Pro: Risk scores for chronic kidney disease progression are robust, powerful and ready for implementation

Omics Studies in CKD: Diagnostic Opportunities and Therapeutic Potential

Omics technologies have significantly advanced the prediction and therapeutic approaches for chronic kidney disease (CKD) by providing comprehensive molecular insights. This is a review of the current state and future prospects of integrating biomarkers into the clinical practice for CKD, aiming to improve patient outcomes by targeted therapeutic interventions. In fact, the integration of genomic, transcriptomic, proteomic, and metabolomic data has enhanced our understanding of CKD pathogenesis and identified novel biomarkers for an early diagnosis and targeted treatment. Advanced computational methods and artificial intelligence (AI) have further refined multi-omics data analysis, leading to more accurate prediction models for disease progression and therapeutic responses. These developments highlight the potential to improve CKD patient care with a precise and individualized treatment plan .

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%.

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.

Validation of the Kidney Failure Risk Equation in the Colombian Population

Introduction

Chronic kidney disease prevention programs must identify patients at risk of early progression to provide better treatment and prolong kidney replacement therapy-free survival. Risk equations have been developed and validated in cohorts outside of Colombia, so this study aims to evaluate the discrimination and calibration of the four-variable kidney failure risk equation in a Colombian population where it has yet to be validated.

Methods

External validation study of a kidney failure risk equation using a historical cohort of patients with CKD stages 3, 4, and 5, adults without a history of dialysis or kidney transplantation with a two-year follow-up, belonging to the Baxter Renal Care Services Colombia network. The discriminatory capacity of the model was evaluated by the concordance index using Harrell's C statistic, and the time-dependent area under the receiver operating characteristic (ROC) curve was estimated using the nearest neighbor method, as well as the optimal cut-off point for sensitivity and specificity. Calibration was determined by the degree of agreement between the observed outcome and the probabilities predicted by the model using the Hosmer-Lemeshow statistic.

Results

A total of 5,477 patients were included, with a mean age of 72 years, 36.4% diabetic, and a mean baseline eGFR of 36 ml/min/1.73 m2. The rate of dialysis initiation was three events per 100 patient-years, 95% CI (2.9-3.6). The optimal cutoff for sensitivity was 0.94, for specificity, 0.76, and the area under the ROC curve was 0.92. Harrell's C-statistic was 0.88 for the total population, 0.88 for diabetic patients, and 0.93 for those 65 years or older. The validation of the model showed good calibration.

Conclusions

In this Colombian cohort, the four-variable KFRE with a two-year prediction horizon has excellent calibration and discrimination, and its use in the care of CKD Colombian patients is recommended.

New marker for chronic kidney disease progression and mortality in medical-word virtual space

A new marker reflecting the pathophysiology of chronic kidney disease (CKD) has been desired for its therapy. In this study, we developed a virtual space where data in medical words and those of actual CKD patients were unified by natural language processing and category theory. A virtual space of medical words was constructed from the CKD-related literature (n = 165,271) using Word2Vec, in which 106,612 words composed a network. The network satisfied vector calculations, and retained the meanings of medical words. The data of CKD patients of a cohort study for 3 years (n = 26,433) were transformed into the network as medical-word vectors. We let the relationship between vectors of patient data and the outcome (dialysis or death) be a marker (inner product). Then, the inner product accurately predicted the outcomes: C-statistics of 0.911 (95% CI 0.897, 0.924). Cox proportional hazards models showed that the risk of the outcomes in the high-inner-product group was 21.92 (95% CI 14.77, 32.51) times higher than that in the low-inner-product group. This study showed that CKD patients can be treated as a network of medical words that reflect the pathophysiological condition of CKD and the risks of CKD progression and mortality.

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.

Implementing a Formalized Risk-Based Approach to Determine Candidacy for Multidisciplinary CKD Care: A Descriptive Cohort Study

Background

The kidney failure risk equation (KFRE) can be used to predict progression to end-stage kidney disease in a clinical setting.

Objective

Evaluate implementation of a formalized risk-based approach in nephrologists' outpatient clinics and multidisciplinary chronic kidney disease (CKD) clinics to determine candidacy for multidisciplinary care, and the impact of CKD care selection on clinical outcomes.

Design

Population-based descriptive cohort study.

Setting

Alberta Kidney Care South.

Patients

Adults attending or considered for a multidisciplinary CKD clinic between April 1, 2017, and March 31, 2019.

Measurements

Exposure-The course of CKD care assigned by the nephrologist: management at multidisciplinary CKD clinic; management by a nephrologist or primary care physician. Primary Outcome-CKD progression, defined as commencement of kidney replacement therapy (KRT). Secondary Outcomes-Death, emergency department visits, and hospitalizations.

