Use of the Kidney Failure Risk Equation to Determine the Risk of Progression to End-stage Renal Disease in Children With Chronic Kidney Disease | Chronic Kidney Disease | JAMA Pediatrics | JAMA Network
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Figure 1.  C Statistics for the 2-Year 8-Variable Kidney Failure Risk Equation by Patient Characteristic
C Statistics for the 2-Year 8-Variable Kidney Failure Risk Equation by Patient Characteristic

Error bars indicate 95% CI. C statistics and 95% CIs closer to 1.00 indicate better discrimination. CKD indicates chronic kidney disease.

Figure 2.  Estimated vs Observed Probability of End-stage Renal Disease (ESRD) at 2 Years by Risk Group
Estimated vs Observed Probability of End-stage Renal Disease (ESRD) at 2 Years by Risk Group

Data represent the median estimated Kidney Failure Risk Equation (KFRE) scores in each tertile of ESRD risk and the actual percentage of the cohort (Kaplan-Meier estimate) who developed ESRD at 2 years. Estimated risk group 1 corresponds to participants with a 2-year KFRE score of less than 2.6%; risk group 2, KFRE scores of at least 2.6% but less than 13.2%; and risk group 3, KFRE risk score of at least 13.2%.

Table 1.  Baseline Characteristics of the Cohort
Baseline Characteristics of the Cohort
Table 2.  C Statistics for the 4- and 8-Variable KFRE Applied to the CKiD Cohort
C Statistics for the 4- and 8-Variable KFRE Applied to the CKiD Cohort
Table 3.  Comparison of Baseline Characteristics by 2-Year KFRE Score
Comparison of Baseline Characteristics by 2-Year KFRE Score
1.
Tangri  N, Stevens  LA, Griffith  J,  et al.  A predictive model for progression of chronic kidney disease to kidney failure.  JAMA. 2011;305(15):1553-1559. PubMedGoogle ScholarCrossref
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Peeters  MJ, van Zuilen  AD, van den Brand  JA, Bots  ML, Blankestijn  PJ, Wetzels  JF; MASTERPLAN Study Group.  Validation of the kidney failure risk equation in European CKD patients.  Nephrol Dial Transplant. 2013;28(7):1773-1779.PubMedGoogle ScholarCrossref
3.
Grams  ME, Li  L, Greene  TH,  et al.  Estimating time to ESRD using kidney failure risk equations: results from the African American Study of Kidney Disease and Hypertension (AASK).  Am J Kidney Dis. 2015;65(3):394-402.PubMedGoogle ScholarCrossref
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Tangri  N, Grams  ME, Levey  AS,  et al; CKD Prognosis Consortium.  Multinational assessment of accuracy of equations for predicting risk of kidney failure: a meta-analysis.  JAMA. 2016;315(2):164-174. PubMedGoogle ScholarCrossref
5.
Warady  BA, Abraham  AG, Schwartz  GJ,  et al.  Predictors of rapid progression of glomerular and nonglomerular kidney disease in children and adolescents: the Chronic Kidney Disease in Children (CKiD) cohort.  Am J Kidney Dis. 2015;65(6):878-888.PubMedGoogle ScholarCrossref
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Mange  KC, Joffe  MM, Feldman  HI.  Effect of the use or nonuse of long-term dialysis on the subsequent survival of renal transplants from living donors.  N Engl J Med. 2001;344(10):726-731.PubMedGoogle ScholarCrossref
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Ellis  EN, Martz  K, Talley  L, Ilyas  M, Pennington  KL, Blaszak  RT.  Factors related to long-term renal transplant function in children.  Pediatr Nephrol. 2008;23(7):1149-1155.PubMedGoogle ScholarCrossref
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Vats  AN, Donaldson  L, Fine  RN, Chavers  BM.  Pretransplant dialysis status and outcome of renal transplantation in North American children: a NAPRTCS Study: North American Pediatric Renal Transplant Cooperative Study.  Transplantation. 2000;69(7):1414-1419.PubMedGoogle ScholarCrossref
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Amaral  S, Sayed  BA, Kutner  N, Patzer  RE.  Preemptive kidney transplantation is associated with survival benefits among pediatric patients with end-stage renal disease.  Kidney Int. 2016;90(5):1100-1108.PubMedGoogle ScholarCrossref
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Groothoff  JW.  Long-term outcomes of children with end-stage renal disease.  Pediatr Nephrol. 2005;20(7):849-853.PubMedGoogle ScholarCrossref
