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Figure 1.
Trends in Adjusted Cardiovascular Hospitalization Rate From Onset of End-stage Renal Disease (ESRD), by Age Group
Trends in Adjusted Cardiovascular Hospitalization Rate From Onset of End-stage Renal Disease (ESRD), by Age Group

Five-year trends in cardiovascular hospitalization for incident end-stage renal disease patients, by age. Adjusted for sex, race, ethnicity, and primary cause of end-stage renal disease. Reference population: patients with incident end-stage renal disease, aged 1 to 29 years (2010-2011).

Figure 2.
Cumulative Incidence of Cardiovascular Mortality by Age Group as a Proportion of All-Cause and Noncardiovascular Mortality
Cumulative Incidence of Cardiovascular Mortality by Age Group as a Proportion of All-Cause and Noncardiovascular Mortality

The proportional 5-year mortality attributable to causes associated with cardiovascular disease (CVD), relative to all-cause mortality.

Table 1.  
Patient Demographics and Clinical Characteristics by Age at End-stage Renal Disease Onset
Patient Demographics and Clinical Characteristics by Age at End-stage Renal Disease Onset
Table 2.  
Unadjusted and Adjusted Hazard Ratios for Risk of Cardiovascular Hospitalizations Based on Characteristics Present at End-stage Renal Disease (ESRD) Onset (N = 13 426)
Unadjusted and Adjusted Hazard Ratios for Risk of Cardiovascular Hospitalizations Based on Characteristics Present at End-stage Renal Disease (ESRD) Onset (N = 13 426)
Table 3.  
Unadjusted and Adjusted Hazard Ratios for Risk of Cardiovascular Mortality Based on Characteristics Present at End-stage Renal Disease (ESRD) Onset (N = 33 156)
Unadjusted and Adjusted Hazard Ratios for Risk of Cardiovascular Mortality Based on Characteristics Present at End-stage Renal Disease (ESRD) Onset (N = 33 156)
1.
Foley  RN, Parfrey  PS, Sarnak  MJ.  Clinical epidemiology of cardiovascular disease in chronic renal disease.  Am J Kidney Dis. 1998;32(5)(suppl 3):S112-S119. doi:10.1053/ajkd.1998.v32.pm9820470PubMedGoogle ScholarCrossref
2.
Saran  R, Robinson  B, Abbott  KC,  et al.  US Renal Data System 2016 annual data report: epidemiology of kidney disease in the United States.  Am J Kidney Dis. 2017;69(3)(suppl 1):A7-A8. doi:10.1053/j.ajkd.2016.12.004PubMedGoogle ScholarCrossref
3.
Groothoff  JW.  Long-term outcomes of children with end-stage renal disease.  Pediatr Nephrol. 2005;20(7):849-853. doi:10.1007/s00467-005-1878-9PubMedGoogle ScholarCrossref
4.
McDonald  SP, Craig  JC; Australian and New Zealand Paediatric Nephrology Association.  Long-term survival of children with end-stage renal disease.  N Engl J Med. 2004;350(26):2654-2662. doi:10.1056/NEJMoa031643PubMedGoogle ScholarCrossref
5.
Wong  CJ, Moxey-Mims  M, Jerry-Fluker  J, Warady  BA, Furth  SL.  CKiD (CKD in children) prospective cohort study: a review of current findings.  Am J Kidney Dis. 2012;60(6):1002-1011. doi:10.1053/j.ajkd.2012.07.018PubMedGoogle ScholarCrossref
6.
Diaz-Gonzalez de Ferris  ME.  Adolescents and emerging adults with chronic kidney disease: their unique morbidities and adherence issues.  Blood Purif. 2011;31(1-3):203-208. doi:10.1159/000321854PubMedGoogle ScholarCrossref
7.
Centers for Medicare and Medicaid Services. ICD-9-CM diagnosis and procedure codes: abbreviated and full code titles. https://www.cms.gov/Medicare/Coding/ICD9ProviderDiagnosticCodes/codes.html. Published 2014. Accessed February 17, 2017.
8.
Centers for Medicare and Medicaid Services. Details for title: CMS 2746. https://www.cms.gov/Medicare/CMS-Forms/CMS-Forms/CMS-Forms-Items/CMS008869.html. Published 2016. Accessed February 14, 2019.
9.
Centers for Disease Control and Prevention. A SAS program for the 2000 CDC growth charts (ages 0 to <20 years). https://www.cdc.gov/nccdphp/dnpao/growthcharts/resources/sas.htm. Published 2016. Accessed February 8, 2017.
10.
Grummer-Strawn  LM, Reinold  C, Krebs  NF; Centers for Disease Control and Prevention (CDC).  Use of World Health Organization and CDC growth charts for children aged 0-59 months in the United States.  MMWR Recomm Rep. 2010;59(RR-9):1-15.PubMedGoogle Scholar
11.
Pickle  LW, White  AA.  Effects of the choice of age-adjustment method on maps of death rates.  Stat Med. 1995;14(5-7):615-627. doi:10.1002/sim.4780140519PubMedGoogle ScholarCrossref
12.
United States Renal Data System, National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases. 2018 ADR chapters. https://www.usrds.org/2018/view/Default.aspx. Published 2018. Accessed February 11, 2019.
13.
Zou  GY, Donner  A.  Extension of the modified Poisson regression model to prospective studies with correlated binary data.  Stat Methods Med Res. 2013;22(6):661-670. doi:10.1177/0962280211427759PubMedGoogle ScholarCrossref
14.
Amorim  LD, Cai  J.  Modelling recurrent events: a tutorial for analysis in epidemiology.  Int J Epidemiol. 2015;44(1):324-333. doi:10.1093/ije/dyu222PubMedGoogle ScholarCrossref
15.
Cox  DR.  Regression models and life-tables.  J R Stat Soc B. 1972;34(2):187-202.Google Scholar
16.
Kalbfleisch  JD, Prentice  RL.  The Statistical Analysis of Failure Time Data. New York, NY: Wiley; 2002. doi:10.1002/9781118032985
17.
Lin  DY.  Non-parametric inference for cumulative incidence functions in competing risks studies.  Stat Med. 1997;16(8):901-910. doi:10.1002/(SICI)1097-0258(19970430)16:8<901::AID-SIM543>3.0.CO;2-MPubMedGoogle ScholarCrossref
18.
Kochanek  KD, Murphy  SL, Xu  J, Tejada-Vera  B.  Deaths: final data for 2014, national vital statistics reports: from the Centers for Disease Control and Prevention, National Center for Health Statistics.  National Vital Statistics System. 2016;65(4):1-122.Google Scholar