Methods

We linked operational data from the clinics (available until March 31, 2019) with administrative health and laboratory data (available until March 31, 2020). Comparisons among patient groups, courses of care, and clinical settings with negative binomial regression count models and calculated unadjusted and fully adjusted incidence rate ratios. For the all-cause death outcome, we used Cox survival models to calculate unadjusted and fully adjusted hazard ratios.

Results

Of the 1748 patients for whom a KFRE was completed, 1347 (77%) remained in or were admitted to a multidisciplinary CKD clinic, 310 (18%) were managed by a nephrologist only, and 91 (5%) were referred back for management by their primary care physician. There was a much higher kidney failure risk among patients who remained at or were admitted to a multidisciplinary CKD clinic (median 2-year risk of 34.7% compared with 3.6% and 0.8% who remained with a nephrologist or primary care physician, respectively). None of the people managed by their primary care physician alone commenced KRT, while only 2 (0.6%) managed by a nephrologist without multidisciplinary CKD care commenced KRT. The rates of emergency department visits, hospitalizations, and death were lower in those assigned to management outside the multidisciplinary CKD clinics when compared with those managed in the multidisciplinary care setting.

Limitations

The follow-up period may not have been long enough to determine outcomes, and potentially limited generalizability given variability of care in multidisciplinary clinics.

Conclusions

Our findings indicate that a portion of patients can be directed to less resource-intensive care without a higher risk of adverse events.

Trial registration

Not applicable.

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

Rationale & Objective

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

Study Design

Retrospective observational study.

Setting & Participants

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

Predictors

Patient demographic and laboratory characteristics.

Outcomes

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

Analytical Approach

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

Results

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

Limitations

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

Conclusions

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

Plain-Language Summary

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

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.

CHA2DS2VASc score predicts risk of end stage renal disease in patients with atrial fibrillation: Long-term follow-up study

Background

End stage renal disease (ESRD) is an increasing worldwide epidemic disease. CHA2DS2-VASc score is a well-established predictor of cardiovascular outcome among atrial fibrillation (AF) patients.

Objective

The aim of this study was to test whether CHA2DS2-VASc score is a good predictor for incident ESRD events.

Methods

This is a retrospective cohort study (from January 2010 to December 2020) with median follow-up of 61.7 months. Clinical parameters and baseline characteristics were recorded. The endpoint was defined as ESRD with dialysis dependent.

Results

The study cohort comprised 29,341 participants. Their median age was 71.0 years, 43.2% were male, 21.5% had diabetes mellitus, 46.1% had hypertension, and mean CHA2DS2-VASc score was 2.89. CHA2DS2-VASc score was incrementally associated with the risk of ESRD status during follow-up. Using the univariate Cox model, we found a 26% increase in ESRD risk with an increase of one point in the CHA2DS2-VASc score (HR 1.26 [1.23-1.29], P < 0.001). And using the multi-variate Cox model adjusted by initial CKD stage, we still observed a 5.9% increase in risk of ESRD with a one-point increase in the CHA2DS2-VASc score (HR 1.059 [1.037-1.082], P < 0.001). The CHA2DS2-VASC score and the initial stage of CKD were associated with the risk of ESRD development in patients with AF.

Conclusions

Our results first confirmed the utility of CHA2DS2-VASC score in predicting progression to ESRD in AF patients. The efficiency is best in CKD stage 1.

Machine-learning-based Web system for the prediction of chronic kidney disease progression and mortality

Chronic kidney disease (CKD) patients have high risks of end-stage kidney disease (ESKD) and pre-ESKD death. Therefore, accurately predicting these outcomes is useful among CKD patients, especially in those who are at high risk. Thus, we evaluated whether a machine-learning system can predict accurately these risks in CKD patients and attempted its application by developing a Web-based risk-prediction system. We developed 16 risk-prediction machine-learning models using Random Forest (RF), Gradient Boosting Decision Tree, and eXtreme Gradient Boosting with 22 variables or selected variables for the prediction of the primary outcome (ESKD or death) on the basis of repeatedly measured data of CKD patients (n = 3,714; repeatedly measured data, n = 66,981) in their electronic-medical records. The performances of the models were evaluated using data from a cohort study of CKD patients carried out over 3 years (n = 26,906). One RF model with 22 variables and another RF model with 8 variables of time-series data showed high accuracies of the prediction of the outcomes and were selected for use in a risk-prediction system. In the validation, the 22- and 8-variable RF models showed high C-statistics for the prediction of the outcomes: 0.932 (95% CI 0.916, 0.948) and 0.93 (0.915, 0.945), respectively. Cox proportional hazards models using splines showed a highly significant relationship between the high probability and high risk of an outcome (p<0.0001). Moreover, the risks of patients with high probabilities were higher than those with low probabilities: 22-variable model, hazard ratio of 104.9 (95% CI 70.81, 155.3); 8-variable model, 90.9 (95% CI 62.29, 132.7). Then, a Web-based risk-prediction system was actually developed for the implementation of the models in clinical practice. This study showed that a machine-learning-based Web system is a useful tool for the risk prediction and treatment of CKD patients.

