Clinical Characteristics of and Risk Factors for Chronic Kidney Disease Among Adults and Children: An Analysis of the CURE-CKD Registry | Chronic Kidney Disease | JAMA Network Open | JAMA Network
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Figure 1.  Prevalence of Chronic Kidney Disease (CKD) Category 3a, 3b, 4, and 5 in 2006 to 2009, 2010 to 2013, and 2014 to 2017
Prevalence of Chronic Kidney Disease (CKD) Category 3a, 3b, 4, and 5 in 2006 to 2009, 2010 to 2013, and 2014 to 2017

A, Unadjusted prevalence. B, Prevalence adjusted by age, sex, and race/ethnicity.

Figure 2.  Prevalence of Prescription Medication Use in Chronic Kidney Disease Categories 3a to 5 in 2006 to 2009, 2010 to 2013, 2014 to 2017
Prevalence of Prescription Medication Use in Chronic Kidney Disease Categories 3a to 5 in 2006 to 2009, 2010 to 2013, 2014 to 2017

ACE indicates angiotensin-converting enzyme; ARB, angiotensin receptor blocker; NSAID, nonsteroidal anti-inflammatory drug; PPI, proton pump inhibitor; and SGLT2, sodium-glucose cotransporter 2.

Table 1.  Characteristics of Adults With CKD in the CURE-CKD Registry
Characteristics of Adults With CKD in the CURE-CKD Registry
Table 2.  Characteristics of Children With CKD in the CURE-CKD Registry
Characteristics of Children With CKD in the CURE-CKD Registry
Table 3.  Prevalence of CKD Among Adults at PSJH and UCLA Healtha
Prevalence of CKD Among Adults at PSJH and UCLA Healtha
1.
Levin  A, Tonelli  M, Bonventre  J,  et al; ISN Global Kidney Health Summit participants.  Global kidney health 2017 and beyond: a roadmap for closing gaps in care, research, and policy.  Lancet. 2017;390(10105):1888-1917. doi:10.1016/S0140-6736(17)30788-2PubMedGoogle ScholarCrossref
2.
Jha  V, Garcia-Garcia  G, Iseki  K,  et al.  Chronic kidney disease: global dimension and perspectives.  Lancet. 2013;382(9888):260-272. doi:10.1016/S0140-6736(13)60687-XPubMedGoogle ScholarCrossref
3.
Bello  AK, Levin  A, Tonelli  M,  et al.  Assessment of global kidney health care status.  JAMA. 2017;317(18):1864-1881. doi:10.1001/jama.2017.4046PubMedGoogle ScholarCrossref
4.
United States Renal Data System.  2018 USRDS Annual Data Report: Epidemiology of Kidney Disease in the United States. Bethesda, MD: National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases; 2018.
5.
Xie  Y, Bowe  B, Mokdad  AH,  et al.  Analysis of the Global Burden of Disease study highlights the global, regional, and national trends of chronic kidney disease epidemiology from 1990 to 2016.  Kidney Int. 2018;94(3):567-581. doi:10.1016/j.kint.2018.04.011PubMedGoogle ScholarCrossref
6.
Bowe  B, Xie  Y, Li  T,  et al.  Changes in the US burden of chronic kidney disease from 2002-2016: an analysis of the Global Burden of Disease study.  JAMA Netw Open. 2018;1(7):e184412. doi:10.1001/jamanetworkopen.2018.4412PubMedGoogle Scholar
7.
Couser  WG, Remuzzi  G, Mendis  S, Tonelli  M.  The contribution of chronic kidney disease to the global burden of major noncommunicable diseases.  Kidney Int. 2011;80(12):1258-1270. doi:10.1038/ki.2011.368PubMedGoogle ScholarCrossref
8.
International Diabetes Federation. IDF Diabetes Atlas. https://www.diabetesatlas.org/en/. Accessed November 13, 2019.
9.
Alicic  RZ, Rooney  MT, Tuttle  KR.  Diabetic kidney disease: challenges, progress, and possibilities.  Clin J Am Soc Nephrol. 2017;12(12):2032-2045. doi:10.2215/CJN.11491116PubMedGoogle ScholarCrossref
10.