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Goldstein  SL, Graham  N, Burwinkle  T, Warady  B, Farrah  R, Varni  JW.  Health-related quality of life in pediatric patients with ESRD.  Pediatr Nephrol. 2006;21(6):846-850.PubMedGoogle ScholarCrossref
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Rana  A, Gruessner  A, Agopian  VG,  et al.  Survival benefit of solid-organ transplant in the United States.  JAMA Surg. 2015;150(3):252-259.PubMedGoogle ScholarCrossref
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Jung  HW, Kim  HY, Lee  YA,  et al.  Factors affecting growth and final adult height after pediatric renal transplantation.  Transplant Proc. 2013;45(1):108-114.PubMedGoogle ScholarCrossref
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Neu  AM.  Immunizations in children with chronic kidney disease.  Pediatr Nephrol. 2012;27(8):1257-1263.PubMedGoogle ScholarCrossref
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Lee  DH, Boyle  SM, Malat  G, Sharma  A, Bias  T, Doyle  AM.  Low rates of vaccination in listed kidney transplant candidates.  Transpl Infect Dis. 2016;18(1):155-159.PubMedGoogle ScholarCrossref
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Ku  E, Johansen  KL, Portale  AA, Grimes  B, Hsu  CY.  State level variations in nephrology workforce and timing and incidence of dialysis in the United States among children and adults: a retrospective cohort study.  BMC Nephrol. 2015;16:2-11.PubMedGoogle ScholarCrossref
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National Institute of Diabetes and Digestive and Kidney Diseases. NIDDK Central Repository. https://www.niddkrepository.org/studies/ckid/. Updated August 22, 2017. Accessed February 21, 2015.
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Furth  SL, Cole  SR, Moxey-Mims  M,  et al.  Design and methods of the Chronic Kidney Disease in Children (CKiD) prospective cohort study.  Clin J Am Soc Nephrol. 2006;1(5):1006-1015.PubMedGoogle ScholarCrossref
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Furth  SL, Abraham  AG, Jerry-Fluker  J,  et al.  Metabolic abnormalities, cardiovascular disease risk factors, and GFR decline in children with chronic kidney disease.  Clin J Am Soc Nephrol. 2011;6(9):2132-2140.PubMedGoogle ScholarCrossref
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Schwartz  GJ, Muñoz  A, Schneider  MF,  et al.  New equations to estimate GFR in children with CKD.  J Am Soc Nephrol. 2009;20(3):629-637.PubMedGoogle ScholarCrossref
21.
Wong  CS, Pierce  CB, Cole  SR,  et al; CKiD Investigators.  Association of proteinuria with race, cause of chronic kidney disease, and glomerular filtration rate in the Chronic Kidney Disease in Children study.  Clin J Am Soc Nephrol. 2009;4(4):812-819.PubMedGoogle ScholarCrossref
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Beier  UH, Green  C, Meyers  KE.  Caring for adolescent renal patients.  Kidney Int. 2010;77(4):285-291.PubMedGoogle ScholarCrossref
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Boehm  M, Winkelmayer  WC, Arbeiter  K, Mueller  T, Aufricht  C.  Late referral to paediatric renal failure service impairs access to pre-emptive kidney transplantation in children.  Arch Dis Child. 2010;95(8):634-638.PubMedGoogle ScholarCrossref
Original Investigation
February 2018

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

Author Affiliations
  • 1Division of Nephrology, Department of Pediatrics, University of California, San Francisco
  • 2Department of Epidemiology and Biostatistics, University of California, San Francisco
  • 3Division of Nephrology and Hypertension, Department of Pediatrics, Cincinnati Children’s Hospital, Cincinnati, Ohio
  • 4Division of Nephrology, Department of Pediatrics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
  • 5Division of Nephrology, Department of Pediatrics, Children’s Mercy Kansas City, Kansas City, Missouri
  • 6Division of Nephrology, Department of Medicine, University of California, San Francisco
JAMA Pediatr. 2018;172(2):174-180. doi:10.1001/jamapediatrics.2017.4083
Key Points

Question  Does the kidney failure risk equation, which was developed for adults with chronic kidney disease, accurately determine the risk of progression to end-stage renal disease in children with chronic kidney disease?