19.
Saran  R, Li  Y, Robinson  B,  et al.  US Renal Data System 2014 annual data report: epidemiology of kidney disease in the United States.  Am J Kidney Dis. 2015;66(1)(suppl 1):S1-S305. doi:10.1053/j.ajkd.2015.05.001PubMedGoogle ScholarCrossref
20.
Parekh  RS, Carroll  CE, Wolfe  RA, Port  FK.  Cardiovascular mortality in children and young adults with end-stage kidney disease.  J Pediatr. 2002;141(2):191-197. doi:10.1067/mpd.2002.125910PubMedGoogle ScholarCrossref
21.
Stack  AG, Bloembergen  WE.  A cross-sectional study of the prevalence and clinical correlates of congestive heart failure among incident US dialysis patients.  Am J Kidney Dis. 2001;38(5):992-1000. doi:10.1053/ajkd.2001.28588PubMedGoogle ScholarCrossref
22.
Nelson  CL, Karschimkus  CS, Dragicevic  G,  et al.  Systemic and vascular inflammation is elevated in early IgA and type 1 diabetic nephropathies and relates to vascular disease risk factors and renal function.  Nephrol Dial Transplant. 2005;20(11):2420-2426. doi:10.1093/ndt/gfi067PubMedGoogle ScholarCrossref
23.
Weaver  DJ  Jr, Somers  MJG, Martz  K, Mitsnefes  MM.  Clinical outcomes and survival in pediatric patients initiating chronic dialysis: a report of the NAPRTCS registry.  Pediatr Nephrol. 2017;32(12):2319-2330. doi:10.1007/s00467-017-3759-4PubMedGoogle ScholarCrossref
24.
Agodoa  L, Eggers  P.  Racial and ethnic disparities in end-stage kidney failure-survival paradoxes in African-Americans.  Semin Dial. 2007;20(6):577-585. doi:10.1111/j.1525-139X.2007.00350.xPubMedGoogle ScholarCrossref
25.
Ku  E, McCulloch  CE, Grimes  BA, Johansen  KL.  Racial and ethnic disparities in survival of children with ESRD.  J Am Soc Nephrol. 2017;28(5):1584-1591. doi:10.1681/ASN.2016060706PubMedGoogle ScholarCrossref
26.
Ma  L, Langefeld  CD, Comeau  ME,  et al.  APOL1 renal-risk genotypes associate with longer hemodialysis survival in prevalent nondiabetic African American patients with end-stage renal disease.  Kidney Int. 2016;90(2):389-395. doi:10.1016/j.kint.2016.02.032PubMedGoogle ScholarCrossref
27.
McLean  NO, Robinson  TW, Freedman  BI.  APOL1 gene kidney risk variants and cardiovascular disease: getting to the heart of the matter.  Am J Kidney Dis. 2017;70(2):281-289. doi:10.1053/j.ajkd.2016.11.020PubMedGoogle ScholarCrossref
28.
Tonelli  M, Wiebe  N, Knoll  G,  et al.  Systematic review: kidney transplantation compared with dialysis in clinically relevant outcomes.  Am J Transplant. 2011;11(10):2093-2109. doi:10.1111/j.1600-6143.2011.03686.xPubMedGoogle ScholarCrossref
29.
Kaiser Family Foundation. Health insurance coverage of adults 19-64. https://www.kff.org/other/state-indicator/adults-19-64/. Published 2017. Accessed February 11, 2019.
30.
Kaiser Family Foundation. Health insurance coverage of children 0-18. https://www.kff.org/other/state-indicator/children-0-18/. Published 2017. Accessed February 11, 2019.
31.
Gillespie  BW, Morgenstern  H, Hedgeman  E,  et al.  Nephrology care prior to end-stage renal disease and outcomes among new ESRD patients in the USA.  Clin Kidney J. 2015;8(6):772-780. doi:10.1093/ckj/sfv103PubMedGoogle ScholarCrossref
32.
Kurella-Tamura  M, Goldstein  BA, Hall  YN, Mitani  AA, Winkelmayer  WC.  State medicaid coverage, ESRD incidence, and access to care.  J Am Soc Nephrol. 2014;25(6):1321-1329. doi:10.1681/ASN.2013060658PubMedGoogle ScholarCrossref
33.
Kalantar-Zadeh  K, Block  G, Humphreys  MH, Kopple  JD.  Reverse epidemiology of cardiovascular risk factors in maintenance dialysis patients.  Kidney Int. 2003;63(3):793-808. doi:10.1046/j.1523-1755.2003.00803.xPubMedGoogle ScholarCrossref
34.
Ku  E, Glidden  DV, Hsu  CY, Portale  AA, Grimes  B, Johansen  KL.  Association of body mass index with patient-centered outcomes in children with ESRD.  J Am Soc Nephrol. 2016;27(2):551-558. doi:10.1681/ASN.2015010008PubMedGoogle ScholarCrossref
35.
Caulfield  LE, de Onis  M, Blössner  M, Black  RE.  Undernutrition as an underlying cause of child deaths associated with diarrhea, pneumonia, malaria, and measles.  Am J Clin Nutr. 2004;80(1):193-198. doi:10.1093/ajcn/80.1.193PubMedGoogle ScholarCrossref
36.
Bechard  LJ, Duggan  C, Touger-Decker  R,  et al.  Nutritional status based on body mass index is associated with morbidity and mortality in mechanically ventilated critically ill children in the PICU.  Crit Care Med. 2016;44(8):1530-1537. doi:10.1097/CCM.0000000000001713PubMedGoogle ScholarCrossref
37.
Ku  E, Kopple  JD, McCulloch  CE,  et al.  Associations between weight loss, kidney function decline, and risk of ESRD in the chronic kidney disease in children (CKiD) cohort study.  Am J Kidney Dis. 2018;71(5):648-656. doi:10.1053/j.ajkd.2017.08.013PubMedGoogle ScholarCrossref
38.
Neild  GH.  Primary renal disease in young adults with renal failure.  Nephrol Dial Transplant. 2010;25(4):1025-1032. doi:10.1093/ndt/gfp653PubMedGoogle ScholarCrossref
39.
Groopman  EE, Marasa  M, Cameron-Christie  S,  et al.  Diagnostic utility of exome sequencing for kidney disease.  N Engl J Med. 2019;380(2):142-151. doi:10.1056/NEJMoa1806891PubMedGoogle ScholarCrossref
40.
Mann  N, Braun  DA, Amann  K,  et al.  Whole-exome sequencing enables a precision medicine approach for kidney transplant recipients.  J Am Soc Nephrol. 2019;30(2):201-215. doi:10.1681/ASN.2018060575PubMedGoogle ScholarCrossref
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Original Investigation
March 20, 2019

Risk of Cardiovascular Disease and Mortality in Young Adults With End-stage Renal Disease: An Analysis of the US Renal Data System