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

Background:

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

Objective:

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

Design:

Retrospective cohort study

Setting:

Single center multidisciplinary CKD clinic in Ontario, Canada

Patients:

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

Measurements:

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

Methods:

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

Results:

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

Limitations:

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

Conclusions:

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

External validation of six clinical models for prediction of chronic kidney disease in a German population

Background

Chronic kidney disease (CKD) is responsible for large personal health and societal burdens. Screening populations at higher risk for CKD is effective to initiate earlier treatment and decelerate disease progress. We externally validated clinical prediction models for unknown CKD that might be used in population screening.

Methods

We validated six risk models for prediction of CKD using only non-invasive parameters. Validation data came from 4,185 participants of the German Heinz-Nixdorf-Recall study (HNR), drawn in 2000 from a general population aged 45–75 years. We estimated discrimination and calibration using the full model information, and calculated the diagnostic properties applying the published scoring algorithms of the models using various thresholds for the sum of scores.

Results

The risk models used four to nine parameters. Age and hypertension were included in all models. Five out of six c-values ranged from 0.71 to 0.73, indicating fair discrimination. Positive predictive values ranged from 15 to 19%, negative predictive values were > 93% using score thresholds that resulted in values for sensitivity and specificity above 60%.

Conclusions

Most of the selected CKD prediction models show fair discrimination in a German general population. The estimated diagnostic properties indicate that the models are suitable for identifying persons at higher risk for unknown CKD without invasive procedures.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12882-022-02899-0.

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.

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

Rationale & Objective

Study Design

Setting & Participants

Outcomes & Analytical Approach

Results

Limitations

Conclusions

Prediction of End-Stage Kidney Disease Using Estimated Glomerular Filtration Rate With and Without Race : A Prospective Cohort Study

BACKGROUND

New estimated glomerular filtration rate (eGFR) equations removed race adjustment, but the impact of its removal on prediction of end-stage kidney disease (ESKD) is unknown.

OBJECTIVE

To compare the ESKD prediction performance of different eGFR equations.

DESIGN

Observational, prospective cohort study.

SETTING

7 U.S. clinical centers.

PARTICIPANTS

3873 participants with chronic kidney disease (CKD) from the CRIC (Chronic Renal Insufficiency Cohort) Study contributing 13 902 two-year risk periods.

MEASUREMENTS

ESKD was defined as initiation of dialysis or transplantation. eGFR was calculated using 5 Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations based on serum creatinine and/or cystatin C, with or without race adjustment. The predicted 2-year risk for ESKD was calculated using the 4-variable Kidney Failure Risk Equation (KFRE). We evaluated the prediction performance of eGFR equations and the KFRE score using discrimination and calibration analyses.

RESULTS

During a maximum 16 years of follow-up, 856 participants developed ESKD. Across all eGFR equations, the KFRE score was superior for predicting 2-year incidence of ESKD compared with eGFR alone (area under the curve ranges, 0.945 to 0.954 vs. 0.900 to 0.927). Prediction performance of KFRE scores using different eGFR equations was similar, but the creatinine equation without race adjustment improved calibration among Black participants. Among all participants, compared with an eGFR less than 20 mL/min/1.73 m2, a KFRE score greater than 20% had similar specificity for predicting 2-year ESKD risk (ranges, 0.94 to 0.97 vs. 0.95 to 0.98) but higher sensitivity (ranges, 0.68 to 0.78 vs. 0.42 to 0.66).

LIMITATION

Data are solely from the United States.

CONCLUSION

The KFRE score better predicts 2-year risk for ESKD compared with eGFR alone, regardless of race adjustment. The creatinine equation with age and sex may improve calibration among Black patients. A KFRE score greater than 20% showed high specificity and sensitivity for predicting 2-year risk for ESKD.

PRIMARY FUNDING SOURCE

National Institutes of Health.

Referral patterns, disease progression and impact of the kidney failure risk equation (KFRE) in a Queensland Chronic Kidney Disease Registry (CKD.QLD) cohort: a study protocol

INTRODUCTION

Chronic kidney disease (CKD) is a rapidly increasing and global phenomenon which carries high morbidity and mortality. Although timely referral from primary care to secondary care confers favourable outcomes, it is not possible for every patient with CKD to be managed at secondary care. With 1 in 10 Australians currently living with markers of CKD against a workforce of about 600 nephrology specialists, a risk stratification strategy is required that will reliably identify individuals whose kidney disease is likely to progress.

