Value of adding the renal pathological score to the kidney failure risk equation in advanced diabetic nephropathy

Abstract Background There is emerging evidence that the 4-variable Kidney Failure Risk Equation (KFRE) can be used for risk prediction of graft failure in transplant recipients. However, geographical validation of the 4-variable KFRE in transplant patients is lacking, as is whether the more extensive 8-variable KFRE improves predictive accuracy. This study aimed to validate the 4- and 8-variable KFRE predictions of the 5-year death-censored risk of graft failure in patients in the United Kingdom. Methods A retrospective cohort study involved 415 transplant recipients who had their first renal transplant between 2003 and 2015 and were under follow-up at Salford Royal NHS Foundation Trust. The KFRE risk scores were calculated on variables taken 1-year post-transplant. The area under the receiver operating characteristic curves (AUC) and calibration plots were evaluated to determine discrimination and calibration of the 4- and 8-variable KFREs in the whole cohort as well as in a subgroup analysis of living and deceased donor recipients and in patients with an eGFR< 45 ml/min/1.73m2. Results There were 16 graft failure events (4%) in the whole cohort. The 4- and 8-variable KFREs showed good discrimination with AUC of 0.743 (95% confidence interval [CI] 0.610–0.876) and 0.751 (95% CI 0.629–0.872) respectively. In patients with an eGFR< 45 ml/min/1.73m2, the 8-variable KFRE had good discrimination with an AUC of 0.785 (95% CI 0.558–0.982) but the 4-variable provided excellent discrimination in this group with an AUC of 0.817 (0.646–0.988). Calibration plots however showed poor calibration with risk scores tending to underestimate risk of graft failure in low-risk patients and overestimate risk in high-risk patients, which was seen in the primary and subgroup analyses. Conclusions Despite adequate discrimination, the 4- and 8-variable KFREs are imprecise in predicting graft failure in transplant recipients using data 1-year post-transplant. Larger, international studies involving diverse patient populations should be considered to corroborate these findings.

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External Validation of the Kidney Failure Risk Equation and Re-Calibration with Addition of Ultrasound Parameters

Clinical Journal of the American Society of Nephrology ◽

10.2215/cjn.08110715 ◽

2016 ◽

Vol 11 (4) ◽

pp. 609-615 ◽

Cited By ~ 13

Author(s):

Claudia S. Lennartz ◽

John William Pickering ◽

Sarah Seiler-Mußler ◽

Lucie Bauer ◽

Kathrin Untersteller ◽

...

Keyword(s):

Kidney Failure ◽

External Validation ◽

Risk Equation ◽

Failure Risk ◽

Ultrasound Parameters

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

Kidney Medicine ◽

10.1016/j.xkme.2021.08.010 ◽

2021 ◽

Author(s):

Salman Ahmed ◽

Suraj Sarvode Mothi ◽

Thomas Sequist ◽

Navdeep Tangri ◽

Roaa M. Khinkar ◽

...

Keyword(s):

Kidney Failure ◽

Care Delivery ◽

Cross Sectional Study ◽

Sectional Study ◽

Cross Sectional ◽

Risk Equation ◽

Failure Risk

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Use of the Kidney Failure Risk Equation to Determine the Risk of Progression to End-stage Renal Disease in Children With Chronic Kidney Disease

JAMA Pediatrics ◽

10.1001/jamapediatrics.2017.4083 ◽

2018 ◽

Vol 172 (2) ◽

pp. 174 ◽

Cited By ~ 16

Author(s):

Erica Winnicki ◽

Charles E. McCulloch ◽

Mark M. Mitsnefes ◽

Susan L. Furth ◽

Bradley A. Warady ◽

...

Keyword(s):

Chronic Kidney Disease ◽

Kidney Disease ◽

Renal Disease ◽

Kidney Failure ◽

End Stage Renal Disease ◽

Risk Equation ◽

Failure Risk ◽

Risk Of Progression ◽

Stage Renal Disease ◽

End Stage

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Validation of the Kidney Failure Risk Equation in Manitoba

Canadian Journal of Kidney Health and Disease ◽

10.1177/2054358117705372 ◽

2017 ◽

Vol 4 ◽

pp. 205435811770537 ◽

Cited By ~ 7

Author(s):

Reid H. Whitlock ◽

Mariette Chartier ◽

Paul Komenda ◽

Jay Hingwala ◽

Claudio Rigatto ◽

...