Author Affiliations
  • 1Division of Pediatric Nephrology, Department of Pediatrics, University of Michigan, Ann Arbor
  • 2Susan B. Meister Child Health Evaluation and Research Center, University of Michigan, Ann Arbor
  • 3Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor
  • 4Arbor Research Collaborative for Health, Ann Arbor, Michigan
  • 5National Institute of Health, National Institute of Diabetes and Digestive and Kidney Diseases, Kidney Urology and Epidemiology, Bethesda, Maryland
  • 6Division of Cardiology, Department of Internal Medicine, University of Michigan, Ann Arbor
  • 7Michigan Integrated Center for Health Analytics & Medical Prediction, University of Michigan, Ann Arbor
  • 8Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor
  • 9Kidney Epidemiology and Cost Center, School of Public Health, University of Michigan, Ann Arbor
  • 10Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor
JAMA Cardiol. 2019;4(4):353-362. doi:10.1001/jamacardio.2019.0375
Key Points

Question  Do young adults with incident end-stage renal disease (ESRD) have different risks for cardiovascular disease than patients in younger age groups?

Findings  In this population-based cohort study, young adults with incident ESRD have a 1-year cardiovascular hospitalization rate and 5-year mortality probability higher than those of children and adolescents with incident ESRD. Their risk of hospitalization and mortality owing to cardiovascular disease is more comparable with older adults than children.

Meaning  Per this analysis, young adults with incident ESRD have cardiovascular disease risks and outcomes that differ from other age groups, suggesting a need for age-appropriate research and management strategies.

Abstract

Importance  Cardiovascular disease (CVD) is a leading cause of death among patients with end-stage renal disease (ESRD). Young adult (ages 22-29 years) have risks for ESRD-associated CVD that may vary from other ages.

Objective  To test the hypothesis that young adult–onset ESRD is associated with higher cardiovascular (CV) hospitalizations and mortality with different characteristics than childhood-onset disease.

Design, Setting, and Participants  This population-based cohort study used the US Renal Data System to categorize patients who initiated ESRD care between 2003 and 2013 by age at ESRD onset (1-11, 12-21, and 22-29 years). Cardiovascular hospitalizations were identified via International Classification of Diseases, Ninth Revision discharge codes and CV mortality from the Centers for Medicare & Medicaid ESRD Death Notification Form. Patients were censored at death from non-CVD events, loss to follow-up, recovery, or survival to December 31, 2014. Adjusted proportional hazard models (95% CI) were fit to determine risk of CV hospitalization and mortality by age group. Data analysis occurred from May 2016 and December 2017.

Exposures  Onset of ESRD.

Main Outcomes and Measures  Cardiovascular mortality and hospitalization.

Results  A total of 33 156 patients aged 1 to 29 years were included in the study population. Young adults (aged 22-29 years) had a 1-year CV hospitalization rate of 138 (95% CI, 121-159) per 1000 patient-years. Young adults had a higher risk for CV hospitalization than children (aged 1-11 years; hazard ratio [HR], 0.41 [95% CI, 0.26-0.64]) and adolescents (aged 12-21 years; HR, 0.86 [95% CI, 0.77-0.97]). Of 4038 deaths in young adults, 1577 (39.1%) were owing to CVD. Five-year cumulative incidence of mortality in this group (7.3%) was higher than in younger patients (adolescents, 4.0%; children, 1.7%). Adjusted HRs for CV mortality were higher for young adults with all causes of ESRD than children (cystic, hereditary, and congenital conditions: HR, 0.22 [95% CI, 0.11-0.46]; P < .001; glomerulonephritis: HR, 0.21 [95% CI, 0.10-0.44]; P < .001; other conditions: HR, 0.33 [95% CI, 0.23-0.49]; P < .001). Adolescents had a lower risk for CV mortality than young adults for all causes of ESRD except glomerulonephritis (cystic, hereditary, and congenital conditions: HR, 0.45 [95% CI, 0.27-0.74]; glomerulonephritis: HR, 0.99 [95% CI, 0.76-1.11]; other: HR, 0.47 [95% CI, 0.40-0.57]). Higher risks for CV hospitalization and mortality were associated with lack of preemptive transplant compared with hemodialysis (hospital: HR, 14.24 [95% CI, 5.92-34.28]; mortality: HR, 13.64 [95% CI, 8.79-21.14]) and peritoneal dialysis [hospital: HR, 8.47 [95% CI, 3.50-20.53]; mortality: HR, 7.86 [95% CI, 4.96-12.45]). Nephrology care before ESRD was associated with lower risk for CV mortality (HR, 0.77 [95% CI, 0.70-0.85]).

Conclusions and Relevance  Cardiovascular disease accounted for nearly 40% of deaths in young adults with incident ESRD in this cohort. Identified risk factors may inform development of age-appropriate ESRD strategies to improve the CV health of this population.

Introduction

Cardiovascular disease (CVD) is a leading cause of morbidity and mortality in children and adults with end-stage renal disease (ESRD).1 In adults 45 years or older, 87% have CVD reported at the time of ESRD onset, and approximately 50% of deaths are attributed to CVD.2 In children with ESRD, mortality is attributed to CVD in 23% of children in the United States and up to 50% in other countries.3,4 While early investigations provide insight into the CVD-associated health status of children and adults with ESRD, they do not adequately describe the burden of CVD in young adults (ages 22-29 years) with ESRD.

Over the last decade, research has shown that young adults exhibit distinct characteristics in the incidence and prevalence of chronic illnesses. These individuals experience unique challenges as they transition into adulthood, with potential lapses in medical care, psychosocial maturation, and transition from student to employee status, which may affect health outcomes. Recognizing the unique epidemiology of this population, the US Renal Data System Annual Data Report has recently begun to report surveillance data specific to the young adult ESRD population.2 This report shows that young adults with incident ESRD in the United States form a unique population that has different disease cause of ESRD, comorbidities, pre-ESRD kidney disease duration, and health outcomes than older adults and children.5,6 To improve the outcomes of these patients, it is critical to understand the contributions of potentially modifiable risk factors such as CVD.

In this work, we have sought to improve understanding of CVD morbidity and mortality in young adults with incident ESRD with the goal of informing the development of age-appropriate ESRD treatment and patient management strategies. We hypothesized that young adult–onset disease would be associated with higher CVD hospitalizations and mortality compared with children and adolescents. Furthermore, we hypothesized that young adult–onset disease would be associated with a different set of risk factors than childhood-onset disease.

Methods

This study was conducted as part of the US Renal Data System Coordinating Center contract with the National Institutes of Health. This study was conducted as a part of the US Renal Data System Coordinating Center contract with the National Institutes of Health and was approved by the University of Michigan institutional review board. Patient consent was not required because of the use of deidentified data.