Thomas  MC, Cooper  ME, Zimmet  P.  Changing epidemiology of type 2 diabetes mellitus and associated chronic kidney disease.  Nat Rev Nephrol. 2016;12(2):73-81. doi:10.1038/nrneph.2015.173PubMedGoogle ScholarCrossref
11.
Fryar  CD, Ostchega  Y, Hales  CM, Zhang  G, Kruszon-Moran  D.  Hypertension prevalence and control among adults: United States, 2015-2016.  NCHS Data Brief. 2017-2016;2017(289):1-8.PubMedGoogle Scholar
12.
NCD Risk Factor Collaboration (NCD-RisC).  Worldwide trends in blood pressure from 1975 to 2015: a pooled analysis of 1479 population-based measurement studies with 19.1 million participants.  Lancet. 2017;389(10064):37-55. doi:10.1016/S0140-6736(16)31919-5PubMedGoogle ScholarCrossref
13.
Ali  MK, Bullard  KM, Saydah  S, Imperatore  G, Gregg  EW.  Cardiovascular and renal burdens of prediabetes in the USA: analysis of data from serial cross-sectional surveys, 1988-2014.  Lancet Diabetes Endocrinol. 2018;6(5):392-403. doi:10.1016/S2213-8587(18)30027-5PubMedGoogle ScholarCrossref
14.
Dharmarajan  SH, Bragg-Gresham  JL, Morgenstern  H,  et al; US Centers for Disease Control and Prevention CKD Surveillance System.  State-level awareness of chronic kidney disease in the US.  Am J Prev Med. 2017;53(3):300-307. doi:10.1016/j.amepre.2017.02.015PubMedGoogle ScholarCrossref
15.
Tuot  DS, Diamantidis  CJ, Corbett  CF,  et al.  The last mile: translational research to improve CKD outcomes.  Clin J Am Soc Nephrol. 2014;9(10):1802-1805. doi:10.2215/CJN.04310514PubMedGoogle ScholarCrossref
16.
Boulware  LE, Troll  MU, Jaar  BG, Myers  DI, Powe  NR.  Identification and referral of patients with progressive CKD: a national study.  Am J Kidney Dis. 2006;48(2):192-204. doi:10.1053/j.ajkd.2006.04.073PubMedGoogle ScholarCrossref
17.
US Department of Health and Human Services. Advancing American kidney health. https://aspe.hhs.gov/pdf-report/advancing-american-kidney-health. Accessed July 20, 2019.
18.
Norris  K, Duru  OK, Alicic  RZ,  et al.  Rationale and design of a multicenter Chronic Kidney Disease (CKD) and at-risk for CKD electronic health records-based registry: CURE-CKD.  BMC Nephrol. In press.Google Scholar
19.
Equator Network. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. http://www.equator-network.org/reporting-guidelines/strobe. Accessed October 16, 2019.
20.
Matsushita  K, Mahmoodi  BK, Woodward  M,  et al; Chronic Kidney Disease Prognosis Consortium.  Comparison of risk prediction using the CKD-EPI equation and the MDRD study equation for estimated glomerular filtration rate.  JAMA. 2012;307(18):1941-1951. doi:10.1001/jama.2012.3954PubMedGoogle ScholarCrossref
21.
Kidney Disease: Improving Global Outcomes (KDIGO) Work Group. KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease. https://kdigo.org/wp-content/uploads/2017/02/KDIGO_2012_CKD_GL.pdf. Accessed November 13, 2019.
22.
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. doi:10.1681/ASN.2008030287PubMedGoogle ScholarCrossref
23.
Nichols  GA, Desai  J, Elston Lafata  J,  et al; SUPREME-DM Study Group.  Construction of a multisite DataLink using electronic health records for the identification, surveillance, prevention, and management of diabetes mellitus: the SUPREME-DM project.  Prev Chronic Dis. 2012;9:E110. doi:10.5888/pcd9.110311PubMedGoogle Scholar
24.
American Diabetes Association.  2. Classification and diagnosis of diabetes: Standards of Medical Care in Diabetes—2019 Diabetes Care. 2019;42(suppl 1):S13-S28. doi:10.2337/dc19-S002PubMedGoogle ScholarCrossref
25.