METHODS AND ANALYSIS

This study will undertake a retrospective secondary analysis of the Chronic Kidney Disease Queensland Registry (CKD.QLD) data of consented adults to examine the referral patterns to specialist nephrology services from primary care providers and map the patient trajectory and outcomes to inform the optimal referral timing for disease mitigation. Patient data over a 5-year period will be examined to determine the impact of the kidney failure risk equation-based risk stratification on the referral patterns, disease progression and patient outcomes. The results will inform considerations of a risk stratification strategy that will ensure adequate predialysis management and add to the discussion of the time interval between referral and initiation of kidney replacement therapy or development of cardiovascular events.

ETHICS AND DISSEMINATION

This protocol was approved by the Ethics Committee of the Royal Brisbane and Women's Hospital in January 2021 (LNR/2020/QRBW/69707 14/01/2021). The HREC waived the requirement for patient consent as all patients had consented for the use of their data for the purpose of research on recruitment into CKD.QLD Registry. The results will be presented as a component of a PhD study with The University of Queensland. It is anticipated that the results will be presented at health-related conferences (local, national and possibly international) and via publication in peer-reviewed academic journals.

Evaluation of a predictive model of end-stage kidney disease in a French-based cohort

BACKGROUND

The risk of ESKD is highly heterogeneous among renal diseases, and risk scores were developed to account for multiple progression factors. Kidney failure risk equation (KFRE) is the most widely accepted, although external validation is scarce. The objective of this study was to evaluate the usefulness of this score in a French case-control cohort and test the pertinence of the proposed thresholds.

METHODS

A retrospective case-control study comparing a group of patients starting renal replacement therapy (RRT) to a group of patients with CKD stages 3-5. Multivariate analysis to assess the predictors of ESKD risk. Discrimination of 4-, 6- and 8-variable scores using ROC curves and compared with eGFR alone and albumin/creatinine ratio (ACR) alone.

RESULTS

314 patients with a ratio of 1 case for 1 control. In multivariate analysis, increasing age and higher eGFR were associated with a lower risk of ESKD (OR 0.62, 95% CI 0.48-0.79; and OR 0.72, 95% CI 0.59-0.86, respectively). The log-transformed ACR was associated with a higher risk of ESKD (OR 1.25 per log unit, 95% CI 1.02-1.55). The 4-variable score was significantly higher in the RRT group than in the CKD-ND group, and was more efficient than the eGFR (AUROC 0.66, 95% CI 0.60-0.72, p = 0.018) and the log-transformed ACR (AUROC 0.63 95% CI 0.60-0.72, p = 0.0087) to predict ESKD. The 6-variable score including BP metrics and diabetes was not more discriminant as the 4-variable score. The 8-variable score had similar performance compared with the 4-score (AUROC 8-variable score: 0.70, 95% CI 0.64-0.76, p = 0.526). A 40% and 20% score thresholds were not superior to eGFR < 15 and 20 mL/min/1.73 m², respectively. A 10% threshold was more specific than an eGFR < 30 mL/min/1.73 m².

CONCLUSION

KFRE was highly discriminant between patients progressing to ESKD vs those non-progressing. The 4-variable score may help stratify renal risk and referral in the numerous patients with stage 3 CKD. Conversely, the proposed thresholds for creating vascular access or preemptive transplantation were not superior to eGFR alone.

The Kidney Failure Risk Equation Score and CKD Care Delivery Measures: A Cross-sectional Study

RATIONALE & OBJECTIVE

The 4-variable kidney failure risk equation (KFRE) allows for the prediction of chronic kidney disease (CKD) progression using age, sex, estimated glomerular filtration rate, and urine albumin/creatinine ratio. Electronic health records enable KFRE auto-calculation, and registries allow population-level application. We assessed whether 2-year KFRE score categories are associated with CKD care metrics.

STUDY DESIGN

Cross-sectional cohort.

SETTING & PARTICIPANTS

This study included individuals with CKD in March 2020 who were receiving care within the Partners HealthCare system in Massachusetts.

OUTCOMES

The presence of sufficient data to calculate the KFRE and, among those with a KFRE score, performance on CKD clinical care metrics, including (1) prescription of angiotensin-converting enzyme inhibitor or angiotensin receptor blocker; (2) blood pressure at goal (<140/90 mm Hg) based on clinic measurements; (3) composite metric of hepatitis B virus immunity; (4) composite metric of referral, evaluation, or waitlist status for kidney transplantation; (5) advance directive documentation; (6) yearly influenza vaccination; and (7) pneumonia vaccination.

ANALYTICAL APPROACH

Multivariable logistic regression analysis was used to analyze the association of KFRE score category with CKD care metrics.

