Update to the study protocol: minimisation of dialysis risk in hospital patients with chronic kidney disease (MinDial): a multicentre, stepped-wedge, cluster-randomised controlled trial.

This document reports a brief update to the previously published protocol of the MinDial study.Trial registration German Clinical Trials Register DRKS00029691. Registered on 12 Sept. 2022, https://drks.de/search/en/trial/DRKS00029691 .

Minimisation of dialysis risk in hospital patients with chronic kidney disease (MinDial): study protocol for a multicentre, stepped-wedge, cluster-randomised controlled trial.

BACKGROUND: Early identification of patients with chronic kidney disease (CKD) and advancing kidney insufficiency, followed by specialist care, can decelerate the progression of the disease. However, awareness of the importance and possible consequences of kidney insufficiency is low among doctors and patients. Since kidney insufficiency can be asymptomatic even in higher stages, it is often not even known to those belonging to risk groups. This study aims to clarify whether, for hospitalised patients with advanced chronic kidney disease, a risk-based appointment with a nephrology specialist reduces disease progression. METHODS: The target population of the study is hospitalised CKD patients with an increased risk of end-stage renal disease (ESRD), more specifically with an ESRD risk of at least 9% in the next 5 years. This risk is estimated by the internationally validated Kidney Failure Risk Equation (KFRE). The intervention consists of a specific appointment with a nephrology specialist after the hospital stay, while control patients are discharged from the hospital as usual. Eight medical centres include participants according to a stepped-wedge design, with randomised sequential centre-wise crossover from recruiting patients into the control group to recruitment to the intervention. The estimated glomerular filtration rate (eGFR) is measured for each patient during the hospital stay and after 12 months within the regular care by the general practitioner. The difference in the change of the eGFR over this period is compared between the intervention and control groups and considered the primary endpoint. DISCUSSION: This study is designed to evaluate the effect of risk-based appointments with nephrology specialists for hospitalised CKD patients with an increased risk of end-stage renal disease. If the intervention is proven to be beneficial, it may be implemented in routine care. Limitations will be examined and discussed. The evaluation will include further endpoints such as non-guideline-compliant medication, economic considerations and interviews with contributing physicians to assess the acceptance and feasibility of the intervention. TRIAL REGISTRATION: German Clinical Trials Register DRKS00029691 . Registered on 12 September 2022.

Evaluation of a proteomic signature coupled with the kidney failure risk equation in predicting end stage kidney disease in a chronic kidney disease cohort.

BACKGROUND: The early identification of patients at high-risk for end-stage renal disease (ESRD) is essential for providing optimal care and implementing targeted prevention strategies. While the Kidney Failure Risk Equation (KFRE) offers a more accurate prediction of ESRD risk compared to static eGFR-based thresholds, it does not provide insights into the patient-specific biological mechanisms that drive ESRD. This study focused on evaluating the effectiveness of KFRE in a UK-based advanced chronic kidney disease (CKD) cohort and investigating whether the integration of a proteomic signature could enhance 5-year ESRD prediction. METHODS: Using the Salford Kidney Study biobank, a UK-based prospective cohort of over 3000 non-dialysis CKD patients, 433 patients met our inclusion criteria: a minimum of four eGFR measurements over a two-year period and a linear eGFR trajectory. Plasma samples were obtained and analysed for novel proteomic signals using SWATH-Mass-Spectrometry. The 4-variable UK-calibrated KFRE was calculated for each patient based on their baseline clinical characteristics. Boruta machine learning algorithm was used for the selection of proteins most contributing to differentiation between patient groups. Logistic regression was employed for estimation of ESRD prediction by (1) proteomic features; (2) KFRE; and (3) proteomic features alongside KFRE. RESULTS: SWATH maps with 943 quantified proteins were generated and investigated in tandem with available clinical data to identify potential progression biomarkers. We identified a set of proteins (SPTA1, MYL6 and C6) that, when used alongside the 4-variable UK-KFRE, improved the prediction of 5-year risk of ESRD (AUC = 0.75 vs AUC = 0.70). Functional enrichment analysis revealed Rho GTPases and regulation of the actin cytoskeleton pathways to be statistically significant, inferring their role in kidney function and the pathogenesis of renal disease. CONCLUSIONS: Proteins SPTA1, MYL6 and C6, when used alongside the 4-variable UK-KFRE achieve an improved performance when predicting a 5-year risk of ESRD. Specific pathways implicated in the pathogenesis of podocyte dysfunction were also identified, which could serve as potential therapeutic targets. The findings of our study carry implications for comprehending the involvement of the Rho family GTPases in the pathophysiology of kidney disease, advancing our understanding of the proteomic factors influencing susceptibility to renal damage.