James  PA, Oparil  S, Carter  BL,  et al.  2014 evidence-based guideline for the management of high blood pressure in adults: report from the panel members appointed to the Eighth Joint National Committee (JNC 8).  JAMA. 2014;311(5):507-520. doi:10.1001/jama.2013.284427PubMedGoogle ScholarCrossref
26.
Navaneethan  SD, Jolly  SE, Schold  JD,  et al.  Development and validation of an electronic health record-based chronic kidney disease registry.  Clin J Am Soc Nephrol. 2011;6(1):40-49. doi:10.2215/CJN.04230510PubMedGoogle ScholarCrossref
27.
Bello  A, Hemmelgarn  B, Manns  B, Tonelli  M; Alberta Kidney Disease Network.  Use of administrative databases for health-care planning in CKD.  Nephrol Dial Transplant. 2012;27(suppl 3):iii12-iii18. doi:10.1093/ndt/gfs163PubMedGoogle Scholar
28.
Tuot  DS, McCulloch  CE, Velasquez  A,  et al.  Impact of a primary care CKD registry in a US public safety-net health care delivery system: a pragmatic randomized trial.  Am J Kidney Dis. 2018;72(2):168-177. doi:10.1053/j.ajkd.2018.01.058PubMedGoogle ScholarCrossref
29.
Liu  FX, Rutherford  P, Smoyer-Tomic  K, Prichard  S, Laplante  S.  A global overview of renal registries: a systematic review.  BMC Nephrol. 2015;16:31.PubMedGoogle ScholarCrossref
30.
Tu  K, Bevan  L, Hunter  K, Rogers  J, Young  J, Nesrallah  G.  Quality indicators for the detection and management of chronic kidney disease in primary care in Canada derived from a modified Delphi panel approach.  CMAJ Open. 2017;5(1):E74-E81.PubMedGoogle ScholarCrossref
31.
Nash  DM, Brimble  S, Markle-Reid  M,  et al.  Quality of care for patients with chronic kidney disease in the primary care setting: a retrospective cohort study from Ontario, Canada.  Can J Kidney Health Dis. 2017;4:2054358117703059. doi:10.1177/2054358117703059PubMedGoogle Scholar
32.
Bello  AK, Ronksley  PE, Tangri  N,  et al.  Quality of chronic kidney disease management in Canadian primary care.  JAMA Netw Open. 2019;2(9):e1910704. doi:10.1001/jamanetworkopen.2019.10704PubMedGoogle Scholar
33.
Go  AS, Chertow  GM, Fan  D, McCulloch  CE, Hsu  CY.  Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization.  N Engl J Med. 2004;351(13):1296-1305. doi:10.1056/NEJMoa041031PubMedGoogle ScholarCrossref
34.
McCullough  PA, Li  S, Jurkovitz  CT,  et al; KEEP Investigators.  Chronic kidney disease, prevalence of premature cardiovascular disease, and relationship to short-term mortality.  Am Heart J. 2008;156(2):277-283. doi:10.1016/j.ahj.2008.02.024PubMedGoogle ScholarCrossref
35.
Myers  OB, Pankratz  VS, Norris  KC, Vassalotti  JA, Unruh  ML, Argyropoulos  C.  Surveillance of CKD epidemiology in the US: a joint analysis of NHANES and KEEP.  Sci Rep. 2018;8(1):15900. doi:10.1038/s41598-018-34233-wPubMedGoogle ScholarCrossref
36.
Murphy  DP, Drawz  PE, Foley  RN.  Trends in angiotensin converting enzyme inhibitor and angiotensin II receptor blocker use among those with impaired kidney function in the United States.  J Am Soc Nephrol. 2019;30(7):1314-1321. doi:10.1681/ASN.2018100971PubMedGoogle ScholarCrossref
37.
Afkarian  M, Sachs  MC, Kestenbaum  B,  et al.  Kidney disease and increased mortality risk in type 2 diabetes.  J Am Soc Nephrol. 2013;24(2):302-308. doi:10.1681/ASN.2012070718PubMedGoogle ScholarCrossref
38.
Tangri  N, Kitsios  GD, Inker  LA,  et al.  Risk prediction models for patients with chronic kidney disease: a systematic review.  Ann Intern Med. 2013;158(8):596-603. doi:10.7326/0003-4819-158-8-201304160-00004PubMedGoogle ScholarCrossref
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    Original Investigation
    Nephrology
    December 20, 2019

