poor acceptance of tools by care providers. However, successful integration of CDS tools into EMR systems have been reported; examples of these include the Statin Choice Decision Aid, and the Kidney Failure Risk Equation (KFRE). This article reviews the history of EMR systems and its integration with CDS tools, the barriers preventing successful integration, and the benefits reported from successful integration. This article also provides suggestions and strategies for improving successful integration, making these tools easier to use and more effective for care providers.

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.

Validation of the kidney failure risk equation in a Portuguese cohort

INTRODUCTION

In chronic kidney disease (CKD) patients, the risk of kidney replacement therapy (KRT) is highly variable. In 2011, Tangri et al. developed the kidney failure risk equations (KFRE) to predict the 2 and 5-year probability of requiring kidney replacement therapy (KRT). The KFRE is an easily calculated 4-variable equation which has been extensively validated in multiple cohorts. The aim of this study was to validate this risk score in a Portuguese cohort.

METHODS

We conducted a retrospective analysis of CKD patients stage 3-5 referred for nephrology consult at Centro Hospitalar Universitário Lisboa Norte during the first 6 months of 2018. Age, gender, estimated glomerular filtration rate (eGFR) and albuminuria were assessed. The 4-variable kidney failure risk equation (KFRE) calibrated to a non-North American population was calculated. Requirement of KRT was assessed in a 2-year follow-up. We assessed the Cox logistic regression method of the KFRE to predict KRT requirement and the discriminatory ability was determined using the receiver operating characteristic (ROC) curve. A cut-off value was defined as that with the highest validity.

RESULTS

360 patients were included and 54.4% were male. Mean age was 74.9±12.2 years, serum creatinine was 1.97±0.84mg/dL, eGFR was 33.4±12.13ml/min/1.73m2 and albuminuria was 571.1±848.3mg/g. Mean calculated risk score was 6.2±11.2%. Twenty-three patients required KRT (6.4%) in the two-year follow-up. The hazard ratio was 1.1 [95% CI (1.06-1.12), p<0.001] for the 2-year risk of KRT. The KFRE predicted progression to KRT requirement with an auROC of 0.903, [95% CI (0.86-0.95), p<0.001], with a sensitivity 91.3% and specificity of 71.8%. The optimal KFRE cut-off was >4.5% for 2-year nephrologist referral, with an hazard ratio of HR 26.7 [95% CI (6.15-116.3), p<0.001] for 2-year risk of KRT requirement.

DISCUSSION

We have independently externally validated the 2-year KFRE and shown that it has excellent discrimination. The KFRE should be incorporated in clinical care of patients with CKD to improve patient-clinician dialogue and provide guidance on timing of referral for nephrology evaluation and planning for dialysis access.

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.

Use of Histologic Parameters to Predict Glomerular Disease Progression: Findings From the China Kidney Biopsy Cohort Study

RATIONALE & OBJECTIVE

Challenges in achieving valid risk prediction and stratification impede treatment decisions and clinical research design for patients with glomerular diseases. This study evaluated whether chronic histologic changes, when complementing other clinical data, improved the prediction of disease outcomes across a diverse group of glomerular diseases.

STUDY DESIGN

Multicenter retrospective cohort study.

SETTING & PARTICIPANTS

4,982 patients with biopsy-proven glomerular disease who underwent native biopsy at 8 tertiary care hospitals across China in 2004-2020.

NEW PREDICTORS & ESTABLISHED PREDICTORS

Chronicity scores depicted as 4 categories of histological chronic change, as well as baseline clinical and demographic variables.

OUTCOME

Progression of glomerular disease defined as a composite of kidney failure or a ≥40% decrease in estimated glomerular filtration rate from the measurement at the time of biopsy.

ANALYTICAL APPROACH

Multivariable Cox proportional hazard models. The performance of predictive models was evaluated by C statistic, time-dependent area under the receiver operating characteristic curve (AUROC), net reclassification index, integrated discrimination index, and calibration plots.

RESULTS

The derivation and validation cohorts included 3,488 and 1,494 patients, respectively. During a median of 31 months of follow-up, a total of 444 (8.9%) patients had disease progression in the 2 cohorts. For prediction of the 2-year risk of disease progression, the AUROC of the model combining chronicity score and the Kidney Failure Risk Equation (KFRE) in the validation cohort was 0.76 (95% CI, 0.65-0.87); in comparison with the KFRE model (AUROC, 0.68 [95% CI, 0.56-0.79]), the combined model was significantly better (P = 0.04). The combined model also had a better fit, with a lower Akaike information criterion and a significant improvement in reclassification as assessed by the integrated discrimination improvements and net reclassification improvements. Similar improvements in predictive performance were observed in subgroup and sensitivity analyses.

LIMITATIONS

Selection bias, relatively short follow-up, lack of external validation.

CONCLUSIONS

Adding histologic chronicity scores to the KFRE model improved the prediction of kidney disease progression at the time of kidney biopsy in patients with glomerular diseases.

PLAIN-LANGUAGE SUMMARY

Risk prediction and stratification remain big challenges for treatment decisions and clinical research design for patients with glomerular diseases. The extent of chronic changes is an important component of kidney biopsy evaluations in glomerular disease. In this large multicenter cohort including 4,982 Chinese adults undergoing native kidney biopsy, we evaluated whether histologic chronicity scores, when added to clinical data, could improve the prediction of disease prognosis for a diverse set of glomerular diseases. We observed that adding histologic chronicity scores to the kidney failure risk equation improved the prediction of kidney disease progression at the time of kidney biopsy in patients with glomerular diseases.