Protocol for a systematic review of the application of the kidney failure risk equation and Oxford classification in estimating prognosis in IgA nephropathy.

BACKGROUND: IgA nephropathy (IgAN) is a common cause of chronic kidney disease (CKD) and end-stage renal disease (ESRD). Outcomes are highly variable and predicting risk of disease progression at an individual level is challenging. Accurate risk stratification is important to identify individuals most likely to benefit from treatment. The Kidney Failure Risk Equation (KFRE) has been extensively validated in CKD populations and predicts the risk of ESRD at 2 and 5 years using non-invasive tests; however, its predictive performance in IgAN is unknown. The Oxford classification (OC) describes pathological features demonstrated on renal biopsy that are associated with adverse clinical outcomes that may also inform prognosis. The objective of this systematic review is to compare the KFRE with the OC in determining prognosis in IgAN. METHODS: A systematic review will be conducted and reported in line with PRISMA guidelines (PRISMA-P checklist attached as Additional file 1). Inclusion criteria will be cohort studies that apply the KFRE or OC to determine the risk of CKD progression or ESRD in individuals with IgAN. Multiple databases will be searched in duplicate to identify relevant studies, which will be screened first by title, then by abstract and then by full-text analysis. Results will be collated for comparison. Risk of bias and confidence assessments will be conducted independently by two reviewers, with a third reviewer available if required. DISCUSSION: Identifying individuals at the highest risk of progression to ESRD is challenging in IgAN, due to the heterogeneity of clinical outcomes. Risk prediction tools have been developed to guide clinicians; however, it is imperative that these aids are accurate and reproducible. The OC is based on observations made by specialist renal pathologists and may be open to observer bias, therefore the utility of prediction models incorporating this classification may be diminished, particularly as in the future novel biomarkers may be incorporated into clinical practice. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42022364569.

External Validation of the Kidney Failure Risk Equation Among Urban Community-Based Chinese Patients With CKD.

Rationale & Objective: The Kidney Failure Risk Equations have been proven to perform well in multinational databases, whereas validation in Asian populations is lacking. This study sought to externally validate the equations in a community-based chronic kidney disease cohort in China. Study Design: A retrospective cohort study. Setting & Participants: Patients with and estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m2 dwelling in an industrialized coastal city of China. Exposure: Age, sex, eGFR, and albuminuria were included in the 4-variable model, whereas serum calcium, phosphate, bicarbonate, and albumin levels were added to the previously noted variables in the 8-variable model. Outcome: Initiation of long-term dialysis treatment. Analytical Approach: Model discrimination, calibration, and clinical utility were evaluated by Harrell's C statistic, calibration plots, and decision curve analysis, respectively. Results: A total of 4,587 participants were enrolled for validation of the 4-variable model, whereas 1,414 were enrolled for the 8-variable model. The median times of follow-up were 4.0 (interquartile range: 2.6-6.3) years for the 4-variable model and 3.4 (2.2-5.6) years for the 8-variable model. For the 4-variable model, the C statistics were 0.750 (95% CI: 0.615-0.885) for the 2-year model and 0.766 (0.625-0.907) for the 5-year model, whereas the values were 0.756 (0.629-0.883) and 0.774 (0.641-0.907), respectively, for the 8-variable model. Calibration was acceptable for both the 4-variable and 8-variable models. Decision curve analysis for the models at the 5-year scale performed better throughout different net benefit thresholds than the eGFR-based (<30 mL/min/1.73 m2) strategy. Limitations: A large proportion of patients lack albuminuria measurements, and only a subset of population could provide complete data for the 8-variable equation. Conclusions: The kidney failure risk equations showed acceptable discrimination and calibration and better clinical utility than the eGFR-based strategy for incidence of kidney failure among community-based urban Chinese patients with chronic kidney disease.

Accurate and reliable risk evaluation of chronic kidney disease (CKD) prognosis can be helpful for physicians to make decisions concerning treatment opportunity and therapeutic strategy. The kidney failure risk equation is an outstanding model for predicting risk of kidney failure among patients with CKD. However, the equation is lacking validation among Chinese populations. In the current study, we demonstrated that the equation had good discrimination among an urban community-based cohort of patients with CKD in China. The calibration was also acceptable. Decision curve analysis also showed that the equation performed better than a traditional kidney function-based strategy. The results provide the basis for using predictions derived from the kidney failure risk equation to improve the management of patients with CKD in community settings in China.