    Clinical Characteristics of and Risk Factors for Chronic Kidney Disease Among Adults and Children: An Analysis of the CURE-CKD Registry

    Author Affiliations
    • 1Providence St Joseph Health, Providence Medical Research Center, Spokane, Washington
    • 2University of Washington School of Medicine, Seattle
    • 3Division of General Internal Medicine, Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles
    • 4Division of Nephrology, University of California, Los Angeles
    • 5Elson S. Floyd College of Medicine, Washington State University, Spokane
    • 6College of Pharmacy and Pharmaceutical Sciences, Washington State University, Spokane
    JAMA Netw Open. 2019;2(12):e1918169. doi:10.1001/jamanetworkopen.2019.18169
    Key Points español 中文 (chinese)

    Question  What are the clinical characteristics of and major risk factors for chronic kidney disease among patients in 2 large US health care systems?

    Findings  In this cohort study of the Center for Kidney Research, Education, and Hope (CURE-CKD) registry, more than 2.6 million adults and children had chronic kidney disease or were at risk. Albuminuria or proteinuria was tested in approximately one-eighth of adults with chronic kidney disease, renin-angiotensin system inhibitors were prescribed to one-fifth, and nonsteroidal anti-inflammatory agents or proton pump inhibitors were prescribed to more than one-third.

    Meaning  Despite common occurrence of chronic kidney disease, rates of identification and use of kidney protective agents were low, while use of potential nephrotoxins was widespread.

    Abstract

    Importance  Chronic kidney disease (CKD) is serious and common, yet recognition and public health responses are limited.

    Objective  To describe clinical features of, prevalence of, major risk factors for, and care for CKD among patients treated in 2 large US health care systems.

    Design, Setting, and Participants  This cohort study collected data from the Center for Kidney Disease Research, Education, and Hope (CURE-CKD) registry, an electronic health record–based registry jointly curated and sponsored by Providence St Joseph Health and the University of California, Los Angeles. Patients were adults and children with CKD (excluding end-stage kidney disease) and adults at risk of CKD (ie, with diabetes, hypertension, or prediabetes) identified by laboratory values, vital signs, prescriptions, and administrative codes. Data were collected from January 2006 through December 2017, with analyses performed from March 2019 through November 2019.

    Exposures  Diabetes, hypertension, and prediabetes.

    Main Outcomes and Measures  Clinical and demographic characteristics, prevalence, and prescribed medications.