Findings  In this cohort study of 603 children with chronic kidney disease in the Chronic Kidney Disease in Children study, the kidney failure risk equation score was associated with progression to end-stage renal disease at 1, 2, and 5 years.

Meaning  Use of the kidney failure risk equation in children with chronic kidney disease may improve the ability to determine the short- and intermediate-term risk for progression to end-stage renal disease; this knowledge may improve the timing of appropriate anticipatory guidance and preparation for kidney transplant or dialysis.

Abstract

Importance  The kidney failure risk equation (KFRE) has been shown to accurately estimate progression to kidney failure in adults with chronic kidney disease (CKD). Use of the KFRE in children with CKD, if accurate, would help to optimize planning for end-stage renal disease (ESRD) care.

Objective  To determine whether the KFRE adequately discriminates the risk of ESRD in children with CKD.

Design, Setting, and Participants  This retrospective cohort study included 603 children with an estimated glomerular filtration rate of less than 60 mL/min/1.73 m2 in the Chronic Kidney Disease in Children study, a national multicenter observational study. Data were collected from January 1, 2005, through July 31, 2013, and analyzed from September 30, 2016, through September 8, 2017.

Exposures  The primary predictive factors were the 4-variable (age, sex, bedside Schwartz estimated glomerular filtration rate, and ratio of albumin to creatinine levels) and 8-variable (4 variables plus serum calcium, phosphate, bicarbonate, and albumin levels) KFREs, which provide 1-, 2-, and 5-year estimates of the risk of progression to ESRD.

Main Outcomes and Measures  Time to ESRD. The Cox proportional hazards model was used to examine the association between the KFRE score and time to ESRD. C statistics were used to discriminate ESRD risk by the KFRE, with a value of greater than 0.80 indicating strong discrimination.

Results  Of the 603 children included in the study, 378 were boys (62.7%) and 225 were girls (37.3%); median age at study entry was 12 years (interquartile range, 8-15 years). Median estimated glomerular filtration rate was 44 mL/min/1.73 m2. Four hundred fifty-seven participants (75.8%) had a nonglomerular cause of CKD. Median observation time was 3.8 years (interquartile range, 1.7-6.2 years); 144 (23.9%) developed ESRD within 5 years of enrollment. The 4-variable KFRE scores discriminated risk of ESRD, with C statistics of 0.90, 0.86, and 0.81 for the 1-, 2-, and 5-year risk scores, respectively. Results were similar using the 8-variable equation.

Conclusions and Relevance  The KFRE is a simple tool that provides excellent discrimination of the risk of ESRD. Results suggest that the KFRE could be incorporated into the clinical care of children with CKD to aid in anticipatory guidance, timing of referral for transplant evaluation, and planning for dialysis access.

Introduction

The kidney failure risk equations (KFREs) were originally developed in a large Canadian cohort to aid in clinical decision making for adults with chronic kidney disease (CKD).1 These equations have been shown to be highly accurate in the identification of adults at high risk for progression to end-stage renal disease (ESRD) and can be used to help optimize the timing of dialysis access and/or kidney transplant. Since their development, these risk equations have been validated in diverse adult populations worldwide.2-4

Although risk factors for disease progression in children with CKD have been well-established,5 no established risk equations currently help to determine when a child will likely require renal replacement therapy. Improved estimation of when a child with CKD will experience progression to ESRD has several potentially valuable applications. First, knowledge of the risk of ESRD within a specified period would improve the ability of clinicians to give anticipatory guidance to patients and their families. Second, routine use of the KFRE may improve rates of preemptive kidney transplant (transplant before the need for dialysis occurs). Preemptive transplant currently occurs in only 22% of children with ESRD despite increasing evidence in children with CKD that preemptive transplant improves allograft and patient survival6-9 and that prompt transplant confers additional benefits, including improved health-related quality of life, cognition, and growth in children, compared with maintenance dialysis.10-13 Third, better estimation of the timing of ESRD onset would allow for improved completion of immunizations in children with CKD before transplant, which is currently suboptimal despite the significant morbidity and mortality associated with vaccine-preventable infections after transplant.14,15 In addition, in rural areas with no ready access to pediatric nephrologists,16 improved estimation of when a child will progress to ESRD might aid general practitioners in timing referrals to nephrology. The objective of this study was to apply the KFREs to a pediatric CKD cohort to determine whether these equations accurately discriminate the risk of ESRD in children.