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.

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.

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.

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.

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.

Using the kidney failure risk equation to predict end-stage kidney disease in CKD patients of South Asian ethnicity: an external validation study.

BACKGROUND: The kidney failure risk equation (KFRE) predicts the 2- and 5-year risk of needing kidney replacement therapy (KRT) using four risk factors - age, sex, urine albumin-to-creatinine ratio (ACR) and creatinine-based estimated glomerular filtration rate (eGFR). Although the KFRE has been recalibrated in a UK cohort, this did not consider minority ethnic groups. Further validation of the KFRE in different ethnicities is a research priority. The KFRE also does not consider the competing risk of death, which may lead to overestimation of KRT risk. This study externally validates the KFRE for patients of South Asian ethnicity and compares methods for accounting for ethnicity and the competing event of death. METHODS: Data were gathered from an established UK cohort containing 35,539 individuals diagnosed with chronic kidney disease. The KFRE was externally validated and updated in several ways taking into account ethnicity, using recognised methods for time-to-event data, including the competing risk of death. A clinical impact assessment compared the updated models through consideration of referrals made to secondary care. RESULTS: The external validation showed the risk of KRT differed by ethnicity. Model validation performance improved when incorporating ethnicity and its interactions with ACR and eGFR as additional risk factors. Furthermore, accounting for the competing risk of death improved prediction. Using criteria of 5 years ≥ 5% predicted KRT risk, the competing risks model resulted in an extra 3 unnecessary referrals (0.59% increase) but identified an extra 1 KRT case (1.92% decrease) compared to the previous best model. Hybrid criteria of predicted risk using the competing risks model and ACR ≥ 70 mg/mmol should be used in referrals to secondary care. CONCLUSIONS: The accuracy of KFRE prediction improves when updated to consider South Asian ethnicity and to account for the competing risk of death. This may reduce unnecessary referrals whilst identifying risks of KRT and could further individualise the KFRE and improve its clinical utility. Further research should consider other ethnicities.

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 / Validación de la ecuación de riesgo de insuficiencia renal en una cohorte portuguesa

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%. (AU)

Introducción: En pacientes con enfermedad renal crónica (ERC), el riesgo de la terapia de reemplazo renal (TRR) es muy variable. En 2011, Tangri et al. desarrollaron las ecuaciones de riesgo de insuficiencia renal (KFRE) para predecir la probabilidad de 2 y 5años de requerir terapia de reemplazo renal (KRT). El KFRE es una ecuación de 4 variables de fácil cálculo que ha sido ampliamente validada en múltiples cohortes. El objetivo de este estudio fue validar esta puntuación de riesgo en una cohorte portuguesa. Métodos: Se realizó un análisis retrospectivo de pacientes con ERC estadio 3-5 remitidos para consulta de Nefrología en el Centro Hospitalario Universitário Lisboa Norte durante los primeros 6meses de 2018. Se evaluaron la edad, el sexo, el filtrado glomerular estimado (TFGe) y la albuminuria. Se calculó la ecuación de riesgo de insuficiencia renal (KFRE) de 4 variables calibrada para una población no norteamericana. La necesidad de KRT se evaluó en un seguimiento de 2años. Evaluamos el método de regresión logística de Cox del KFRE para predecir el requisito de KRT, y la capacidad discriminatoria se determinó utilizando la curva de característica operativa del receptor (ROC). Se definió como valor de corte el de mayor validez. Resultados: Se incluyeron 360 pacientes, y el 54,4% eran varones. La edad media fue de 74,9±12,2 años, la creatinina sérica de 1,97±0,84mg/dl, la TFGe de 33,4±12,13ml/min/1,73m2 y la albuminuria de 571,1±848,3mg/g. La puntuación de riesgo media calculada fue de 6,2±11,2%. Veintitrés pacientes requirieron KRT (6,4%) en los 2años de seguimiento. El cociente de riesgos instantáneos fue de 1,1 (IC del 95%: 1,06-1,12; p<0,001) para el riesgo de 2años de KRT. El KFRE predijo la progresión al requerimiento de KRT con un auROC de 0,903 (p<0,001; IC del 95%: 0,86-0,95), con una sensibilidad del 91,3% y una especificidad del 71,8%. (AU)

Planning vascular access creation: The promising role of the kidney failure risk equation.