Keyword(s):

Kidney Failure ◽

Risk Equation ◽

Failure Risk

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Performance of the Kidney Failure Risk Equation by Disease Etiology in Advanced CKD

Clinical Journal of the American Society of Nephrology ◽

10.2215/cjn.03940320 ◽

2020 ◽

Vol 15 (10) ◽

pp. 1424-1432

Author(s):

Gregory L. Hundemer ◽

Navdeep Tangri ◽

Manish M. Sood ◽

Tim Ramsay ◽

Ann Bugeja ◽

...

Keyword(s):

Kidney Disease ◽

Confidence Interval ◽

Kidney Failure ◽

Operating Characteristic ◽

Area Under The Curve ◽

Polycystic Kidney ◽

Receiver Operating Characteristic Curves ◽

Time Points ◽

Risk Equation ◽

Failure Risk

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.

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Estimating kidney failure risk using electronic medical records

Kidney360 ◽

10.34067/kid.0005592020 ◽

2021 ◽

pp. 10.34067/KID.0005592020

Author(s):

Felipe S. Naranjo ◽

Yingying Sang ◽

Shoshana H. Ballew ◽

Nikita Stempniewicz ◽

Stephan C. Dunning ◽

...

Keyword(s):

Electronic Medical Records ◽

Kidney Failure ◽

Medical Records ◽

Individual Risk ◽

Systematic Bias ◽

Risk Equation ◽

Protein Levels ◽

Urine Dipstick ◽

Urine Albumin ◽

Failure Risk

Background: The 4-variable kidney failure risk equation (KFRE) is a well-validated tool for patients with GFR <60 ml/min/1.73 m2 that incorporates age, sex, GFR, and urine albumin-to-creatinine ratio (ACR) to forecast individual risk of kidney failure. Implementing the KFRE in the electronic medical record is challenging, however, due to low ACR testing in clinical practice. The aim of this study was to determine, when ACR is missing, whether to impute ACR from PCR or dipstick protein for use in the 4-variable KFRE or to use the 3-variable KFRE that does not require ACR. Methods: Using electronic health records from OptumLabs® Data Warehouse, patients with eGFR <60 ml/min/1.73 m2 were categorized based on the availability of ACR testing within the previous 3 years. For patients missing ACR, we extracted urine protein-to-creatinine (PCR) and dipstick protein results, comparing the discrimination of the 3-variable KFRE (age, sex, GFR) with the 4-variable KFRE estimated using imputed ACR from PCR and dipstick protein levels. Results: There were 976,299 patients in 39 health care organizations; 59.0% were women, mean age was 72 years and mean eGFR was 47 ml/min/1.73m2. The proportion with ACR testing was 19.3% within the previous 3 years. An additional 1.7% had an available PCR and 36.3% had a dipstick protein; the remaining 42.8% had no form of albuminuria testing. The 4-variable KFRE had significantly better discrimination than the 3-variable KFRE among patients with ACR testing, PCR testing and urine dipstick protein levels, even with imputed ACR for the latter two groups. Calibration of the 4-variable KFRE was acceptable in each group, but the 3-variable equation showed systematic bias in the groups that lacked ACR or PCR testing. Conclusion: Implementation of the KFRE in electronic medical records should incorporate ACR even if only imputed from PCR or urine dipstick protein levels.

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

BMC Nephrology ◽

10.1186/s12882-019-1643-0 ◽

2019 ◽

Vol 20 (1) ◽

Cited By ~ 1

Author(s):

Yeli Wang ◽

Francis Ngoc Hoang Long Nguyen ◽

John C. Allen ◽

Jasmine Quan Lan Lew ◽

Ngiap Chuan Tan ◽

...

Keyword(s):

Primary Care ◽

Kidney Disease ◽

Kidney Failure ◽

Brier Score ◽

End Stage Kidney Disease ◽

Primary Care Clinics ◽

Risk Equation ◽

Failure Risk ◽

End Stage ◽

Better Than

Abstract 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.