Methods
Study Population

We studied participants enrolled in the Chronic Kidney Disease in Children (CKiD) study.17 Details of the CKiD study design have been published previously.18,19 In brief, the CKiD study enrolled children and adolescents aged 1 to 16 years (hereinafter referred to as children) with an estimated glomerular filtration rate (eGFR) from 30 to 90 mL/min/1.73 m2 and followed them up prospectively from January 1, 2005, through July 31, 2013. For the purpose of this study, we included all children in the CKiD study with an eGFR of less than 60 mL/min/1.73 m2 using the bedside Schwartz equation20 at baseline enrollment. We chose to include only children with an eGFR of less than 60 mL/min/1.73 m2, because the KFRE was developed and validated in adults with an eGFR below this level. For participants who did not have an eGFR of less than 60 mL/min/1.73 m2 at the time of baseline enrollment into the CKiD study, the first visit when the eGFR fell below 60 mL/min/1.73 m2 was used as their baseline visit. Participants with missing covariates needed for determination of the KFRE were excluded. Participants were also excluded if they had a diagnosis of hyperoxaluria, because these participants may start renal replacement at higher levels of renal function than the norm. The institutional review board of the University of California considered this study exempt from research approval for human subjects. Informed consent was obtained locally from study participants at all CKiD sites.

Variables

The primary predictive factor was the calculated risk of kidney failure (percentage of risk of developing ESRD within a 1-, 2-, or 5-year period). The 4-variable (age, sex, eGFR, and ratio of albumin to creatinine levels [ACR]) and the 8-variable (4 variables plus serum calcium, phosphate, bicarbonate, and albumin levels) KFREs were used as primary predictive factors in separate models.1,3,4 The variables needed to determine the risk, as described above, were obtained from the baseline visit or the visit when eGFR first decreased to below 60 mL/min/1.73 m2. Because albuminuria was not uniformly measured in the CKiD study, the ratio of protein to creatinine levels (PCR) was transformed to an ACR using a published equation.3 We ascertained the adequacy of this conversion by determining the correlation between the ACR and the PCR transformed to ACR (Spearman r = 0.9; P < .001).

The study outcome of interest was time to ESRD, defined as receipt of long-term dialysis or kidney transplant (whichever came first). Additional covariates of interest included sex, race, ethnicity, cause of CKD, systolic and diastolic blood pressure measurements, and hemoglobin level.

Statistical Analysis

Data were analyzed from September 30, 2016, through September 8, 2017. For our analysis, Cox proportional hazards models were used to examine the association between estimated risk of kidney failure and time to ESRD. Postestimation C statistics were used to determine the ability of the KFRE to discriminate the risk of ESRD. Confidence intervals for the C statistics and their difference were determined using a bootstrap approach with 500 repetitions. We also examined whether discrimination of the KFRE was different among the following prespecified subgroups: age (≥12 vs <12 years), sex, race (white vs nonwhite), ethnicity (Hispanic vs non-Hispanic), and cause of CKD (glomerular vs nonglomerular). Participants who died (n = 2) were censored from our analysis at the time of death; therefore, deaths were not treated as a competing risk given their rare occurrence within this pediatric cohort.

In secondary analyses to determine whether the model was well-calibrated, we divided the cohort into risk tertiles based on the 2-year, 8-variable KFRE scores, and we fit Kaplan-Meier survival curves to each risk tertile to determine whether the estimated survival matched the actual survival. In addition, we compared characteristics of participants with a KFRE score of greater than 13% (representing the highest tertile) at 2 years with those with a KFRE risk score of 13% or less at 2 years using the unpaired 2-tailed t test, χ2 test, or Wilcoxon rank sum test as appropriate.

Sensitivity Analyses

To determine whether the ACR derived from mathematical conversion would yield results similar to actual ACR measurements, we compared risk discrimination by the 2-year, 8-variable KFRE among the subset of the population that had measured and calculated ACR data available.