BACKGROUND: Planning for vascular access (VA) creation is essential in pre-dialysis patients although optimal timing for VA referral and placement is debatable. Guidelines suggest referral when eGFR is 15-20 mL/min/1.73 m2. This study aimed to validate the use of kidney failure risk equation (KFRE) in VA planning. METHODS: Retrospective analysis of all adult patients with CKD who were referred for first VA placement, namely AVF or AVG, at a tertiary center, between January 2018 and December 2019. The four-variable KFRE was calculated. Start of KRT, mortality, and VA placement were assessed in a 2-year follow-up. We used Cox regression to predict KRT start and calculated the ROC curve. RESULTS: 256 patients were included and 64.5% were male, mean age was 70.4 ± 12.9 years and mean eGFR was 16.09 ± 10.43 mL/min/1.73 m2. One hundred fifty-nine patients required KRT (62.1%) and 72 (28.1%) died in the 2-year follow-up. The KFRE accurately predicted KRT start within 2-years (38.3 ± 23.8% vs 17.6 ± 20.9%, p < 0.001; HR 1.05 95% CI (1.06-1.12), p < 0.001), with an auROC of 0.788 (p < 0.001, 95% CI (0.733-0.837)). The optimal KFRE cut-off was >20%, with a HR of 9.2 (95% CI (5.06-16.60), p < 0.001). Patients with KFRE ⩾ 20% had a significant lower mean time from VA consult to KRT initiation (10.8 ± 9.4 vs 15.6 ± 10.3 months, p < 0.001). On a sub-analysis of patients with an eGFR < 20 mL/min/1.73 m2, a KFRE ⩾ 20% was also a significant predictor of 2-year start of KRT, with an HR of 6.61 (95% CI (3.49-12.52), p < 0.001). CONCLUSION: KFRE accurately predicted 2-year KRT start in this cohort of patients. A KFRE ⩾ 20% can help to establish higher priority patients for VA placement. The authors suggest referral for VA creation when eGFR < 20 mL/min/1.73 m2 and KFRE ⩾ 20%.

Association of the Kidney Failure Risk Equation With High Health Care Costs.

Introduction: The Kidney Failure Risk Equations (KFRE) are accurate and validated to predict the risk of kidney failure in individuals with chronic kidney disease (CKD), but their potential to predict health care costs in the US health care system is unknown. We assessed the association of kidney failure risk from the 4-variable and 8-variable 2-year KFRE models with monthly health care costs in US patients with CKD stages G3 and G4. Methods: This was an ancillary study to a larger observational, retrospective cohort study examining the association between serum bicarbonate and adverse kidney outcomes. Monthly medical costs were calculated from individual health care insurance claims. Generalized linear regression models were used to examine the association of KFRE score with health care costs. Results: A total of 1721 patients qualified for the study (1475 and 246 with CKD stages G3 and G4, respectively). For 8-variable KFRE, each 1% (absolute) increase in risk was associated with 13.5% (P < 0.001) and 4.1% (P < 0.001) higher monthly costs for patients with CKD stage G3 and G4, respectively. For 4-variable KFRE, a 1% increase in risk was associated with 6.7% (P = 0.016) and 2.9% (P= 0.014) increase in monthly costs for patients with CKD stage G3 and G4, respectively. Conclusion: Higher risks of kidney failure as predicted by the 4-variable or 8-variable KFRE were associated with higher 2-year medical costs for patients with CKD stages G3 and G4. The KFRE may be a useful tool to anticipate medical costs and target cost-reducing interventions for patients at risk of kidney failure.

Utility of the Kidney Failure Risk Equation and Estimated GFR for Estimating Time to Kidney Failure in Advanced CKD.