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Rizikóbecslések előrehaladott krónikus vesebetegek és dializáltak esetén

Hypertonia és Nephrologia ◽

10.33668/hn.25.002 ◽

2021 ◽

Vol 25 (1) ◽

pp. 18-24

Author(s):

Imre Kulcsár ◽

Dalma Kulcsár

Keyword(s):

Kidney Failure ◽

Risk Equation ◽

Failure Risk ◽

French Registry

Az elmúlt évtizedben egyre több epidemiológiai tanulmány jelent meg a krónikus vesebetegségek progressziójával, illetve halálozásának kockázatával kapcsolatban. A téma jelentőségét mutatja, hogy világszerte foglalkoztatja a nefrológusokat (Kanadától Új-Zélandig) és olyan nagy nemzeti (REIN Study – French Registry) vagy nemzetközi vizsgálatoknak is fókuszába került, mint a KDIGO Controversies Conference, a DOPPS 1–5 vagy az európai AROII Study. Hasznos lehet mind a kezelőszemélyzet (alapellátás, társszakmák, nefrológia), mind a betegek számára azon rizikóbecslések ismerete, amelyek megjósolhatják a CKD 3–5. stádiumú betegek életkilátásait (vesepótló kezelés szükségének várható ideje, mortalitás kockázata), illetve dialízisbe kerülő páciensek rövid (vagy akár hosszabb) távú túlélési esélyeit. Kiemelnénk predializált betegeknél a Bansal-score, a kétféle (négy és nyolc variábilist tartalmazó) Kidney Failure Risk Equation (KFRE) egyenleteket, dialízisbe került betegeknél pedig a rövid távú mortalitást prognosztizáló REIN tanulmányt, illetve a Cohen-modellt – valamennyi könnyen számítható webkalkulátorok segítségével. A közleményben bemutatott validált vizsgálatokban a prognosztizált és az észlelt túlélési adatok különbsége csekély (2-7%). Ennek ellenére a predikció értékelését egyénre szabva kell végezni, hogy a beteg preferenciái szerint a számára legmegfelelőbb döntés szülessen a további kezelést illetően: konzervatív terápia vagy dialízis (esetleg transzplantáció).

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MO287REAL WORLD COMPARISON OF THE PREDICTIVE UTILITY OF INTERNATIONAL IGA RISK PREDICTION SCORE AND KIDNEY FAILURE RISK EQUATION IN IGA NEPHROPATHY PATIENTS

Nephrology Dialysis Transplantation ◽

10.1093/ndt/gfab104.0045 ◽

2021 ◽

Vol 36 (Supplement_1) ◽

Author(s):

Sophia Mohammed ◽

Rajkumar Chinnadurai ◽

Arvind Ponnusamy

Keyword(s):

Iga Nephropathy ◽

Risk Prediction ◽

Kidney Failure ◽

Renal Outcome ◽

Predictive Utility ◽

Statistical Validation ◽

Risk Equation ◽

Failure Risk ◽

Stage Renal Disease

Abstract Background and Aims IgA nephropathy is the most prevalent cause of glomerular disease worldwide. The international IgA risk prediction (IgAN) score is a well validated tool to predict the risk of 50% decline in eGFR or end stage renal disease (ESRD) at five years after biopsy in patients with IgA nephropathy. Also, the four variable kidney failure risk equation (KFRE) is another validated tool used to predict the two- and five-year risk of progression to ESRD of all cause chronic kidney disease (CKD 3-5). Our aim is to compare the predictive utility of IgAN score and the KFRE in a real-world cohort of Caucasian patients with long-term follow-up data. Method All available patients with biopsy-proven IgA nephropathy in our centre between January 2001 and December 2013 were included in this observational study. Baseline (biopsy date) data relevant to the scores including demographics, laboratory and the histopathological features were collated at the time of biopsy. Follow up data on renal functions and renal outcome (50% decline in eGFR or reaching ESRD) were collected until an arbitrary end date 31/12/2018. Results We had a total of 115 patients recorded over this 13-year period. The median age of our cohort at time of biopsy was 41 years. Men represented 71% of the cohort. At baseline 84% were hypertensive and 11% diabetic. 77% were on a renin-angiotensin blocker, with 53% being on a statin. Out of the 115 patients, 74 were eligible to undergo analysis. The percentage risk of reaching the endpoint (50% decline in eGFR or reaching ESRD) was calculated at 2 years and 5 years for all patients. These results can be seen in table 1 and 2. At 2 years, 7 patients had reached the endpoint: 2 patients had a >50% decline in eGFR, 3 patients received RRT and 2 patients underwent transplantation. At 5 years, 14 patients had reached the endpoint: 3 patients had a >50% decline in eGFR, 6 patients received RRT and 5 underwent transplantation. Conclusion Our data suggests that the KRFE tool underpredicts the risk of reaching endpoint, compared to the IgAN. Our study has helped to compare the two tools, but further statistical validation is required using a larger cohort.

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