All data were derived from the National Institute of Diabetes and Digestive and Kidney Diseases Central Repository in deidentified form, and data were administratively censored as of July 2013. Dates of birth in this data set were rounded to the nearest year to protect patient identity. All data analyses were conducted using STATA software (version 13; StataCorp).

Results

A total of 603 participants (378 boys [62.7%] and 225 girls [37.3%]; median age at study entry, 12 years; interquartile range [IQR], 8-15 years) met our inclusion criteria and were included for study. We identified 636 children in the cohort with an eGFR of less than 60 mL/min/1.73 m2, of whom 31 were missing data necessary for the KFRE calculations, and 2 additional participants were excluded for a diagnosis of hyperoxaluria. Of the 603 children remaining, 528 had an eGFR of less than 60 mL/min/1.73 m2 at baseline enrollment into the CKiD, and an additional 75 children were included at the time when their eGFR decreased to less than 60 mL/min/1.73 m2 at a CKiD follow-up visit.

Demographic, baseline physical examination, and laboratory data are shown in Table 1. The median eGFR was 44 mL/min/1.73 m2 (interquartile range [IQR], 33-53 mL/min/1.73 m2). Four hundred one participants (66.5%) were white, 506 (83.9%) were non-Hispanic, and 457 (75.8%) had a nonglomerular cause of CKD. During the follow-up period, 27 (4.5%) progressed to ESRD by 1 year of follow-up; 62 (10.3%), by 2 years; and 144 (23.9%), by 5 years. The median follow-up time for the cohort was 3.8 years (IQR, 1.7-6.2 years).

The results of the application of the KFRE to our cohort are shown in Table 2. Overall, the equation provided excellent discrimination and was similar whether the 4- or 8-variable equation was used. As shown in Figure 1, although performance of the KFRE was similar by race and cause of CKD, a statistically significant difference occurred in discriminatory performance by ethnicity and age. The C statistic was higher in the Hispanic (0.96; 95% CI, 0.91-0.98) compared with the non-Hispanic (0.86; 95% CI, 0.81-0.90) populations and higher for those younger than 12 years (0.94; 95% CI, 0.91-0.97) compared with those 12 years and older (0.85; 95% CI, 0.79-0.90). Of note, the percentage of Hispanic vs non-Hispanic children who progressed to ESRD by 2 years was similar (9 [9.3%] vs 52 [10.4%], respectively), whereas 15 children (5%) younger than 12 years progressed to ESRD by 2 years compared with 47 (15.2%) of those 12 years or older.

Results from our model calibration are shown in Figure 2. The cohort was divided into risk group tertiles based on the 2-year, 8-variable KFRE score as follows: estimated risk group 1 corresponds to a 2-year KFRE score of less than 2.6%; estimated risk group 2, a KFRE score of at least 2.6% but less than 13.2%; and estimated risk group 3, a KFRE score of at least 13.2%. The median KFRE score for those in the highest tertile was 33%. As depicted in Figure 2, the estimated proportion of participants who developed ESRD closely matched the observed proportion who progressed to ESRD at 2 years.

We compared the characteristics of children with a 2-year KFRE score in the top tertile (>13%) with those of children in the lower tertiles (≤13%) (Table 3). Overall, the median 2-year KFRE score was 6% (IQR, 2%-21%). Of the entire cohort, 202 children (33.5%) had a 2-year KFRE score of greater than 13%, and 55 (27.2%) of this group progressed to ESRD within 2 years of follow-up compared with only 7 of 401 (1.7%) with a 2-year KFRE score of 13% or less. Participants with a 2-year KFRE score above 13% were notably older and more often male and had higher blood pressures and lower hemoglobin levels at the time of entry into our study.

One hundred forty-six participants (24.2%) had measured ACR data available. For this subset of patients, no difference was found in the 2-year risk discrimination using the KFRE (C statistic, 0.96 for models including measured or calculated ACR; 95% CI for the difference in C statistics, −0.001 to 0.30).

Discussion

Few tools are currently available to estimate whether a child with CKD will develop ESRD within 5 years. In this study, we used a validated risk equation originally developed in an adult cohort to determine whether equal risk discrimination could be provided with the use of the KFRE in children. Overall, we found that the KFRE score provides excellent discrimination and calibration of the risk of ESRD in a pediatric cohort with stages 3 and 4 CKD. The KFRE is a simple tool that uses routinely collected demographic and laboratory data and can easily be applied by all clinicians caring for children with CKD to help provide anticipatory guidance, gauge timing of referral to subspecialty care and transplant centers, and plan for the optimal timing of dialysis access and kidney transplant.