    Results  Of 2 625 963 adults and children in the sample, 606 064 adults (23.1%) with CKD had a median (interquartile range [IQR]) age of 70 (59-81) years, with 338 785 women (55.9%) and 434 474 non-Latino white individuals (71.7%). A total of 12 591 children (0.4%) with CKD had a median (IQR) age of 6 (1-13) years, with 7079 girls (56.2%) and 6653 non-Latino white children (52.8%). Median (IQR) estimated glomerular filtration rate was 53 (41-61) mL/min/1.73 m2 among adults and 70 (50-95) mL/min/1.73 m2 in children. Prevalence rates for CKD in adults were 4.8% overall (606 064 of 12 669 700) with 1.6% (93 644 of 6 011 129) during 2006 to 2009, 5.7% (393 455 of 6 903 084) during 2010 to 2013, and 8.4% (683 574 of 8 179 860) during 2014 to 2017 (P < .001). A total of 226 693 patients (37.4%) had category 3a CKD; 100 239 (16.5%), category 3b CKD; 39 125 (6.5%), category 4 CKD; and 20 328 (3.4%), category 5 CKD. Among adults with CKD, albuminuria and proteinuria assessments were available in 52 551 (8.7%) and 25 035 (4.1%) patients, respectively. A renin-angiotensin system inhibitor was prescribed to 124 575 patients (20.6%), and 204 307 (33.7%) received nonsteroidal anti-inflammatory drugs or proton pump inhibitors. Of 1 973 258 adults (75.1%) at risk, one-quarter had diabetes or prediabetes (512 299 [26.0%]), nearly half had hypertension (955 812 [48.4%]), and one-quarter had both hypertension and diabetes or prediabetes (505 147 [25.6%]).

    Conclusions and Relevance  This registry-based cohort study revealed a burgeoning number of patients with CKD and its major risk factors. Rates of identification and use of kidney protective agents were low, while potential nephrotoxin use was widespread, underscoring the pressing need for practice-based improvements in CKD prevention, recognition, and treatment.

    Introduction

    Chronic kidney disease (CKD) is a serious and common disease, and it eventuates in multiple complications, including premature mortality and end-stage kidney disease (ESKD).1-3 An estimated 1 in 7 to 10 adults worldwide have CKD, with only approximately 10% surviving to ESKD and only half of survivors receiving dialysis or a kidney transplant because of lack of access or high costs.3 From 1990 to 2016, the prevalence of CKD increased by 90%, and related deaths, mainly due to cardiovascular diseases and infections, nearly doubled in the United States and globally.4-6 In high-income countries, 2% to 3% of annual health care costs are devoted to the 0.03% of the population with ESKD.7

    The increasing prevalence of CKD is closely tied to the increase of at-risk populations with diabetes, hypertension, and prediabetes. Indeed, diabetes is the leading cause of CKD and a global health emergency, with 425 million individuals affected worldwide in 2017 and a projected 629 million individuals affected by 2045.8-10 Hypertension is the second most frequent cause of CKD, affecting nearly one-third of US adults and 1.13 billion people globally in 2015.11,12 The estimated population size for prediabetes was 78.5 million among adults in the United States between 2011 and 2014, and nearly one-tenth have been reported with CKD.13 Even so, awareness of CKD and its major risk factors remains strikingly low among health care professionals and patients alike.14-16

    The Advancing American Kidney Health initiative was recently launched by a US executive order calling for new approaches to prevent and treat CKD, with a goal of reducing ESKD incidence 25% by 2030.17 Real-world data from electronic health records (EHRs) provide an ideal platform to answer this call by improving CKD detection, tracking, and public health responses. The Center for Kidney Disease Research, Education, and Hope (CURE-CKD) registry contains detailed patient-level EHR data from more than 2.6 million adults and children with CKD and at risk of CKD during 12 inclusive years.18 The objective of this study was to describe baseline clinical features of, prevalence of, major risk factors for, and care for CKD based on data from the CURE-CKD registry.