RESULTS

Of 61,546 patients, 18,272 (30%) had auto-calculated 2-year KFRE scores; the remaining patients lacked KFRE scores because of absent albuminuria assessment. Individuals with a KFRE score were more likely to have a primary care provider or nephrologist. Among patients with 2-year KFRE scores, high-risk patients had increased odds of completing advance directives (OR, 1.52; 95% CI, 1.07-2.17), while low-risk patients had decreased odds of influenza vaccination (OR, 0.85; 95% CI, 0.75-0.97). Patients with moderate- and high-risk KFRE scores had lower odds of having blood pressure at goal (OR, 0.77; 95% CI, 0.61-0.96 and OR, 0.63; 95% CI, 0.44-0.88, respectively).

LIMITATIONS

Albuminuria data may have been assessed outside of the Partners system.

CONCLUSIONS

A higher-risk KFRE score is associated with the delivery of some but not all CKD care measures. An opportunity exists to improve albuminuria measurement.

National Estimates of CKD Prevalence and Potential Impact of Estimating Glomerular Filtration Rate Without Race

BACKGROUND

The implications of removing the adjustment for Black race in equations to eGFR on the prevalence of CKD and management strategies are incompletely understood.

METHODS

We estimated changes in CKD prevalence and the potential effect on therapeutic drug prescriptions and prediction of kidney failure if race adjustment were removed from the CKD-EPI GFR estimating equation. We used cross-sectional and longitudinal data from adults aged ≥18 years in the National Health and Nutrition Examination Survey (NHANES) from 2015 to 2016, and the Veterans Affairs (VA) Health Care System in 2015. In the VA cohort, we assessed use of common medications that require dose adjustment on the basis of kidney function, and compared the prognostic accuracy of the Kidney Failure Risk Equation with versus without race adjustment of eGFR.

RESULTS

The prevalence of CKD among Black adults increased from 5.2% to 10.6% in NHANES, and from 12.4% to 21.6% in the VA cohort after eliminating race adjustment. Among Black veterans, 41.0% of gabapentin users, 33.5% of ciprofloxacin users, 24.0% of metformin users, 6.9% of atenolol users, 6.6% of rosuvastatin users, and 5.8% of tramadol users were reclassified to a lower eGFR for which dose adjustment or discontinuation is recommended. Without race adjustment of eGFR, discrimination of the Kidney Failure Risk Equation among Black adults remained high and calibration was marginally improved overall, with better calibration at higher levels of predicted risk.

CONCLUSIONS

Removal of race adjustment from CKD-EPI eGFR would double the estimated prevalence of CKD among Black adults in the United States. Such a change is likely to affect a sizeable number of drug-dosing decisions. It may also improve the accuracy of kidney failure risk prediction among higher-risk Black adults.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Kidney Failure Prediction Models: A Comprehensive External Validation Study in Patients with Advanced CKD

BACKGROUND

Various prediction models have been developed to predict the risk of kidney failure in patients with CKD. However, guideline-recommended models have yet to be compared head to head, their validation in patients with advanced CKD is lacking, and most do not account for competing risks.

METHODS

To externally validate 11 existing models of kidney failure, taking the competing risk of death into account, we included patients with advanced CKD from two large cohorts: the European Quality Study (EQUAL), an ongoing European prospective, multicenter cohort study of older patients with advanced CKD, and the Swedish Renal Registry (SRR), an ongoing registry of nephrology-referred patients with CKD in Sweden. The outcome of the models was kidney failure (defined as RRT-treated ESKD). We assessed model performance with discrimination and calibration.

RESULTS

The study included 1580 patients from EQUAL and 13,489 patients from SRR. The average c statistic over the 11 validated models was 0.74 in EQUAL and 0.80 in SRR, compared with 0.89 in previous validations. Most models with longer prediction horizons overestimated the risk of kidney failure considerably. The 5-year Kidney Failure Risk Equation (KFRE) overpredicted risk by 10%-18%. The four- and eight-variable 2-year KFRE and the 4-year Grams model showed excellent calibration and good discrimination in both cohorts.

CONCLUSIONS

Some existing models can accurately predict kidney failure in patients with advanced CKD. KFRE performed well for a shorter time frame (2 years), despite not accounting for competing events. Models predicting over a longer time frame (5 years) overestimated risk because of the competing risk of death. The Grams model, which accounts for the latter, is suitable for longer-term predictions (4 years).

Dynamic prediction models for graft failure in paediatric kidney transplantation

BACKGROUND

Several models have been proposed to predict kidney graft failure in adult recipients but none in younger recipients. Our objective was to propose a dynamic prediction model for graft failure in young kidney transplant recipients.