Machine learning to predict end stage kidney disease in chronic kidney disease

The purpose of this study was to assess the feasibility of machine learning (ML) in predicting the risk of end-stage kidney disease (ESKD) from patients with chronic kidney disease (CKD). Data were obtained from a longitudinal CKD cohort. Predictor variables included patients' baseline characteristics and routine blood test results. The outcome of interest was the presence or absence of ESKD by the end of 5 years. Missing data were imputed using multiple imputation. Five ML algorithms, including logistic regression, naïve Bayes, random forest, decision tree, and K-nearest neighbors were trained and tested using fivefold cross-validation. The performance of each model was compared to that of the Kidney Failure Risk Equation (KFRE). The dataset contained 748 CKD patients recruited between April 2006 and March 2008, with the follow-up time of 6.3 ± 2.3 years. ESKD was observed in 70 patients (9.4%). Three ML models, including the logistic regression, naïve Bayes and random forest, showed equivalent predictability and greater sensitivity compared to the KFRE. The KFRE had the highest accuracy, specificity, and precision. This study showed the feasibility of ML in evaluating the prognosis of CKD based on easily accessible features. Three ML models with adequate performance and sensitivity scores suggest a potential use for patient screenings. Future studies include external validation and improving the models with additional predictor variables.

Healthcare professionals' perspectives on facilitators of and barriers to CKD management in primary care: a qualitative study in Singapore clinics

INTRODUCTION

The burden of chronic kidney disease (CKD) is rising globally including in Singapore. Primary care is the first point of contact for most patients with early stages of CKD. However, several barriers to optimal CKD management exist. Knowing healthcare professionals' (HCPs) perspectives is important to understand how best to strengthen CKD services in the primary care setting. Integrating a theory-based framework, we explored HCPs' perspectives on the facilitators of and barriers to CKD management in primary care clinics in Singapore.

METHODS

In-depth interviews were conducted on a purposive sample of 20 HCPs including 13 physicians, 2 nurses and 1 pharmacist from three public primary care polyclinics, and 4 nephrologists from one referral hospital. Interviews were audio recorded, transcribed verbatim and thematically analyzed underpinned by the Theoretical Domains Framework (TDF) version 2.

RESULTS

Numerous facilitators of and barriers to CKD management identified. HCPs perceived insufficient attention is given to CKD in primary care and highlighted several barriers including knowledge and practice gaps, ineffective CKD diagnosis disclosure, limitations in decision-making for nephrology referrals, consultation time, suboptimal care coordination, and lack of CKD awareness and self-management skills among patients. Nevertheless, intensive CKD training of primary care physicians, structured CKD-care pathways, multidisciplinary team-based care, and prioritizing nephrology referrals with risk-based assessment were key facilitators. Participants underscored the importance of improving awareness and self-management skills among patients. Primary care providers expressed willingness to manage early-stage CKD as a collaborative care model with nephrologists. Our findings provide valuable insights to design targeted interventions to enhance CKD management in primary care in Singapore that may be relevant to other countries.

CONCLUSIONS

The are several roadblocks to improving CKD management in primary care settings warranting urgent attention. Foremost, CKD deserves greater priority from HCPs and health planners. Multipronged approaches should urgently address gaps in care coordination, patient-physician communication, and knowledge. Strategies could focus on intensive CKD-oriented training for primary care physicians and building novel team-based care models integrating structured CKD management, risk-based nephrology referrals coupled with education and motivational counseling for patients. Such concerted efforts are likely to improve outcomes of patients with CKD and reduce the ESKD burden.

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.

The Kidney Failure Risk Equation for Prediction of Allograft Loss in Kidney Transplant Recipients

Rationale & Objective

The Kidney Failure Risk Equation (KFRE) is a simple widely validated prediction model using age, sex, estimated glomerular filtration rate, and urinary albumin-creatinine ratio to predict the risk for end-stage kidney disease. Data are limited for its applicability to kidney transplant recipients.

Study Design

Validation study of the KFRE as a post hoc analysis of the Folic Acid for Vascular Outcomes Reduction in Transplantation (FAVORIT) Trial.

Setting & Participants

Adult kidney transplant recipients with functioning kidney allografts at least 6 months posttransplantation from 30 centers in the United States, Canada, and Brazil. Participants with estimated glomerular filtration rates < 60 mL/min/1.73 m² at study entry were included.

Predictor

2- and 5-year kidney failure risk predicted by the KFRE using variables at study entry.

Outcome

Graft loss, defined by initiation of dialysis.

Analytical Approach

Discrimination of the KFRE was assessed using C statistics; calibration was assessed by plotting predicted risk against observed cumulative incidence of graft loss.

Results

2,889 participants were included. Within 2 years, 98 participants developed graft loss, 107 participants died with a functioning graft, and 129 participants were lost to follow-up, and by 5 years, 252 had developed graft loss, 265 died with a functioning graft, and 1,543 were lost to follow-up. The KFRE demonstrated accurate calibration and discrimination (C statistic, 0.85 [95% CI, 0.81-0.88] at 2 years and 0.81 [95% CI, 0.78-0.84] at 5 years); performance was similar regardless of donor type (living vs deceased) and graft vintage, with the noted exception of poorer calibration for graft vintage less than 2 years.

Limitations

Unavailable cause of graft loss.

Conclusions

The KFRE accurately predicted the risk for graft loss among adult kidney transplant recipients with graft vintage longer than 2 years and may be a useful prognostic tool for nephrologists caring for kidney transplant recipients.