    Methods

    The study was approved by Providence St Joseph Health (PSJH; Washington, Montana, Oregon, Alaska, and California) and the University of California, Los Angeles (UCLA; California) Health institutional review boards, which determined that written informed consent was not required for this limited data set. Data use agreements between PSJH and UCLA Health formed the framework for data sharing, stewardship, and security. This study was conducted according to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline for cohort studies.19

    Study Design

    Detailed methodology for CURE-CKD has been previously published.18 The formation of CURE-CKD was supported by institutional funding from PSJH and UCLA Health. Both health care systems use Epic EHRs (Epic Systems). The first phase of CURE-CKD created a data repository with patient information from EHRs with at least 1 measure indicating CKD, diabetes, prediabetes, or hypertension based on patient-level laboratory values, vital signs, prescription medications, and administrative codes from January 1, 2006, to December 31, 2017. Electronic health record data for these patients were extracted from ambulatory and inpatient encounters. Unstructured data from the EHRs were not extracted. The total number of patients with encounters and serum creatinine measures for the health care systems was also recorded. Repository updates are performed annually.

    The second phase crafted an EHR-based registry of participants with CKD and at risk for CKD derived from the repository. The first 90 days a patient was included in the registry were considered the baseline period. Registry criteria were based on established clinical practice guidelines for CKD (eTable 1 in the Supplement). Adults (ie, aged ≥18 years) were included with 2 or more of the following laboratory measurements recorded at least 90 days apart: estimated glomerular filtration rate (eGFR) less than 60 mL/min/1.73 m2, calculated from serum creatinine levels using the Chronic Kidney Disease Epidemiology equation; urine albumin to creatinine ratio greater than 30 mg/g; and urine protein to creatinine ratio greater than 150 mg/g.20,21 Children (ie, aged <18 years) with CKD were identified using the same criteria, except the bedside Schwartz equation was used to calculate eGFR from serum creatinine levels.22 We identified CKD categories 1 and 2 by an administrative code, urine albumin to creatinine ratio greater than 30 mg/g, and/or urine protein to creatinine ratio greater than 150 mg/g. We identified CKD categories 3 to 5 based on eGFR and/or administrative code. Patients with ESKD treated with dialysis or kidney transplant were excluded. Participants with diabetes, prediabetes, and hypertension were identified by clinical practice guidelines and published criteria for EHR identification23-25 (eTable 1 in the Supplement).

    Statistical Analysis

    Data analyses were performed from March 2019 through November 2019. Continuous variables are reported as mean and SD or as median and interquartile range (IQR) for skewed or kurtotic distributions. Categorical variables are reported as frequencies and percentages. The Pearson χ2 test for independence was used to determine differences between categorical variables. Prevalence rates for CKD among adults are presented as a combined data set from PSJH and UCLA Health and by each system. To address sources of bias in CKD prevalence rates, data were analyzed as proportions based on the 3 following definitions for CKD: (1) CURE-CKD entry criteria, (2) 2 measurements of eGFR less than 60 mL/min/1.73 m2 at least 90 days apart; and (3) 1 measurement of eGFR less than 60 mL/min/1.73 m2. Serial prevalence rates of CKD overall, by categories, and prescription medication use during 3 periods (ie, 2006-2009, 2010-2013, and 2014-2017) were analyzed by logistic regression models. Prevalence was adjusted for age, sex, and race/ethnicity in the models using repository data (ie, CKD by 1 or 2 eGFR measurements). Adjustments could not be made for CKD prevalence with the denominator based on the total number of patients with encounters because the institutional review board approvals did not include data extraction for age, sex, and race/ethnicity from the total populations in the health care system.

    To reduce risk of type I error, a 2-tailed P < .001 was the a priori threshold for statistical significance because of the large sample size and resultant high level of statistical power. Because overall CKD participant characteristics, except distribution of geolocation, were similar between PSJH and UCLA Health, findings other than prevalence are presented from a jointly curated data set. Descriptive statistics and the Pearson χ2 test were conducted with SQL Server Management Studio 2012 version 11.0.2100.60 (Microsoft Corp); tests for normality and logistic regression were completed using SPSS statistical software version 23 (IBM Corp).