RATIONALE & OBJECTIVE: The Kidney Failure Risk Equation (KFRE) predicts the 2-year risk of kidney failure for patients with chronic kidney disease (CKD). Translating KFRE-predicted risk or estimated glomerular filtration rate (eGFR) into time to kidney failure could inform decision making for patients approaching kidney failure. STUDY DESIGN: Retrospective cohort. SETTING & PARTICIPANTS: CKD Outcomes and Practice Patterns Study (CKDOPPS) cohort of patients with an eGFR<60mL/min/1.73m2 from 34 US nephrology practices (2013-2021). EXPOSURE: 2-year KFRE risk or eGFR. OUTCOME: Kidney failure defined as initiation of dialysis or kidney transplantation. ANALYTICAL APPROACH: Accelerated failure time (Weibull) models used to estimate the median, 25th, and 75th percentile times to kidney failure starting from KFRE values of 20%, 40%, and 50%, and from eGFR values of 20, 15, and 10mL/min/1.73m2. We examined variability in time to kidney failure by age, sex, race, diabetes status, albuminuria, and blood pressure. RESULTS: Overall, 1,641 participants were included (mean age 69±13 years; median eGFR of 28mL/min/1.73m2 [IQR 20-37mL/min/1.73 m2]). Over a median follow-up period of 19 months (IQR, 12-30 months), 268 participants developed kidney failure, and 180 died before reaching kidney failure. The median estimated time to kidney failure was widely variable across patient characteristics from an eGFR of 20mL/min/1.73m2 and was shorter for younger age, male sex, Black (versus non-Black), diabetes (vs no diabetes), higher albuminuria, and higher blood pressure. Estimated times to kidney failure were comparably less variable across these characteristics for KFRE thresholds and eGFR of 15 or 10mL/min/1.73m2. LIMITATIONS: Inability to account for competing risks when estimating time to kidney failure. CONCLUSIONS: Among those with eGFR<15mL/min/1.73m2 or KFRE risk>40%), both KFRE risk and eGFR showed similar relationships with time to kidney failure. Our results demonstrate that estimating time to kidney failure in advanced CKD can inform clinical decisions and patient counseling on prognosis, regardless of whether estimates are based on eGFR or the KFRE. PLAIN-LANGUAGE SUMMARY: Clinicians often talk to patients with advanced chronic kidney disease about the level of kidney function expressed as the estimated glomerular filtration rate (eGFR) and about the risk of developing kidney failure, which can be estimated using the Kidney Failure Risk Equation (KFRE). In a cohort of patients with advanced chronic kidney disease, we examined how eGFR and KFRE risk predictions corresponded to the time patients had until reaching kidney failure. Among those with eGFR<15mL/min/1.73m2 or KFRE risk > 40%), both KFRE risk and eGFR showed similar relationships with time to kidney failure. Estimating time to kidney failure in advanced CKD using either eGFR or KFRE can inform clinical decisions and patient counseling on prognosis.

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.

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.

Impacting Management of Chronic Kidney Disease Through Primary Care Practice Audits: A Quality Improvement Study.

Background: Risk prediction tools are important in chronic disease management, but their implementation into clinical workflow is often limited by lack of electronic health record (EHR)-linked solutions. Objective: To implement the Khure Health (KH) clinical decision support platform with an artificial intelligence (AI)-enabled algorithm for chronic kidney disease (CKD) risk detection in 201 primary care provider practices across Ontario. Design: Multi-practice quality improvement study. Setting: The study was conducted in Ontario, Canada. Participants: 201 primary care practices. Measurements: Per-practice CKD risk stratification and clinician action. Methods: Data for estimated glomerular filtration rate (eGFR), albuminuria, demographics, and comorbid conditions were extracted from the EHR using KH's natural language processing (NLP) algorithms. Patients already on dialysis, visiting a nephrologist, older than 85, or already on a sodium-glucose cotransporter 2 inhibitor (SGLT2i) were excluded. The remaining individuals were risk stratified using the kidney failure risk equation, presence or absence of cardiovascular disease (CVD), or other comorbid conditions. A dashboard with disease-specific educational information and links to the EHRs of the identified patients was created. Results: We screened 361 299 individuals and identified 8194 patients with CKD Stage 3 at risk for progression or cardiovascular events. A total of 620 individuals were at high risk for CKD progression or CVD, and 2592 were at intermediate risk. A total of 2010 individuals (10 patients per practice) at high or moderate risk were selected for a chart audit, and appropriate additional testing (repeat eGFR or albuminuria) or prescription of disease-modifying therapy occurred in 24.32% of these patients. Limitations: Data on comorbidities, medications, or demographic variables are not available for presentation or statistical analysis due to privacy legislation and primary care provider (PCP) custodianship over EHR data. Conclusion: An AI-enabled EHR clinical decision support application that can detect and risk stratify patients with CKD can enable improved laboratory testing and management. Larger trials of clinical decision support and practice audit applications will be needed to impact CKD management nationally.