Although Tangri et al1 initially developed and validated the KFRE in a large Canadian cohort, the KFRE was subsequently validated in a cohort of European patients with CKD2 and was found to accurately estimate risk of kidney failure, with the best performance by the 8-variable equation. Performance of the equation has also been validated in the African American Study of Kidney Disease and Hypertension cohort,3 in which use of the 1-year 4-variable equation improved ESRD risk estimation better than eGFR alone. In addition, Tangri et al4 assessed the accuracy of the KFREs for estimating the risk of kidney failure using the CKD Prognosis Consortium with participants from 30 countries and generally found that the equations remained highly accurate, although a calibration factor was suggested for non–North American cohorts for which the original equation overestimated risk. Our study is novel in the application of this risk equation for the first time, to our knowledge, to a large cohort of children with CKD.

Although an equation developed in adults who have different causes of their CKD performed well in children, the variables used in the KFRE notably include those that have been previously identified as risk factors for CKD progression in adults and children.5 Warady et al5 previously found that older age, male sex, proteinuria, hypoalbuminemia, and hyperphosphatemia were associated with a shorter time to CKD progression in children with a nonglomerular cause of ESRD, although for those with a glomerular cause, age, sex, and hyperphosphatemia were not associated with ESRD risk. For all causes of CKD, additional risk factors not accounted for in the KFRE but previously identified as being important in children included elevated blood pressure and anemia.5 In contrast to what would be expected based on the previously cited study, a glomerular vs nonglomerular cause of CKD did not significantly affect performance of the KFRE, although the number of patients with glomerular causes of CKD was lower in the CKiD cohort.5 In addition, those with glomerular causes of CKD would be expected to have more albuminuria,21 which is likely the main driver of CKD progression in this population, as evidenced by the high C statistic with use of the KFRE even among this subpopulation.

We found better performance of the KFRE in younger children and those of Hispanic ethnicity. The reasons for differences by age are unclear, although we speculate that in younger children, kidney disease tends to have nonglomerular causes (congenital anomalies of the kidney and urinary tract) and therefore may have a more predictable pattern of progression. In addition, progression of CKD in older children may be subject to additional confounders, including nonadherence to therapy22 and increased heterogeneity in the causes of CKD, which may decrease the KFRE’s ability to discriminate the risk of ESRD. We believe that the difference in performance of the KFRE by ethnicity deserves further exploration and validation, and we cannot definitively rule out the possibility that this finding occurred by chance. We did not, on the other hand, find statistically significant differences in performance of the KFRE by race or cause of CKD.

Use of the KFRE may provide opportunities to improve the care of children with CKD. The provision of better tools to estimate the timing of ESRD onset could allow for better timing of living donor workup and planning for preemptive transplant. Preemptive transplant is associated with better renal allograft and patient survival among children compared with any dialysis exposure9 and for this reason should be the preferred treatment for children with ESRD. Preemptive transplant rates could improve with better estimation of the timing of ESRD onset, and future studies might evaluate the KFRE score that may warrant transplant waitlist activation. For example, in the adult literature, a 1-year KFRE score of greater than 10% should prompt planning for ESRD care.3 Use of the KFRE in children with CKD would not only be useful for pediatric nephrologists but also would have broader applicability to general practitioners and adult nephrologists caring for children with CKD. A number of states have no pediatric nephrologists16; thus, the KFRE score would be useful in appropriately timing referral to pediatric dialysis and transplant centers for end-stage care. This use is especially important given that late referral to pediatric nephrology care has been associated with a decreased likelihood of preemptive transplant.23 Vaccination rates before kidney transplant may also be improved15 with better estimation of the amount of time before ESRD onset. Specifically, an accelerated vaccine schedule might be appropriate for the child rapidly progressing to ESRD, and revaccination for children without protective antibody titers should be strongly considered before kidney transplant, after which live vaccines are contraindicated.14 In addition, all clinicians could use the information gained from use of the KFRE to provide counseling and guide families in the planning of important life decisions regarding finances, housing, and education. However, we would emphasize that a low 5-year KFRE score does not mean that the lifetime risk of ESRD is necessarily low. Given the long-term survival of most pediatric patients with CKD, the lifetime risk of kidney failure may still be significant, and ongoing interventions that retard the progression of CKD should still be aggressively pursued.

