Dataset Information

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

ABSTRACT:

Introduction

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

Methods

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

Results

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

Conclusion

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

SUBMITTER: Hundemer GL

PROVIDER: S-EPMC8640561 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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Publications

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

Hundemer Gregory L GL Tangri Navdeep N Sood Manish M MM Clark Edward G EG Canney Mark M Edwards Cedric C White Christine A CA Oliver Matthew J MJ Ramsay Tim T Akbari Ayub A

Kidney international reports 20211008 12

<h4>Introduction</h4>The Kidney Failure Risk Equation (KFRE) is a clinical tool widely used to predict progression from chronic kidney disease (CKD) to kidney failure. This study aimed to evaluate the effect of age on KFRE performance in advanced CKD.<h4>Methods</h4>We conducted a retrospective cohort study among 1701 consecutive patients referred to an advanced CKD clinic in Ottawa, Canada, between 2010 and 2018. Patients were categorized by age as follows: <60, 60 to 69, 70 to 79, and ≥80 year ...[more]

PMID: 34901569

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Project description:Background and objectivesThe kidney failure risk equation is a clinical tool commonly used for prediction of progression from CKD to kidney failure. The kidney failure risk equation's accuracy in advanced CKD and whether this varies by CKD etiology remains unknown. This study examined the kidney failure risk equation's discrimination and calibration at 2 and 5 years among a large tertiary care population with advanced CKD from heterogeneous etiologies.Design, setting, participants, & measurementsThis retrospective cohort study included 1293 patients with advanced CKD (median eGFR 15 ml/min per 1.73 m2) referred to the Ottawa Hospital Multi-Care Kidney Clinic between 2010 and 2016, with follow-up clinical data available through 2018. Four-variable kidney failure risk equation scores for 2- and 5-year risks of progression to kidney failure (defined as dialysis or kidney transplantation) were calculated upon initial referral and correlated with the subsequent observed kidney failure incidence within these time frames. Receiver operating characteristic curves and calibration plots were used to measure the discrimination and calibration of the kidney failure risk equation both in the overall advanced CKD population and by CKD etiology: diabetic kidney disease, hypertensive nephrosclerosis, GN, polycystic kidney disease, and other. Pairwise comparisons of the receiver operating characteristic curves by CKD etiology were performed to compare kidney failure risk equation discrimination.ResultsThe kidney failure risk equation provided adequate to excellent discrimination in identifying patients with CKD likely to progress to kidney failure at the 2- and 5-year time points both overall (2-year area under the curve, 0.83; 95% confidence interval, 0.81 to 0.85; 5-year area under the curve, 0.81; 95% confidence interval, 0.77 to 0.84) and across CKD etiologies. The kidney failure risk equation displayed adequate calibration at the 2- and 5-year time points both overall and across CKD etiologies (Hosmer-Lemeshow P≥0.05); however, the predicted risks of kidney failure were higher than the observed risks across CKD etiologies with the exception of polycystic kidney disease.ConclusionsThe kidney failure risk equation provides adequate discrimination and calibration in advanced CKD and across CKD etiologies.

Project description:Rationale & objectiveThe 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 designCross-sectional cohort.Setting & participantsThis study included individuals with CKD in March 2020 who were receiving care within the Partners HealthCare system in Massachusetts.OutcomesThe 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 approachMultivariable logistic regression analysis was used to analyze the association of KFRE score category with CKD care metrics.ResultsOf 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).LimitationsAlbuminuria data may have been assessed outside of the Partners system.ConclusionsA higher-risk KFRE score is associated with the delivery of some but not all CKD care measures. An opportunity exists to improve albuminuria measurement.

Project description:The Kidney Failure Risk Equation (KFRE) predicts the need for dialysis or transplantation using age, sex, estimated glomerular filtration rate (eGFR), and urine albumin to creatinine ratio (ACR). The eGFR and ACR have known biological and analytical variability. We examined the effect of biological and analytical variability of eGFR and ACR on the 2-year KFRE predicted kidney failure probabilities using single measure and the average of repeat measures of simulated eGFR and ACR. Previously reported values for coefficient of variation (CV) for ACR and eGFR were used to calculate day to day variability. Variation was also examined with outpatient laboratory data from patients with an eGFR between 15 and 50 mL/min/1.72 m2. A web application was developed to calculate and model day to day variation in risk. The biological and analytical variability related to ACR and eGFR lead to variation in the predicted probability of kidney failure. A male patient age 50, ACR 30 mg/mmol and eGFR 25, had a day to day variation in risk of 7% (KFRE point estimate: 17%, variability range 14% to 21%). The addition of inter laboratory variation due to different instrumentation increased the variability to 9% (KFRE point estimate 17%, variability range 13% to 22%). Averaging of repeated measures of eGFR and ACR significantly decreased the variability (KFRE point estimate 17%, variability range 15% to 19%). These findings were consistent when using outpatient laboratory data which showed that most patients had a KFRE 2-year risk variability of ≤ 5% (79% of patients). Approximately 13% of patients had variability from 5-10% and 8% had variability > 10%. The mean age (SD) of this cohort was 64 (15) years, 36% were females, the mean (SD) eGFR was 32 (10) ml/min/1.73m2 and median (IQR) ACR was 22.7 (110). Biological and analytical variation intrinsic to the eGFR and ACR may lead to a substantial degree of variability that decreases with repeat measures. Use of a web application may help physicians and patients understand individual patient's risk variability and communicate risk (https://mccudden.shinyapps.io/kfre_app/). The web application allows the user to alter age, gender, eGFR, ACR, CV (for both eGFR and ACR) as well as units of measurements for ACR (g/mol versus mg/g).