Contexte: Les outils de prédiction des risques sont importants dans la gestion des maladies chroniques, mais leur intégration dans le flux de travail clinique est souvent limitée par le manque de solutions liées aux dossiers de santé informatisés. Objectif: Mettre en œuvre, dans 201 cabinets de soins primaires de l'Ontario, la plateforme d'aide à la décision clinique Khure Health (KH) dotée d'un algorithme basé sur l'intelligence artificielle (IA) pour détecter les risques d'insuffisance rénale chronique (IRC). Conception: Étude d'amélioration de la qualité dans plusieurs cabinets. Cadre: Étude menée en Ontario (Canada). Sujets: 201 cabinets de soins primaires. Mesures: Stratification du risque d'IRC par cabinet et actions du clinicien. Méthodologie: Les données relatives au débit de filtration glomérulaire estimé (DFGe), à l'albuminurie, à la démographie et aux maladies concomitantes ont été extraites des dossiers de santé informatisés (DSI) à l'aide des algorithmes de traitement du langage naturel (TLN) de KH. Ont été exclus les patients sous dialyse, suivis par un néphrologue, âgés de plus de 85 ans ou traités avec un inhibiteur du cotransporteur sodium-glucose de type 2 (SGLT2i). Les autres individus ont été stratifiés selon le risque évalué par l'équation prédictive du risque d'évolution vers l'insuffisance rénale, et par la présence ou l'absence de maladie cardiovasculaire ou d'autres affections concomitantes. Un tableau de bord contenant des informations éducatives sur la maladie et des liens vers les DSI des patients identifiés a été créé. Résultats: Nous avons examiné les dossiers de 361 299 personnes et identifié 8 194 patients atteints d'IRC de stade 3 présentant un risque de progression de l'IRC ou d'événements cardiovasculaires. De ces 8 194 patients, 620 présentaient un risque jugé élevé et 2 592 un risque modéré. En tout, 2 010 personnes (10 patients par cabinet) présentant un risque élevé ou modéré ont été sélectionnées pour une vérification des dossiers. Les tests supplémentaires appropriés (répétition des mesures du DFGe ou de l'albuminurie) ou la prescription de traitement modifiant la maladie ont été ordonnés chez 24,32 % de ces patients. Limites: Les données sur les maladies concomitantes, la médication ou les variables démographiques n'étaient pas disponibles pour la présentation ou l'analyse statistique en raison de la loi sur la protection de la vie privée et du fait que les DSI sont sous la garde du médecin de soins primaires. Conclusion: Une application d'aide à la prise de décisions cliniques basée sur l'IA pour les DSI, qui est capable de détecter et de stratifier les risques chez les patients atteints d'IRC, pourrait permettre d'améliorer la gestion de la maladie et les tests de laboratoire. Des essais à plus grande échelle portant sur les applications d'aide à la décision clinique et de vérification des pratiques seront nécessaires pour avoir une incidence sur la gestion de l'IRC à l'échelle nationale.

Risk-based versus GFR threshold criteria for nephrology referral in chronic kidney disease.

Chronic kidney disease (CKD) and kidney failure are global health problems associated with morbidity, mortality and healthcare costs, with unequal access to kidney replacement therapy between countries. The diversity of guidelines concerning referral from primary care to a specialist nephrologist determines different outcomes around the world among patients with CKD where several guidelines recommend referral when the glomerular filtration rate (GFR) is <30 mL/min/1.73 m2 regardless of age. Additionally, fixed non-age-adapted diagnostic criteria for CKD that do not distinguish correctly between normal kidney senescence and true kidney disease can lead to overdiagnosis of CKD in the elderly and underdiagnosis of CKD in young patients and contributes to the unfair referral of CKD patients to a kidney specialist. Non-age-adapted recommendations contribute to unnecessary referral in the very elderly with a mild disease where the risk of death consistently exceeds the risk of progression to kidney failure and ignore the possibility of effective interventions of a young patient with long life expectancy. The opportunity of mitigating CKD progression and cardiovascular complications in young patients with early stages of CKD is a task entrusted to primary care providers who are possibly unable to optimally accomplish guideline-directed medical therapy for this purpose. The shortage in the nephrology workforce has classically led to focused referral on advanced CKD stages preparing for kidney replacement, but the need for hasty referral to a nephrologist because of the urgent requirement for kidney replacement therapy in advanced CKD is still observed and changes are required to move toward reducing the kidney failure burden. The Kidney Failure Risk Equation (KFRE) is a novel tool that can guide wiser nephrology referrals and impact patients.