METHODS

We included 793 kidney transplant recipients waitlisted before the age of 18 years who received a first kidney transplantation before the age of 21 years in France in 2002-13 and survived >90 days with a functioning graft. We used a Cox model including baseline predictors only (sex, age at transplant, primary kidney disease, dialysis duration, donor type and age, human leucocyte antigen matching, cytomegalovirus serostatus, cold ischaemia time and delayed graft function) and two joint models also accounting for post-transplant estimated glomerular filtration rate (eGFR) trajectory. Predictive performances were evaluated using a cross-validated area under the curve (AUC) and R2 curves.

RESULTS

When predicting the risk of graft failure from any time within the first 7 years after paediatric kidney transplantation, the predictions for the following 3 or 5 years were accurate and much better with the joint models than with the Cox model (AUC ranged from 0.83 to 0.91 for the joint models versus 0.56 to 0.64 for the Cox model).

CONCLUSION

Accounting for post-transplant eGFR trajectory strongly increased the accuracy of graft failure prediction in young kidney transplant recipients.

Predicting kidney failure risk after acute kidney injury among people receiving nephrology clinic care

Background

Outcomes after acute kidney injury (AKI) are well described, but not for those already under nephrology clinic care. This is where discussions about kidney failure risk are commonplace. We evaluated whether the established kidney failure risk equation (KFRE) should account for previous AKI episodes when used in this setting.

Methods

This observational cohort study included 7491 people referred for nephrology clinic care in British Columbia in 2003–09 followed to 2016. Predictors were previous Kidney Disease: Improving Global Outcomes–based AKI, age, sex, proteinuria, estimated glomerular filtration rate (eGFR) and renal diagnosis. Outcomes were 5-year kidney failure and death. We developed cause-specific Cox models (AKI versus no AKI) for kidney failure and death, stratified by eGFR (</≥30 mL/min/1.73 m²). We also compared prediction models comparing the 5-year KFRE with two refitted models, one with and one without AKI as a predictor.

Results

AKI was associated with increased kidney failure (33.1% versus 26.3%) and death (23.8% versus 16.8%) (P  < 0.001). In Cox models, AKI was independently associated with increased kidney failure in those with an eGFR ≥30 mL/min/1.73 m² {hazard ratio [HR] 1.35 [95% confidence interval (CI) 1.07–1.70]}, no increase in those with eGFR <30 mL/min/1.73 m² ([HR 1.05 95% CI 0.91–1.21)] and increased mortality in both subgroups [respective HRs 1.89 (95% CI 1.56–2.30) and 1.43 (1.16–1.75)]. Incorporating AKI into a refitted kidney failure prediction model did not improve predictions on comparison of receiver operating characteristics (P = 0.16) or decision curve analysis. The original KFRE calibrated poorly in this setting, underpredicting risk.

Conclusions

AKI carries a poorer long-term prognosis among those already under nephrology care. AKI may not alter kidney failure risk predictions, but the use of prediction models without appreciating the full impact of AKI, including increased mortality, would be simplistic. People with kidney diseases have risks beyond simply kidney failure. This complexity and variability of outcomes of individuals is important.

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.

CKD Awareness Among US Adults by Future Risk of Kidney Failure

RATIONALE & OBJECTIVE

Persons with chronic kidney disease (CKD) are often unaware of their disease status. Efforts to improve CKD awareness may be most effective if focused on persons at highest risk for progression to kidney failure.

STUDY DESIGN

Serial cross-sectional surveys.

SETTING & PARTICIPANTS

Nonpregnant adults (aged≥20 years) with CKD glomerular filtration rate categories 3-4 (G3-G4) who participated in the National Health and Nutrition Examination Survey from 1999 to 2016 (n = 3,713).

PREDICTOR

5-year kidney failure risk, estimated using the Kidney Failure Risk Equation. Predicted risk was categorized as minimal (<2%), low (2%-<5%), intermediate (5%-<15%), or high (≥15%).

OUTCOME

CKD awareness, defined by answering "yes" to the question "Have you ever been told by a doctor or other health professional that you had weak or failing kidneys?"

ANALYTICAL APPROACH

Prevalence of CKD awareness was estimated within each risk group using complex sample survey methods. Associations between Kidney Failure Risk Equation risk and CKD awareness were assessed using multivariable logistic regression. CKD awareness was compared with awareness of hypertension and diabetes during the same period.

RESULTS

In 2011 to 2016, unadjusted CKD awareness was 9.6%, 22.6%, 44.7%, and 49.0% in the minimal-, low-, intermediate-, and high-risk groups, respectively. In adjusted analyses, these proportions did not change over time. Awareness of CKD, including among the highest risk group, remains consistently below that of hypertension and diabetes and awareness of these conditions increased over time.

LIMITATIONS

Imperfect sensitivity of the "weak or failing kidneys" question for ascertaining CKD awareness.