Population and Data Source

Data were obtained from the US Renal Data System for all patients aged 1 to 29 years who started renal replacement therapy for ESRD between January 1, 2003, and December 31, 2013, including data available from the Centers for Medicaid and Medicare Services (CMS) Medical Evidence Form (form 2728). Hospitalization data were obtained from CMS claims data. Death information was obtained via CMS ESRD Death Notification Forms (form 2746).

Definitions

Based on age at ESRD incidence, the study population was categorized into young adults (aged 22-29 years), adolescents (12-21 years), and children (1-11 years), respectively. Hospitalizations for CVD were identified via International Classification of Diseases, Ninth Revision discharge codes (eTable 1 in the Supplement).7 Cause of CVD death was defined by the CMS ESRD Death Notification Form (eTable 2 in the Supplement).8 Body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) categories were defined per the US Centers for Disease Control and Prevention growth charts for patients aged 2 to 17 years (underweight, <5th percentile; normal, 5th-85th percentile; overweight, 85th-95th percentile; obese, >95th percentile).9,10 For patients younger than 2 years, World Health Organization growth charts were used per Centers for Disease Control and Prevention recommendations, with conversion from World Health Organization clinical practice guidelines (which regarded weights at less than the second percentile as underweight and greater than 98th percentile as overweight) to Centers for Disease Control and Prevention guidelines for consistency.10 For patients aged 18 to 29 years, BMI categories were defined as underweight (<18.5), normal (18.5-24.9), overweight (25-29.9), and obese (≥30.0).

Outcome Measures and Statistical Analysis

The primary outcomes were CVD hospitalization and CVD mortality. We computed 1-year, 3-year, and 5-year CVD hospitalization and CVD mortality rates adjusted by sex, race, ethnicity, and primary cause of ESRD. To allow for comparisons across other reported estimates, adjustments as described by Pickle and White11 and currently in use by the US Renal Data System Annual Data Report12 were used for computed rates with 2010-2011 as reference years. Trends of 5-year adjusted CVD hospitalization rates were also evaluated. An adjusted, modified Poisson model was fit to determine the risk of CVD hospitalizations in young adults, compared with patients who were aged 1 to 11 years and 12 to 21 years at ESRD onset.13 Modeling was also performed via the proportional rates model as a sensitivity analysis with similar results.14 We report the more conservative Poisson model results. Proportional hazards models were fitted to make covariate-adjusted comparisons of CVD mortality risk for young adults, adolescents, and children.15 Specifically, to account for competing risks, we modeled the cause-specific hazard of CVD death using Cox regression. The cumulative incidence of CVD mortality was estimated nonparametrically by integrating the cause-specific hazards of CVD and non-CVD death; confidence intervals were computed as derived by Lin.16,17 Unadjusted, demographic-adjusted, and final adjusted models are presented for CVD hospitalization and CVD mortality. Final models were adjusted for patient age at ESRD onset, sex, race, year of ESRD onset, type of renal replacement therapy at ESRD onset, pre-ESRD nephrology care, insurance status at ESRD onset, cause of ESRD, and pre-ESRD history and comorbidities (ie, heart failure, coronary artery and cardiac disease, other vascular disease, hypertension, and diabetes).

The CVD mortality model includes patient BMI categories at ESRD onset. Body mass index was not found to be significant in CVD hospitalization model. Owing to missing BMI data (2%), multiple imputation was used.

Given the inherent differences in the age groups, interactions between age and all variables, including cause of ESRD, were evaluated. Patients were followed up until they died, were lost to follow-up, recovered, or survived to the end of the study period (December 31, 2014). All analyses were conducted using SAS version 9.4 (SAS Institute). Data analysis occurred from May 2016 and December 2017.

Results

The study population included 33 156 patients with incident ESRD aged 1 to 29 years (eFigure in the Supplement). The incident ESRD study population included 20 245 young adults, 10 024 adolescents, and 2887 children. Of the study population of 33 156 individuals, 5357 (16.2%) died, 1195 (3.6%) were lost to follow-up, 2247 (6.8%) recovered, and 24 357 (73.4%) survived to the end of the study period. Demographic data are provided in Table 1. Young adults had the highest percentage of patients who were black, the lowest proportion of those with private insurance, the highest proportion uninsured, high rates of being overweight or obese, and were least likely to have received pre-ESRD nephrology care.

Most young adults had a cause of ESRD other than glomerulonephritis or congenital, hereditary, or cystic diseases. Of these other causes, unspecified hypertension or large vessel disease (4142 of 12 136 [34.1%]) and diabetes mellitus (4057 of 12 136 [30.4%]) were most common. Approximately two-thirds of those with ESRD secondary to diabetes had type 1 diabetes mellitus (n = 2656 of 4057 with diabetes). Hypertension (n = 15 791 of 20 245) and diabetes (n = 4537 of 20 245) were the most common comorbidities in young adults at ESRD onset.

Cardiovascular Hospitalization

In the 5 years after ESRD onset, the adjusted CVD hospitalization rate for young adults remained high and stable (Figure 1), converging with rising rates in adolescents. Rates in children remained low and stable over the follow-up period. The adjusted CVD hospitalization rates for young adults were 138 (95% CI, 121-159) at 1 year, 147 (95% CI, 134-162) at 3 years, and 146 (95% CI, 134-160) per 1000 patient-years at 5 years. These rates were significantly higher than the CVD hospitalization rates for adolescents of 74 (95% CI, 55-100), 102 (95% CI, 85-122), and 116 (95% CI, 98-137) per 1000 patient-years at 1, 3, and 5 years, respectively. The rates of CVD hospitalization for children were 48 (95% CI, 21-117), 37 (95% CI, 18-79), and 33 (95% CI, 14-78) per 1000 patient-years at 1, 3, and 5 years, respectively. Common causes of CVD hospitalization over 5 years in young adults included heart failure, coronary artery disease, arrhythmias, and valvular heart disease.

Hemodialysis (hazard ratio [HR], 14.24 [95% CI, 5.92-34.28]) and peritoneal dialysis (HR, 8.47 [95% CI, 3.50-20.53]) were associated with a higher risk of CVD hospitalization compared with preemptive transplant. Other factors associated with CVD hospitalization included age older than 21 years at ESRD onset (age 22-29 years: reference; 12-21 years: HR, 0.86 [95% CI, 0.77-0.97]; P < .001; 1-11 years: HR, 0.41 [95% CI, 0.26-0.64]; P < .001), black race (HR, 1.30 [95% CI, 1.19-1.43]; P < .001), female sex (1.12 [95% CI, 1.03-1.22]; P < .001), enrollment in public insurance or no insurance (vs private insurance) at ESRD onset (public insurance: HR, 1.48 [95% CI, 1.28-1.72]; P < .001; no insurance: HR, 1.27 [95% CI, 1.08-1.49]; P < .001), and ESRD cause other than congenital, hereditary, or cystic diseases (glomerulonephritis: HR, 1.34 [95% CI, 1.04-1.73; P < .001; other causes: HR, 1.37 [95% CI, 1.06-1.76; P < .001; Table 2). Prevalent comorbid factors at ESRD onset associated with higher risk of CVD hospitalization included heart failure (HR, 1.48 [95% CI, 1.32-1.66]; P < .001), coronary artery or cardiac disease (HR, 1.31 [95% CI, 1.13-1.50]; P < .001), hypertension (HR, 1.25 [95% CI, 1.11-1.42]; P < .001), and diabetes (HR, 1.34 [95% CI, 1.21-1.48]; P < .001). Patients had a higher risk of CVD hospitalization if they had a more recent year of ESRD onset (HR, 1.11 [95% CI, 1.08-1.13]).