Project description:BackgroundPredicting allograft failure in kidney transplant recipients can help plan renal replacement therapy and guide patient-provider communication. The kidney failure risk equation (KFRE) accurately predicts the need for dialysis in patients with chronic kidney disease (CKD), but has not been validated in kidney transplant recipients.ObjectiveWe sought to validate the 4-variable KFRE (age, sex, estimated glomerular filtration rate [eGFR], and urine albumin-to-creatinine ratio [ACR]) for prediction of 2- and 5-year death-censored allograft failure.DesignRetrospective cohort study.SettingFour independent North American Cohorts from Ontario, Canada; Alberta, Canada; Manitoba, Canada; and Wisconsin, United States, between January 1999 and December 2017.PatientsAdult kidney transplant patients at 1-year posttransplantation.MeasurementsKidney failure risk as measured by the KFRE (eGFR, urine ACR, age, and sex).MethodsWe included all adult patients who had at least 1 serum creatinine and at least 1 urine ACR measurement approximately 1 year following kidney transplantation. The performance of the KFRE was evaluated using the area under the receiver operating characteristic curve (C-statistic). C-statistics from the 4 cohorts were meta-analyzed using random-effects models.ResultsA total of 3659 patients were included. Pooled C-statistics were good in the entire population, at 0.81 (95% confidence interval: 0.72-0.91) for the 2-year KFRE and 0.73 (0.67-0.80) for the 5-year KFRE. Discrimination improved among patients with poorer kidney function (eGFR < 45 mL/min/1.73 m2), with a C-statistic of 0.88 (0.78-0.98) for the 2-year KFRE and 0.83 (0.74-0.91) for the 5-year KFRE.LimitationsThe KFRE does not predict episodes of acute rejection and there was heterogeneity between cohorts.ConclusionsThe KFRE accurately predicts kidney failure in kidney transplant recipients at 1-year posttransplantation. Further validation in larger cohorts with longer follow-up times can strengthen the case for clinical implementation.

Project description:BackgroundThe 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.MethodsData 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.ResultsThe 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.ConclusionsThe 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.

Project description:Rationale & objectiveThe use of renin-angiotensin system (RAS) inhibitors is standard of care in people with early to moderate chronic kidney disease (CKD). Less is known regarding the efficacy of RAS inhibitors in very advanced CKD. In this study, we describe patterns of use of RAS inhibitors and associations of these patterns of use with risk for CKD progression and mortality in patients with advanced CKD.Study designPropensity-matched cohort study.Settings & participantsWe identified 678 participants who were enrolled in the multicenter Chronic Renal Insufficiency Cohort (CRIC) Study with estimated glomerular filtration rates (eGFRs) < 30 mL/min/1.73 m2 at the baseline visit.ExposureUse of RAS inhibitors within the first year after the baseline visit, characterized by 4 patterns of use: never users, always users, dynamic users, and new users.OutcomesProgression to end-stage renal disease (ESRD) and all-cause mortality.Analytical approachWe generated propensity scores and matched participants in the always users group with a 1:1 ratio with a participant from the other 3 groups, matching by age, sex, race, diabetes, hypertension, systolic blood pressure, eGFR, urinary protein-creatinine ratio, and serum potassium level. Cox models were used to test the association of patterns of RAS inhibitor use with risk for kidney failure and death.ResultsOf the 678 participants with eGFRs < 30 mL/min/1.73 m2, 57% were identified as always users of RAS inhibitors during the 1 year, 23% as never users, 13% as dynamic users, and 7% as new users. We found no differences in risk for ESRD across patterns of RAS inhibitor use (never users [HR, 1.09; 95% CI, 0.71-1.67], dynamic users [HR, 1.46; 95% CI, 0.83-2.55], new users [HR, 0.78; 95% CI, 0.33-1.84] vs the always users reference group). Similarly, there was no association of patterns of RAS inhibitor use with death (never users [HR, 1.02; CI, 0.74-1.40], dynamic users [HR, 1.23; 95% CI, 0.80-1.90], new users [HR, 1.10; 95% CI, 0.63-1.92] vs always users).LimitationsObservational study.ConclusionsUse of RAS inhibitors in patients with eGFRs < 30 mL/min/1.73 m2 is heterogeneous..We found no difference in risk for progression to ESRD or mortality across patterns of RAS inhibitor use. Further research is required to identify optimal prescribing strategies of RAS inhibitors during advanced stages of CKD.

Dataset Information

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

Introduction

Methods

Results

Conclusion

Publications

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

Similar Datasets

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