Accuracy of Kidney Failure Risk Equation in Transplant Recipients

An increasing number of kidney transplants are being performed worldwide. At present, patients with failing kidney allografts comprise a significant proportion (5%) of patients beginning dialysis.1Molnar M.Z. Ichii H. Lineen J. et al.Timing of return to dialysis in patients with failing kidney transplants.Semin Dial. 2013; 26: 667-674Crossref PubMed Scopus (17) Google Scholar Accurately predicting the risk of end-stage kidney disease (ESKD) in transplant patients may help clinical decision making to individualize patient care and improve access planning for dialysis and retransplantation. Kidney transplantation is the treatment of choice for ESKD, as it improves both mortality and morbidity compared with dialysis modalities.2Suthanthiran M. Strom T.B. Renal transplantation.N Engl J Med. 1994; 331: 365-376Crossref PubMed Scopus (370) Google Scholar, 3Schnuelle P. Lorenz D. Trede M. et al.Impact of renal cadaveric transplantation on survival in end-stage renal failure: evidence for reduced mortality risk compared with hemodialysis during long-term follow-up.J Am Soc Nephrol. 1998; 9: 2135-2141Crossref PubMed Google Scholar Several models have been developed to predict kidney allograft failure,4Kabore R. Haller M.C. Harambat J. et al.Risk prediction models for graft failure in kidney transplantation: a systematic review.Nephrol Dial Transplant. 2017; 32: ii68-ii76Crossref PubMed Scopus (26) Google Scholar but many of them require a renal biopsy and are not simple to use in day-to-day clinical decision making. Tangri et al.5Tangri N. Stevens L.A. Griffith J. et al.A predictive model for progression of chronic kidney disease to kidney failure.JAMA. 2011; 305: 1553-1559Crossref PubMed Scopus (580) Google Scholar developed the Kidney Failure Risk Equation (KFRE) for patients with native chronic kidney disease. It accurately predicts the risk of needing renal replacement therapy at 2 and 5 years. The equation relies on age, sex, estimated glomerular filtration rate (eGFR) and spot urine albumin-creatinine ratio. The KFRE has been adopted as a tool for predicting the need for renal replacement therapy in several jurisdictions.6Ontario Renal NetworkOntario 2016 CKD System Atlas: Trends in Kidney Disease and care. Ontario Renal Network, Toronto, Canada2016Google Scholar Although the KFRE has been validated in several populations,7Peeters M.J. van Zuilen A.D. van den Brand J.A.J.G. et al.Validation of the kidney failure risk equation in European CKD patients.Nephrol Dial Transplant. 2013; 28: 1773-1779Crossref PubMed Scopus (40) Google Scholar to our knowledge, the KFRE has not been validated in populations that have received a kidney transplant. This study assesses the accuracy of the KFRE in renal transplant recipients. A total of 956 kidney transplants were performed at The Ottawa Hospital between January 1, 2000, and December 31, 2014. Data were collected on 877 kidney transplants. Seventy-nine patients did not have adequate data to calculate KFRE or had died before reaching the 1-year point. Patient characteristics are shown in Table 1. Data to calculate KFRE were available on 877 patients (living donors n = 414; eGFR < 60 n = 488) at 12 months, 801 patients (living donors n = 386; eGFR < 60 n = 400) at 24 months, and 547 patients (living donors n = 264; eGFR < 60 n = 269) at 60 months. Mean age was 51 and most patients were white. The most common cause of kidney disease was glomerulonephritis.Table 1Kidney transplant recipient characteristics, N = 887Age, yr, mean (SD)51 (14.1)Female, n (%)340 (38.3)White, n (%)762 (86.0)Asian, n (%)41 (4.6)Black, n (%)48 (5.4)Other, n (%)38 (4.0)Living donor, n (%)426 (48)Cause of kidney diseaseGlomerulonephritis, n (%)207 (23)Polycystic kidney disease, n (%)100 (11.3)Diabetes, n (%)168 (18.9)Hypertension, n (%)39 (4.4)Other, n (%)122 (13.8)Unknown, n (%)251 (28.3)eGFR,aCalculated by Chronic Kidney Disease–Epidemiology Collaboration equation.11 ml/min per 1.73 m2, mean (SD)At 12 mo58.4 (22)At 24 mo61.1 (22)At 60 mo61.2 (23)ACR, mg/mmol, median (IQR)At 12 mo2 (1–6)At 24 mo2.2 (1–7)At 60 mo2.8 (1–10)ACR, albumin-creatinine ratio; eGFR, estimated glomerular filtration rate; IQR, interquartile range.a Calculated by Chronic Kidney Disease–Epidemiology Collaboration equation.11 Open table in a new tab ACR, albumin-creatinine ratio; eGFR, estimated glomerular filtration rate; IQR, interquartile range. When comparing 2-year KFRE predictions with observed ESKD events, the receiver operating characteristic curve values ranged from 0.73 to 0.93 for different time periods of calculation (Table 2 and Figure 1). The 5-year KFRE risk prediction receiver operating characteristic values ranged from 0.72 to 0.78 for different time periods of calculation (Table 2 and Figure 1). Number of patient deaths with graft function was significantly higher than observed ESKD events.Table 2End-stage kidney disease outcomes from time of KFRE CalculationTime point of KFRE calculationNo. (%) reaching end-stage kidney diseaseNo. (%) of deathsArea under ROC curve (95% CI), allArea under ROC curve (95% CI), eGFR < 60Area under ROC curve (95% CI), eGFR ≥ 602 yr from KFRE calculation12 mo (n = 877)18 (2.1)27 (3.1)0.76 (0.73–0.79)0.79 (0.75–0.83), n = 4880.66 (0.61–0.71), n = 38924 mo (n = 801)13 (1.6)21 (2.6)0.93 (0.91–0.95)0.93 (0.90–0.96), n = 400Unable to calculate, n = 40160 mo (n = 547)8 (1.5)24 (4.4)0.73 (0.69–0.77)0.64 (0.58–0.70), n = 269Unable to calculate, n = 2785 yr from KFRE calculation12 mo (n = 877)37 (4.2)63 (7.2)0.72 (0.69–0.70)0.76 (0.72–0.80), n = 4880.64 (0.60–0.69), n = 38924 mo (n = 801)29 (3.6)56 (7.0)0.78 (0.75–0.80)0.87 (0.83–0.90), n = 4000.51 (0.46–0.56), n = 40160 mo (n = 547)19 (3.5)42 (7.7)0.77 (0.73–0.80)0.73 (0.68–0.79), n = 2690.74 (0.68–0.79), n = 278CI, confidence interval; KFRE, Kidney Failure Risk Equation; ROC, receiver operating characteristic. Open table in a new tab CI, confidence interval; KFRE, Kidney Failure Risk Equation; ROC, receiver operating characteristic. Sensitivity analysis between living and deceased donors did not reveal any major difference. The receiver operating characteristic values ranged from 0.67 to 0.96 for different time periods. We could not calculate 5-year KFRE risk separately for deceased donors because there was only one outcome in this group. A second sensitivity analysis stratified by eGFR of <60 and ≥ 60 ml/min per 1.73 m2 revealed better risk prediction of 2- and 5-year risk at the 12-month time point (Table 2). For eGFR < 60 ml/min per 1.73 m2, the receiver operating characteristic values for 2-year KFRE predictions to observed ESKD events, ranged from 0.64 to 0.93 and for eGFR ≥ 60 ml/min per 1.73 m2, it was 0.51 to 0.74. We could not calculate 2-year KFRE risk separately for eGFR ≥ 60 for 24- and 60-month time points because there was only one outcome in these 2 groups. Our data reveal that KFRE can be used to predict ESKD with good accuracy in kidney transplant recipients at 2 and 5 years in patients surviving at least 1 year posttransplant. Nephrologists can use the KFRE to guide aggressiveness of treatment when issues such as late rejection, malignancy, or infection develop and there is a high predicted risk of ESKD in the near future. This information may also help guide transition away from a calcineurin inhibitor–based regimen. Nephrologists also can use this model to refer patients back to transplant centers when there is a high risk of graft failure. Patients also can benefit from the KFRE while considering retransplantation, and it may encourage living donation. If retransplantation is not an option, it could be used to make access planning for dialysis more efficient. Fifteen studies have assessed predictors for allograft failure in kidney transplant recipients.4Kabore R. Haller M.C. Harambat J. et al.Risk prediction models for graft failure in kidney transplantation: a systematic review.Nephrol Dial Transplant. 