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.

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.

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.

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.

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.

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.

A Dynamic Predictive Model for Progression of CKD.

BACKGROUND: Predicting the progression of chronic kidney disease (CKD) is vital for clinical decision making and patient-provider communication. We previously developed an accurate static prediction model that used single-timepoint measurements of demographic and laboratory variables. STUDY DESIGN: Development of a dynamic predictive model using demographic, clinical, and time-dependent laboratory data from a cohort of patients with CKD stages 3 to 5. SETTING & PARTICIPANTS: We studied 3,004 patients seen April 1, 2001, to December 31, 2009, in the outpatient CKD clinic of Sunnybrook Hospital in Toronto, Canada. CANDIDATE PREDICTORS: Age, sex, and urinary albumin-creatinine ratio at baseline. Estimated glomerular filtration rate (eGFR), serum albumin, phosphorus, calcium, and bicarbonate values as time-dependent predictors. OUTCOMES: Treated kidney failure, defined by initiation of dialysis therapy or kidney transplantation. ANALYTICAL APPROACH: We describe a dynamic (latest-available-measurement) prediction model using time-dependent laboratory values as predictors of outcome. Our static model included all 8 candidate predictors. The latest-available-measurement model includes age and the latter 5 variables as time-dependent predictors. We used Cox proportional hazards models for time to kidney failure and compared discrimination, calibration, model fit, and net reclassification for the models. RESULTS: We studied 3,004 patients, who had 344 kidney failure events over a median follow-up of 3 years and an average of 5 clinic visits. eGFR was more strongly associated with kidney failure in the latest-available-measurement model versus the baseline visit static model (HR, 0.44 vs 0.65). The association of calcium level was unchanged, but male sex and phosphorus, albumin, and bicarbonate levels were no longer significant. Discrimination and goodness of fit showed incremental improvement with inclusion of time-dependent covariates (integrated discrimination improvement, 0.73%; 95% CI, 0.56%-0.90%). LIMITATIONS: Our data were derived from a nephrology clinic at a single center. We were unable to include time-dependent changes in albuminuria. CONCLUSIONS: A latest-available-measurement predictive model with eGFR as a time-dependent predictor can incrementally improve risk prediction for kidney failure over a static model with only a single eGFR.