Cardiovascular Mortality

The proportion of all-cause death observed among the study population was 16.2% (n = 5357), with CVD mortality accounting for 37.7% of the total deaths (n = 2019). Of these, CVD accounted for 1577 of 4038 young adult deaths (39.1%). The 5-year mortality cumulative incidence attributable to CVD causes relative to all-cause mortality was 7.3% in young adults, 4.0% in adolescents, and 1.7% in children (Figure 2). The rates of CVD mortality in young adults at 1, 3, and 5 years were significantly higher at 11 (95% CI, 8-14), 37 (95% CI, 27-47) and 70 (95% CI, 50-91) per 1000 patient-years compared with 8 (95% CI, 5-11), 29 (95% CI, 19-39), and 65 (95% CI, 40-89) per 1000 patient-years in adolescents and 10 (95% CI, 4-17), 22 (95% CI, 8-35), and 42 (95% CI, 9-74) per 1000 patient-years in children, respectively.

Unadjusted and adjusted proportional hazards for CVD mortality are presented in Table 3. Dialysis (hemodialysis: HR, 13.64 [95% CI, 8.79-21.14]; P < .001; peritoneal dialysis: HR, 7.86 [95% CI, 4.96-12.45]; P < .001) as renal replacement therapy had a higher risk of CVD mortality compared with preemptive transplant. Other factors associated with higher risk of CVD mortality included female sex (HR, 1.28 [95% CI, 1.16-1.41]; P < .001), black race (HR, 2.04 [95% CI, 1.84-2.26]; P < .001), lack of private insurance (public insurance: HR, 2.25 [95% CI, 2.00-2.53]; P < .001; no insurance: HR, 1.65 [95% CI, 1.42-1.91]; P < .001), and low BMI (HR, 1.40 [95% CI, 1.17-1.68]; P < .001). A more recent year of ESRD onset was associated with a lower risk of CVD mortality (HR, 0.67 [95% CI, 0.65-0.69]). Comorbid factors at ESRD onset that were associated with higher risk of CVD mortality included heart failure, coronary artery or cardiac disease, other vascular disease, and diabetes mellitus. The presence of pre-ESRD nephrology care was associated with greater CVD mortality-free survival (HR, 0.77 [95% CI, 0.70-0.85]; P < .001). An interaction between age and cause of ESRD was observed. Young adults had a higher risk of CVD mortality when the cause of ESRD was congenital, hereditary, or cystic diseases (young adults: reference; adolescents: HR, 0.45 [95% CI, 0.27-0.74]; P = .002; children: HR, 0.22 [95% CI, 0.11-0.46]; P < .001) and other primary causes for ESRD (young adults: reference; adolescents: HR, 0.47 [95% CI, 0.40-0.57]; P < .001; children: HR, 0.33 [95% CI, 0.23-0.49]; P < .001). Among patients with glomerulonephritis, children had a lower risk of CVD mortality than young adults (HR, 0.21 [95% CI, 0.10-0.44]; P < .001), with no difference between young adults and adolescents.

Discussion

We analyzed CVD morbidity and mortality in young adults with ESRD in a 10-year incident cohort of US patients. Young adults had a significantly higher CVD burden with higher 1-year, 3-year, and 5-year CVD hospitalization rates and a higher 5-year CVD mortality probability than younger age groups with ESRD. Based on these results, young adults with incident ESRD had a 143 to 500 times higher risk for CVD mortality than the age-matched general population.18 We show that young adults began ESRD care with a higher burden of preexisting CVD and CVD risk factors, which may present a target for earlier intervention to improve outcomes.

To improve outcomes in young adults, a crucial first step is to delineate the morbidity and mortality relative to children and older adults. This study shows that the young adult population has distinct CVD-associated outcomes. The 1-year CVD hospitalization rates were incrementally higher among older age groups than among children, adolescents, and young adults with ESRD. These rates were lower than published 1-year CVD hospitalization rates among adults with ESRD (400 per 1000 patient-years).2 A different pattern was seen concerning CVD mortality. Low 1-year CVD mortality rates were observed for children, adolescents, and young adults compared with the much higher published 1-year CVD mortality rate for adults with ESRD of 79 per 1000 patient-years.19 In comparison, the rates of overall mortality attributed to CVD in the general population are less than 2.2 per 100 000 population for patients younger than 25 years, 7.7 for those aged 25 to 34 years, and 16.7 for the overall US adult population.18 These differences demonstrate the profound nature of CVD affecting young adults with ESRD.

This study also highlights the substantial effect of CVD, with 37.7% of total deaths attributed to CVD causes in patients with ESRD who had started renal replacement therapy between ages 1 and 29 years. Previous studies have only evaluated young adults with ESRD onset as children, reporting CVD mortality between 40% and 54%.3,4,20 The current analysis expands on previously published data by explicitly evaluating those with incident ESRD between 20 and 29 years of age, in whom 39% of deaths resulted from CVD. These findings suggest a similar proportion of CVD death in young adults with incident ESRD compared with the previously published findings in cohorts of adult survivors of childhood-onset ESRD. This is despite the presumably longer ESRD course and its associated CVD risks for childhood-onset patients. Differences in comorbidities, ESRD cause by age, pre-ESRD care, and subsequent ESRD management associated with ESRD onset in young adults requires additional study.

Several longitudinal trends in age-associated CVD hospitalization rates and mortality were observed. The rate of CVD hospitalization for children remained low over the course of 5 years relative to those of adolescents and young adults. This persistently low CVD hospitalization rate may have been because of the low prevalence of preceding CVD risk factors, CVD history, and (potentially) the short duration of kidney disease prior to ESRD.5 The convergence of the adolescent and young adult hospitalization rates may suggest a threshold pre-ESRD duration with kidney disease or a differential burden of other preexisting CVD risk factors. It may also reflect a period of vulnerability during age and health care transitions.