2017; 32: ii68-ii76Crossref PubMed Scopus (26) Google Scholar None are in widespread use, as they require variables that are not easily and readily available to most clinicians, whereas variables used by the KFRE are readily available and routinely measured. Our data indicate that the KFRE can be used to predict ESKD in transplant populations. This is in spite of the etiology and pathophysiology of kidney allograft failure being different, compared with native kidney disease. There are several pathological processes that can lead to graft loss, such as calcineurin inhibitor toxicity, chronic antibody-mediated rejection, and acute rejection.8Morales J.M. Marcen R. del Castillo D. et al.Risk factors for graft loss and mortality after renal transplantation according to recipient age: a prospective multicentre study.Nephrol Dial Transplant. 2012; 27: iv39-iv46Crossref PubMed Scopus (66) Google Scholar Transplanted kidneys also are thought to be more susceptible to acute kidney injury9Cooper J.E. Wiseman A.C. Acute kidney injury in kidney transplantation.Curr Opin Nephrol Hypertens. 2013; 22: 698-703Crossref PubMed Scopus (45) Google Scholar and display an accelerated senescence compared with native kidneys (S1). In addition, the accuracy of GFR calculated by the Chronic Kidney Disease–Epidemiology Collaboration equation (S2) has been questioned in renal transplant recipients (S12–15). Despite the differences between transplant and nontransplant populations, the KFRE risk estimate seems to be reasonable to use in the clinical care of patients with a kidney transplant. We did not have data to calculate 8 variable KFRE, which may improve further risk prediction in this population. Limitations to our study should be noted. This study was conducted at a single center where recipients are followed in a subspeciality transplant clinic for the duration of their kidney transplant. However, our outcomes are similar to other centers in Canada.10Davenport A. Review article: Low-molecular-weight heparin as an alternative anticoagulant to unfractionated heparin for routine outpatient haemodialysis treatments.Nephrology (Carlton). 2009; 14: 455-461Crossref PubMed Scopus (101) Google Scholar The dataset was not complete, as a small number of patients did not have the required tests done to calculate KFRE at different time points, but we were able to obtain data on >90% of the patients. We did not have data on use of antirejection medications or on rejection episodes of patients. Although the number of patients included in the study was large, the number of outcomes recorded during the study period was moderate. We may not be able to extrapolate our results to patients surviving with kidney transplant to later time points, as we calculated the KFRE at only 1, 2, and 5 years posttransplant. Finally, 86% of the population was white, and the racial homogeneity of the study population means that the results might not be generalizable to other settings. Strengths of our study include the large number of patients, a robust outcomes assessment, and that all laboratory data were extracted directly from the laboratory system at the center. The KFRE is a useful tool to prognosticate kidney transplant recipients for ESKD at different time points if they have survived without ESKD for 1 year. Clinicians should use the KFRE for prognostication of their patients, and high-risk patients should be referred back to transplant centers (if followed elsewhere), aggressiveness of treatment should be assessed when there is a high risk of ESKD in the short term, and consideration should be given to prepare high-risk patients for dialysis or retransplantation.

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