Strengths and Limitations

Our study has several strengths and limitations. Our study is strengthened by the relatively large size of this pediatric CKD cohort with national representation and comprehensive follow-up data, including ascertainment of time to ESRD. Our study is limited by missing albuminuria measurements, although we were able to estimate albuminuria from proteinuria measurements using a previously validated approach with high correlation.3 Our study is also limited by the median follow-up time of only 3.8 years, which may explain the relatively lower C statistic for the 5-year KFRE score compared with shorter-term risk scores in this cohort. Our study results may not be generalizable to the larger population of children with CKD, given that enrollment in the CKiD study is voluntary and therefore may select for more adherent patients and families.

Conclusions

We found that use of the KFRE in children with CKD accurately discriminates risk of ESRD. We propose that this equation be incorporated into the clinical care of children with CKD to aid clinicians, patients, and their families in planning for end-stage care.

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Article Information

Corresponding Author: Erica Winnicki, MD, Division of Nephrology, Department of Pediatrics, University of California, San Francisco, 550 16th St, 5th Floor, San Francisco, CA 94143 (erica.winnicki@ucsf.edu).

Accepted for Publication: September 18, 2017.

Published Online: December 18, 2017. doi:10.1001/jamapediatrics.2017.4083

Author Contributions: Drs Winnicki and Ku had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Winnicki, Furth, Ku.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Winnicki, Ku.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Winnicki, McCulloch.

Obtained funding: Furth, Warady, Ku.

Administrative, technical, or material support: Warady.

Study supervision: Furth, Ku.

Conflict of Interest Disclosures: None reported.

Funding/Support: This work was supported by grants HL131023 (Dr Ku) and DK090070 (Dr Mitsnefes) from the National Institutes of Health. The Chronic Kidney Disease in Children Cohort Study (CKiD) was conducted by the CKiD investigators and supported by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), with additional support from grants U01-DK-66143, U01-DK-66174, and U01-DK-66116 from the National Institute of Child Health and Human Development and the National Heart, Lung, and Blood Institute, and NIDDK and grants U01DK-082194 and U01-DK-66116 from the NIDDK. The data and samples from the CKiD study reported herein were supplied by the NIDDK Central Repositories.

Role of the Funder/Sponsor: The sponsors had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: This article does not necessarily reflect the opinions or views of the CKiD study, the NIDDK Central Repositories, or the NIDDK.

Additional Contributions: Barbara Grimes, PhD, University of California, San Francisco, contributed to and verified the statistical analysis. She was compensated for this work.

References
1.
Tangri  N, Stevens  LA, Griffith  J,  et al.  A predictive model for progression of chronic kidney disease to kidney failure.  JAMA. 2011;305(15):1553-1559. PubMedGoogle ScholarCrossref
2.
Peeters  MJ, van Zuilen  AD, van den Brand  JA, Bots  ML, Blankestijn  PJ, Wetzels  JF; MASTERPLAN Study Group.  Validation of the kidney failure risk equation in European CKD patients.  Nephrol Dial Transplant. 2013;28(7):1773-1779.PubMedGoogle ScholarCrossref
3.
Grams  ME, Li  L, Greene  TH,  et al.  Estimating time to ESRD using kidney failure risk equations: results from the African American Study of Kidney Disease and Hypertension (AASK).  Am J Kidney Dis. 2015;65(3):394-402.PubMedGoogle ScholarCrossref
4.
Tangri  N, Grams  ME, Levey  AS,  et al; CKD Prognosis Consortium.  Multinational assessment of accuracy of equations for predicting risk of kidney failure: a meta-analysis.  JAMA. 2016;315(2):164-174. PubMedGoogle ScholarCrossref
5.
Warady  BA, Abraham  AG, Schwartz  GJ,  et al.  Predictors of rapid progression of glomerular and nonglomerular kidney disease in children and adolescents: the Chronic Kidney Disease in Children (CKiD) cohort.  Am J Kidney Dis. 2015;65(6):878-888.PubMedGoogle ScholarCrossref
6.
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