Multivariable analysis showed that preexisting coronary artery and cardiac disease, diabetes, and heart failure were associated with higher CVD hospitalizations and mortality, consistent with previous literature.21 Furthermore, we showed that the causes of ESRD might be linked to higher risk of CVD morbidity and mortality. In this study, glomerular diseases were associated with adolescent or young adult ESRD onset and a higher risk of CVD morbidity. It is possible that glomerular diseases increase systemic inflammation and thereby contribute to a higher CVD burden.22 This study suggests that these populations may benefit from greater attention to cardiovascular health and disease by clinicians and scientists focused on improving health outcomes.

We identified additional risk factors that may contribute to age-associated differences in CVD hospitalization and mortality, including black race and the type of renal replacement therapy at ESRD onset. The proportion of patients who were black in the comparison groups was higher with age. Although CVD mortality has not previously been investigated in young adults with incident ESRD, the association between black race and risk of CVD mortality is consistent with studies of all-cause mortality in children.23 However, these results contrast with studies that consistently show a 45% lower risk of all-cause mortality while on dialysis among black American adults compared with white American adults.24 Some have reported that overall lower survival in black children with ESRD may be explained by disparities in transplants.25 To our knowledge, the literature does not include evaluations of this population for CVD morbidity and mortality; however, in this study, black race remained a risk for CVD mortality independent of the type of renal replacement therapy patients received. We did not have access to genotyping data, which may, in future studies, help illuminate any potential contribution to this excess risk in black Americans based on genetic risks.26,27

The differential use of type of renal replacement therapy by age at ESRD onset may also play a role in CVD hospitalization and mortality. Initiation of ESRD care with transplant was lower with increasing age in this study, including only 5.6% of young adults. The multivariate analysis provides evidence consistent with prior publications in that initiation of renal replacement therapy with dialysis was a strong risk factor for subsequent CVD hospitalization and death.1,28 Providing health care that enables preemptive transplant for young adult patients may therefore help improve these outcomes.

Lack of private insurance at ESRD onset was associated with higher risk of CVD hospitalization in this study. Young adults had the lowest private insurance coverage (7256 of 20 245 [37.2%]) and were the highest proportion of patients without insurance (4796 of 20 245 [23.7%]). The percentage of uninsured patients in the general US population is 13% for adults (18-64 years old) and 5% for children, while the percentage of public insurance is 20% in adults and 40% in children.29,30 Lack of adequate insurance prior to ESRD onset implies limited access to pre-ESRD care, with concomitant risks of delayed diagnosis and insufficient opportunity to provide optimal management during times of less severe kidney disease. In this study, lack of pre-ESRD nephrology care was associated with increased mortality, further highlighting importance of access to care. In another study, children and adults with ESRD had 3.6-times higher odds of having at least 12 months of pre-ESRD nephrology care if the patient had insurance coverage and had lower mortality with longer pre-ESRD care.31 Additionally, adults with Medicaid or no insurance have been shown to be less likely to receive early nephrology care, be waitlisted for transplant within 1 year of ESRD onset, and receive a transplant within 1 year of ESRD onset.32 This second study reported that adults in states with broader Medicaid coverage had a lower incidence of ESRD and fewer gaps in insurance coverage.

This study found no difference in survival for patients with elevated BMI, in contrast with the well-documented obesity paradox in adult ESRD.33 These results are more consistent with the pediatric literature.34 In the setting of pediatric ESRD, low BMI may be considered a marker of poor nutritional status and has been shown to be associated with increased mortality in other conditions.35,36 Children with decreasing BMI have increased risk of developing ESRD.37 Low BMI at ESRD onset was associated with increased CVD mortality in this study. This may suggest cardiovascular benefit of pre-ESRD nephrology care and the access to nutritional support that it would imply. Strengths of this study are its sample size, national database, and linked hospitalization data.

Limitations

We do acknowledge limitations. This study did not include patients with ESRD onset before age 1 year or adults older than 29 years. Given inherent limitations of the data set, not all potential risk factors were available for analysis. This study was limited by lack of access to data on the use of antihypertensive or lipid-lowering medications and detailed information regarding pre-ESRD management. Our evaluation of cause, comorbidity, and cause of death has acknowledged limitations common to all studies using administrative health care data. Specifically, reported ESRD causative processes can be subject to misclassification, which has been noted in previous studies.38-40

Conclusions

Young adults form a unique population that share features of both adult and pediatric ESRD, requiring specialized clinical and research attention to improve outcomes. Cardiovascular diseases mortality accounts for almost 40% of all deaths in patients with ESRD, beginning in young adulthood, and is up to 500 times the rates documented in the general population.18 Potentially modifiable risk factors for this young adult population may include optimizing health care for the underlying kidney disease and other coexisting conditions before the onset of ESRD and increasing access to preemptive transplant. Both modifiable and nonmodifiable risk factors, including race and sex, provide additional opportunities to explore the genetic, biologic, environmental, and social determinants of the observed differential CVD mortality. These findings provide the basis for continued rigorous evaluation of CVD disease in young adults with incident ESRD. Identified modifiable risk factors may also be future targets for interventions. Together, these steps may lead to improved implementation of age-appropriate treatment and patient management strategies and overall cardiovascular health of this unique population.

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Article Information

Accepted for Publication: January 24, 2019.

Corresponding Author: Zubin J. Modi, MD, Division of Pediatric Nephrology, Department of Pediatrics, University of Michigan, 1540 E Hospital Dr, SPC 4297, Ann Arbor, MI 48109 (modiz@med.umich.edu).

Published Online: March 20, 2019. doi:10.1001/jamacardio.2019.0375

Author Contributions: Dr Modi and Mr Ji 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. Drs Modi and Lu are co–first authors. Drs Saran and Gipson are co–senior authors.

Concept and design: Modi, Lu, Kapke, Selewski, Dietrich, Schaubel, Saran, Gipson.

Acquisition, analysis, or interpretation of data: Modi, Ji, Kapke, Selewski, Dietrich, Abbott, Nallamothu, Schaubel, Saran, Gipson.

Drafting of the manuscript: Modi, Lu, Selewski, Dietrich, Schaubel, Gipson.

Critical revision of the manuscript for important intellectual content: Modi, Ji, Kapke, Selewski, Abbott, Nallamothu, Schaubel, Saran, Gipson.

Statistical analysis: Modi, Lu, Ji, Kapke, Dietrich, Schaubel.

Obtained funding: Saran.

Administrative, technical, or material support: Modi, Selewski, Abbott, Schaubel, Saran, Gipson.

Supervision: Selewski, Schaubel, Saran, Gipson.