CONCLUSIONS

Among adults with CKD G3-G4 who have 5-year estimated risks for kidney failure of 5%-<15% and≥15%, approximately half were unaware of their kidney disease, a gap that has persisted nearly 2 decades.

The transition clinic in chronic kidney disease care

People with advanced chronic kidney disease and evidence of progression have a high risk of renal replacement therapy. Specialized transition clinics could offer a better option for preparing these patients for dialysis, transplantation or conservative care. This review focuses on the different aspects of such transition clinics. We discuss which patients should be referred to these units and when referral should take place. Patient involvement in the decision-making process is important and requires unbiased patient education. There are many themes, both patient-centred and within the healthcare structure, that will influence the process of shared decision-making and the modality choice. Aspects of placing an access for haemodialysis and peritoneal dialysis are reviewed. Finally, we discuss the importance of pre-emptive transplantation and a planned dialysis start, all with a focus on multidisciplinary collaboration at the transition clinic.

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

BACKGROUND

Patients with chronic kidney disease (CKD) are at high risk of end-stage kidney disease (ESKD). The Kidney Failure Risk Equation (KFRE), which predicts ESKD risk among patients with CKD, has not been validated in primary care clinics in Southeast Asia (SEA). Therefore, we aimed to (1) evaluate the performance of existing KFRE equations, (2) recalibrate KFRE for better predictive precision, and (3) identify optimally feasible KFRE thresholds for nephrologist referral and dialysis planning in SEA.

METHODS

All patients with CKD visiting nine primary care clinics from 2010 to 2013 in Singapore were included and applied 4-variable KFRE equations incorporating age, sex, estimated glomerular filtration rate (eGFR), and albumin-to-creatinine ratio (ACR). ESKD onset within two and five years were acquired via linkage to the Singapore Renal Registry. A weighted Brier score (the squared difference between observed vs predicted ESKD risks), bias (the median difference between observed vs predicted ESKD risks) and precision (the interquartile range of the bias) were used to select the best-calibrated KFRE equation.

RESULTS

The recalibrated KFRE (named Recalibrated Pooled KFRE SEA) performed better than existing and other recalibrated KFRE equations in terms of having a smaller Brier score (square root: 2.8% vs. 4.0-9.3% at 5 years; 2.0% vs. 6.1-9.1% at 2 years), less bias (2.5% vs. 3.3-5.2% at 5 years; 1.8% vs. 3.2-3.6% at 2 years), and improved precision (0.5% vs. 1.7-5.2% at 5 years; 0.5% vs. 3.8-4.2% at 2 years). Area under ROC curve for the Recalibrated Pooled KFRE SEA equations were 0.94 (95% confidence interval [CI]: 0.93 to 0.95) at 5 years and 0.96 (95% CI: 0.95 to 0.97) at 2 years. The optimally feasible KFRE thresholds were > 10-16% for 5-year nephrologist referral and > 45% for 2-year dialysis planning. Using the Recalibrated Pooled KFRE SEA, an estimated 82 and 89% ESKD events were included among 10% of subjects at highest estimated risk of ESKD at 5-year and 2-year, respectively.

CONCLUSIONS

The Recalibrated Pooled KFRE SEA performs better than existing KFREs and warrants implementation in primary care settings in SEA.

Economic Modelling of Chronic Kidney Disease: A Systematic Literature Review to Inform Conceptual Model Design

BACKGROUND

Chronic kidney disease (CKD) is a progressive condition that leads to irreversible damage to the kidneys and is associated with an increased incidence of cardiovascular events and mortality. As novel interventions become available, estimates of economic and clinical outcomes are needed to guide payer reimbursement decisions.

OBJECTIVE

The aim of the present study was to systematically review published economic models that simulated long-term outcomes of kidney disease to inform cost-effectiveness evaluations of CKD treatments.

METHODS

The review was conducted across four databases (MEDLINE, Embase, the Cochrane library and EconLit) and health technology assessment agency websites. Relevant information on each model was extracted. Transition and mortality rates were also extracted to assess the choice of model parameterisation on disease progression by simulating patient's time with end-stage renal disease (ESRD) and time to ESRD/death. The incorporation of cardiovascular disease in a population with CKD was qualitatively assessed across identified models.

RESULTS

The search identified 101 models that met the criteria for inclusion. Models were classified into CKD models (n = 13), diabetes models with nephropathy (n = 48), ESRD-only models (n = 33) and cardiovascular models with CKD components (n = 7). Typically, published models utilised frameworks based on either (estimated or measured) glomerular filtration rate (GFR) or albuminuria, in line with clinical guideline recommendations for the diagnosis and monitoring of CKD. Generally, two core structures were identified, either a microsimulation model involving albuminuria or a Markov model utilising CKD stages and a linear GFR decline (although further variations on these model structures were also identified). Analysis of parameter variability in CKD disease progression suggested that mean time to ESRD/death was relatively consistent across model types (CKD models 28.2 years; diabetes models with nephropathy 24.6 years). When evaluating time with ESRD, CKD models predicted extended ESRD survival over diabetes models with nephropathy (mean time with ESRD 8.0 vs. 3.8 years).