Conflict of Interest Disclosures: Dr Selewski reports receiving grants from the National Institute of Diabetes and Digestive and Kidney Diseases during the conduct of the study. Dr Nallamothu reports receiving grants from the National Institutes of Health during the conduct of the study, as well as funding from the National Institutes of Health, Veterans Affairs Health Services Research and Development Service, and the American Heart Association; he also reports receiving compensation as an editor of Circulation: Cardiovascular Quality & Outcomes, a journal of the American Heart Association and being a coinventor on US utility patent US15/356,012 (US20170148158A1), which uses software technology with signal processing and machine learning to automate the reading of coronary angiograms, is held by the University of Michigan, and is licensed to AngioAid Inc, in which Dr Nallamothu holds ownership shares. Dr Gipson reports receiving grants from National Institute of Diabetes and Digestive and Kidney Diseases during the conduct of the study. No other disclosures were reported.

Funding/Support: The data reported here have been supplied by the US Renal Data System, which is funded by the National Institute of Diabetes and Digestive and Kidney Diseases, through National Institutes of Health contract HHSN276201400001C (project officer: Dr Abbott). The US Renal Data System Coordinating Center is located at the Kidney Epidemiology and Cost Center, University of Michigan (Dr Saran), in partnership with Arbor Research Collaborative for Health. Further support was received via the National Institutes of Health (T32 research training grant 5T32DK007378-38 [Dr Modi] and grant R01-DK070869 [Dr Schaubel]).

Role of the Funder/Sponsor: The National Institute of Diabetes and Digestive and Kidney Diseases program officer Dr Abbott is an author of this study, and his role is described in the Author Contributions section. Except as indicated, the funder 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: The interpretation and reporting of these data are the responsibility of the authors and in no way should be seen as an official policy or interpretation of the US government.

Meeting Presentation: Preliminary results of this study were presented as an abstract at the American Society of Nephrology Kidney Week 2016; Chicago, Illinois; November 17, 2016.

Additional Contributions: We thank Janet Leslie, MS, and Ruth Shamraj, MA, Kidney Epidemiology and Cost Center, University of Michigan, for their editorial review of the manuscript. We also thank April Wyncott, MPH, MBA, and Vivian Kurtz, MPH, the US Renal Data System Coordinating Center, for project coordination of US Renal Data System program and research study. These contributions were a part of their employment responsibilities as part of the US Renal Data System project team.

References
1.
Foley  RN, Parfrey  PS, Sarnak  MJ.  Clinical epidemiology of cardiovascular disease in chronic renal disease.  Am J Kidney Dis. 1998;32(5)(suppl 3):S112-S119. doi:10.1053/ajkd.1998.v32.pm9820470PubMedGoogle ScholarCrossref
2.
Saran  R, Robinson  B, Abbott  KC,  et al.  US Renal Data System 2016 annual data report: epidemiology of kidney disease in the United States.  Am J Kidney Dis. 2017;69(3)(suppl 1):A7-A8. doi:10.1053/j.ajkd.2016.12.004PubMedGoogle ScholarCrossref
3.
Groothoff  JW.  Long-term outcomes of children with end-stage renal disease.  Pediatr Nephrol. 2005;20(7):849-853. doi:10.1007/s00467-005-1878-9PubMedGoogle ScholarCrossref
4.
McDonald  SP, Craig  JC; Australian and New Zealand Paediatric Nephrology Association.  Long-term survival of children with end-stage renal disease.  N Engl J Med. 2004;350(26):2654-2662. doi:10.1056/NEJMoa031643PubMedGoogle ScholarCrossref
5.
Wong  CJ, Moxey-Mims  M, Jerry-Fluker  J, Warady  BA, Furth  SL.  CKiD (CKD in children) prospective cohort study: a review of current findings.  Am J Kidney Dis. 2012;60(6):1002-1011. doi:10.1053/j.ajkd.2012.07.018PubMedGoogle ScholarCrossref
6.
Diaz-Gonzalez de Ferris  ME.  Adolescents and emerging adults with chronic kidney disease: their unique morbidities and adherence issues.  Blood Purif. 2011;31(1-3):203-208. doi:10.1159/000321854PubMedGoogle ScholarCrossref
7.
Centers for Medicare and Medicaid Services. ICD-9-CM diagnosis and procedure codes: abbreviated and full code titles. https://www.cms.gov/Medicare/Coding/ICD9ProviderDiagnosticCodes/codes.html. Published 2014. Accessed February 17, 2017.
8.
Centers for Medicare and Medicaid Services. Details for title: CMS 2746. https://www.cms.gov/Medicare/CMS-Forms/CMS-Forms/CMS-Forms-Items/CMS008869.html. Published 2016. Accessed February 14, 2019.
9.
Centers for Disease Control and Prevention. A SAS program for the 2000 CDC growth charts (ages 0 to <20 years). https://www.cdc.gov/nccdphp/dnpao/growthcharts/resources/sas.htm. Published 2016. Accessed February 8, 2017.
10.
Grummer-Strawn  LM, Reinold  C, Krebs  NF; Centers for Disease Control and Prevention (CDC).  Use of World Health Organization and CDC growth charts for children aged 0-59 months in the United States.  MMWR Recomm Rep. 2010;59(RR-9):1-15.PubMedGoogle Scholar
11.
Pickle  LW, White  AA.  Effects of the choice of age-adjustment method on maps of death rates.  Stat Med. 1995;14(5-7):615-627. doi:10.1002/sim.4780140519PubMedGoogle ScholarCrossref
12.
United States Renal Data System, National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases. 2018 ADR chapters. https://www.usrds.org/2018/view/Default.aspx. Published 2018. Accessed February 11, 2019.
13.
Zou  GY, Donner  A.  Extension of the modified Poisson regression model to prospective studies with correlated binary data.  Stat Methods Med Res. 2013;22(6):661-670. doi:10.1177/0962280211427759PubMedGoogle ScholarCrossref
14.
Amorim  LD, Cai  J.  Modelling recurrent events: a tutorial for analysis in epidemiology.  Int J Epidemiol. 2015;44(1):324-333. doi:10.1093/ije/dyu222PubMedGoogle ScholarCrossref
15.
Cox  DR.  Regression models and life-tables.  J R Stat Soc B. 1972;34(2):187-202.Google Scholar
16.
Kalbfleisch  JD, Prentice  RL.  The Statistical Analysis of Failure Time Data. New York, NY: Wiley; 2002. doi:10.1002/9781118032985
17.
Lin  DY.  Non-parametric inference for cumulative incidence functions in competing risks studies.  Stat Med. 1997;16(8):901-910. doi:10.1002/(SICI)1097-0258(19970430)16:8<901::AID-SIM543>3.0.CO;2-MPubMedGoogle ScholarCrossref
18.
Kochanek  KD, Murphy  SL, Xu  J, Tejada-Vera  B.  Deaths: final data for 2014, national vital statistics reports: from the Centers for Disease Control and Prevention, National Center for Health Statistics.  National Vital Statistics System. 2016;65(4):1-122.Google Scholar
19.
Saran  R, Li  Y, Robinson  B,  et al.  US Renal Data System 2014 annual data report: epidemiology of kidney disease in the United States.  Am J Kidney Dis. 2015;66(1)(suppl 1):S1-S305. doi:10.1053/j.ajkd.2015.05.001PubMedGoogle ScholarCrossref
20.
Parekh  RS, Carroll  CE, Wolfe  RA, Port  FK.  Cardiovascular mortality in children and young adults with end-stage kidney disease.  J Pediatr. 2002;141(2):191-197. doi:10.1067/mpd.2002.125910PubMedGoogle ScholarCrossref
21.
Stack  AG, Bloembergen  WE.  A cross-sectional study of the prevalence and clinical correlates of congestive heart failure among incident US dialysis patients.  Am J Kidney Dis. 2001;38(5):992-1000. doi:10.1053/ajkd.2001.28588PubMedGoogle ScholarCrossref
22.
Nelson  CL, Karschimkus  CS, Dragicevic  G,  et al.  Systemic and vascular inflammation is elevated in early IgA and type 1 diabetic nephropathies and relates to vascular disease risk factors and renal function.  Nephrol Dial Transplant. 2005;20(11):2420-2426. doi:10.1093/ndt/gfi067PubMedGoogle ScholarCrossref
23.
Weaver  DJ  Jr, Somers  MJG, Martz  K, Mitsnefes  MM.  Clinical outcomes and survival in pediatric patients initiating chronic dialysis: a report of the NAPRTCS registry.  Pediatr Nephrol. 2017;32(12):2319-2330. doi:10.1007/s00467-017-3759-4PubMedGoogle ScholarCrossref
24.
Agodoa  L, Eggers  P.  Racial and ethnic disparities in end-stage kidney failure-survival paradoxes in African-Americans.  Semin Dial. 2007;20(6):577-585. doi:10.1111/j.1525-139X.2007.00350.xPubMedGoogle ScholarCrossref
25.
Ku  E, McCulloch  CE, Grimes  BA, Johansen  KL.  Racial and ethnic disparities in survival of children with ESRD.  J Am Soc Nephrol. 2017;28(5):1584-1591. doi:10.1681/ASN.2016060706PubMedGoogle ScholarCrossref
26.
Ma  L, Langefeld  CD, Comeau  ME,  et al.  APOL1 renal-risk genotypes associate with longer hemodialysis survival in prevalent nondiabetic African American patients with end-stage renal disease.  Kidney Int. 2016;90(2):389-395. doi:10.1016/j.kint.2016.02.032PubMedGoogle ScholarCrossref
27.
McLean  NO, Robinson  TW, Freedman  BI.  APOL1 gene kidney risk variants and cardiovascular disease: getting to the heart of the matter.  Am J Kidney Dis. 2017;70(2):281-289. doi:10.1053/j.ajkd.2016.11.020PubMedGoogle ScholarCrossref
28.
Tonelli  M, Wiebe  N, Knoll  G,  et al.  Systematic review: kidney transplantation compared with dialysis in clinically relevant outcomes.  Am J Transplant. 2011;11(10):2093-2109. doi:10.1111/j.1600-6143.2011.03686.xPubMedGoogle ScholarCrossref
29.
Kaiser Family Foundation. Health insurance coverage of adults 19-64. https://www.kff.org/other/state-indicator/adults-19-64/. Published 2017. Accessed February 11, 2019.
30.
Kaiser Family Foundation. Health insurance coverage of children 0-18. https://www.kff.org/other/state-indicator/children-0-18/. Published 2017. Accessed February 11, 2019.
31.
Gillespie  BW, Morgenstern  H, Hedgeman  E,  et al.  Nephrology care prior to end-stage renal disease and outcomes among new ESRD patients in the USA.  Clin Kidney J. 2015;8(6):772-780. doi:10.1093/ckj/sfv103PubMedGoogle ScholarCrossref
32.
Kurella-Tamura  M, Goldstein  BA, Hall  YN, Mitani  AA, Winkelmayer  WC.  State medicaid coverage, ESRD incidence, and access to care.  J Am Soc Nephrol. 2014;25(6):1321-1329. doi:10.1681/ASN.2013060658PubMedGoogle ScholarCrossref
33.
Kalantar-Zadeh  K, Block  G, Humphreys  MH, Kopple  JD.  Reverse epidemiology of cardiovascular risk factors in maintenance dialysis patients.  Kidney Int. 2003;63(3):793-808. doi:10.1046/j.1523-1755.2003.00803.xPubMedGoogle ScholarCrossref
34.
Ku  E, Glidden  DV, Hsu  CY, Portale  AA, Grimes  B, Johansen  KL.  Association of body mass index with patient-centered outcomes in children with ESRD.  J Am Soc Nephrol. 2016;27(2):551-558. doi:10.1681/ASN.2015010008PubMedGoogle ScholarCrossref
35.
Caulfield  LE, de Onis  M, Blössner  M, Black  RE.  Undernutrition as an underlying cause of child deaths associated with diarrhea, pneumonia, malaria, and measles.  Am J Clin Nutr. 2004;80(1):193-198. doi:10.1093/ajcn/80.1.193PubMedGoogle ScholarCrossref
36.
Bechard  LJ, Duggan  C, Touger-Decker  R,  et al.  Nutritional status based on body mass index is associated with morbidity and mortality in mechanically ventilated critically ill children in the PICU.  Crit Care Med. 2016;44(8):1530-1537. doi:10.1097/CCM.0000000000001713PubMedGoogle ScholarCrossref
37.
Ku  E, Kopple  JD, McCulloch  CE,  et al.  Associations between weight loss, kidney function decline, and risk of ESRD in the chronic kidney disease in children (CKiD) cohort study.  Am J Kidney Dis. 2018;71(5):648-656. doi:10.1053/j.ajkd.2017.08.013PubMedGoogle ScholarCrossref
38.
Neild  GH.  Primary renal disease in young adults with renal failure.  Nephrol Dial Transplant. 2010;25(4):1025-1032. doi:10.1093/ndt/gfp653PubMedGoogle ScholarCrossref
39.
Groopman  EE, Marasa  M, Cameron-Christie  S,  et al.  Diagnostic utility of exome sequencing for kidney disease.  N Engl J Med. 2019;380(2):142-151. doi:10.1056/NEJMoa1806891PubMedGoogle ScholarCrossref
40.
Mann  N, Braun  DA, Amann  K,  et al.  Whole-exome sequencing enables a precision medicine approach for kidney transplant recipients.  J Am Soc Nephrol. 2019;30(2):201-215. doi:10.1681/ASN.2018060575PubMedGoogle ScholarCrossref
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