DISCUSSION

This review provides an overview of how CKD is typically modelled. While common frameworks were identified, model structure varied, and no single model type was used for the modelling of patients with CKD. In addition, many of the current methods did not explicitly consider patient heterogeneity or underlying disease aetiology, except for diabetes. However, the variability of individual patients' GFR and albuminuria trajectories perhaps provides rationale for a model structure designed around the prediction of individual patients' GFR trajectories. Frameworks of future CKD models should be informed and justified based on clinical rationale and availability of data to ensure validity of model results. In addition, further clinical and observational research is warranted to provide a better understanding of prognostic factors and data sources to improve economic modelling accuracy in CKD.

Patient and provider experience and perspectives of a risk-based approach to multidisciplinary chronic kidney disease care: a mixed methods study

BACKGROUND

The Kidney Failure Risk Equation (KFRE) predicts risk of progression to kidney failure and is used to guide clinical decisions for patients with chronic kidney disease (CKD).

METHODS

The KFRE was implemented to guide access to multidisciplinary care for CKD patients in Alberta, Canada, based on their 2-year risk of kidney failure. We used a mixed methods approach to investigate patients' and providers' perspectives and experiences 1 year following KFRE implementation. We conducted post-implementation interviews with multidisciplinary clinic providers and with low-risk patients who transitioned from multidisciplinary to general nephrology care. We also administered pre- and post-implementation patient care experience surveys, targeting both low-risk patients discharged to general nephrology and high-risk patients who remained in the multidisciplinary clinic, and provider job satisfaction surveys.

RESULTS

Twenty-seven interviews were conducted (9 patients, 1 family member, 17 providers). Five categories were identified among patients and providers: targeted care; access to resources outside the multidisciplinary clinics; self-efficacy; patient reassurance and reduced stress; and transition process for low-risk patients Two additional categories were identified among providers only: anticipated concerns and job satisfaction. Patients and providers reported that the risk-based approach allowed the clinic to target care to those most likely to experience kidney failure and most likely to benefit from multidisciplinary care. While some participants indicated the risk-based model enhanced the sustainability of the clinics, others expressed concern that care for low-risk patients discharged from multidisciplinary care, or those now considered ineligible, may be inadequate. Overall, 413 patients completed the care experience survey and 73 providers completed the workplace satisfaction survey. The majority of patients were satisfied with their care in both periods with no overall differences. When considering the responses "Always" and "Often" together versus not, there were statistically significant improvements in domains of access to care, caring staff, and safety of care. There were no differences in healthcare providers' job satisfaction following KFRE implementation.

CONCLUSIONS

Patients and healthcare providers reported that the risk-based approach improved the focus of the multidisciplinary CKD clinics by targeting patients at highest risk, with survey results suggesting no difference in patient care experience or healthcare provider job satisfaction.

Perceived Benefits and Challenges of a Risk-Based Approach to Multidisciplinary Chronic Kidney Disease Care: A Qualitative Descriptive Study

Background:

The kidney failure risk equation (KFRE) provides an estimate of risk of progression to kidney failure, and may guide clinical care.

Objective:

We aimed to describe patient, family, and health care provider’s perspectives of the perceived benefits and challenges of using a risk-based approach to guide care delivery for patients with advanced chronic kidney disease (CKD), and refine implementation based on their input.

Methods:

We used qualitative methodology to explore perceived benefits and challenges of implementing a risk-based approach (using the KFRE) to determine eligibility for multidisciplinary CKD care in Southern Alberta. We obtained perspectives from patients and families through focus groups, as well as input from health care providers through interviews and open-ended responses from an online survey. Twelve patients/family members participated in 2 focus groups, 16 health care providers participated in an interview, and 40 health care providers responded to the survey.

Results:

Overall, participants felt that a KFRE-based approach had the potential to improve efficiency of the clinics by targeting care to patients at highest risk of kidney failure; however, they also expressed concerns about the impact of loss of services for lower risk individuals. Participants also articulated concerns about a perceived lack of capacity for adequate CKD patient care in the community. Our implementation strategy was modified as a result of participants’ feedback.

Conclusions:

We identified benefits and challenges to implementation of a risk-based approach to guide care of patients with advanced CKD. Based on these results, our implementation strategy has been modified by removing the category of referral back to primary care alone, and instead having that decision made jointly by nephrologists and patients among low-risk patients.