    Results
    Demographic Characteristics in Adults and Children With CKD and Adults At Risk of CKD

    A total of 2 625 963 adults and children were included in the sample. The cohort of adults with CKD included 606 064 individuals (23.1%), including 338 785 women (55.9%), 434 474 non-Latino white individuals (71.7%), 17 625 Latino individuals (2.9%), 29 974 black individuals (4.9%), 32 850 Asian individuals (5.4%), 5461 American Indian and Alaska Native individuals (0.9%), and 3899 Hawaiian and Pacific Islander individuals (0.6%) (Table 1). The median (IQR) age among adults was 70 (59-81) years. The proportions with CKD were highest among those aged 60 to 89 years (401 541 [66.3%]). A total of 12 591 children (0.4%) with CKD included 7079 girls (56.2%) and 6653 non-Latino white children (52.8%) (Table 2). The median (IQR) age of children with CKD was 6 (1-13) years, and CKD was comparably distributed across age groups (2545 [20.2%] aged <1 year; 2241 [17.8%], 1-3 years; 1515 [12.0%], 4-6 years; 1863 [14.8%], 7-10 years; 1916 [15.2%], 11-14 years; and 2511 [19.9%], 15-17 years). The cohort of participants at risk for CKD included 1 973 258 adults (75.1%). Among them, 955 812 (48.4%) had hypertension alone, while 505 147 (25.6%) had diabetes or prediabetes with hypertension, and 512 299 (26.0%) had diabetes or prediabetes alone. Those at risk for CKD included 1 014 847 women (51.4%), 1 308 036 non-Latino white individuals (66.3%), 60 201 Latino individuals (3.1%), 92 403 black individuals (4.9%), 114 400 Asian individuals (5.8%), 19 820 American Indian and Alaska Native individuals (1.0%), and 11 420 Hawaiian and Pacific Islander individuals (0.6%) (eTable 2 in the Supplement). Proportions of participants at risk for CKD were highest among those aged 50 to 69 years (866 528 [43.9%]).

    Comparing adults with CKD with those at risk for CKD, women were more frequently represented in the cohort with CKD than in the cohort at risk for CKD (338 785 [55.9%] vs 1 014 847 [51.4%]; P < .001). Non-Latino white individuals (434 474 [71.7%] vs 1 308 036 [66.3%]; P < .001) and individuals aged 70 years or older (315 397 [52.0%] vs 386 364 [19.6%]; P < .001) were also more common among participants with CKD vs those at risk. There was a higher proportion with rural geolocation within PSJH vs UCLA Health (287 622 [17.2%] vs 6918 [1.8%]; P < .001).

    Clinical Characteristics in Adults and Children With CKD and Adults at Risk for CKD

    A total of 243 635 adults with CKD (40.2%) were identified by eGFR, 163 375 (27.0%) by administrative codes, and 151 794 (25.0%) by both eGFR and administrative codes. Various combinations of laboratory measurements and administrative codes accounted for the remainder of adult CKD identification. More than half of adults with CKD were in category 3 (3a, 226 693 [37.4%]; 3b, 100 239 [16.5%]) (Table 1). Decreases in prevalence were observed for CKD category 4 (39 125 [6.5%]) and category 5, not dialyzed (20 328 [3.4%]). Median (IQR) eGFR was 53 (41-61) mL/min/1.73 m2, and measurements of albuminuria and proteinuria were recorded in 52 511 (8.7%) and 25 035 (4.1%) patients, respectively. Mean (SD) systolic and diastolic blood pressure values were 129 (18) mm Hg and 72 (11) mm Hg, respectively. When participants with CKD and diabetes or prediabetes were assessed separately, higher proportions of patients with diabetes than those with prediabetes had CKD category 4 or 5 (9790 [18.4%] vs 3724 [13.2%]; P < .001), and higher levels of albuminuria or proteinuria were present in the group with diabetes compared with the group with prediabetes (5555 [10.4%] vs 965 [3.4%]; P < .001) (eTable 3 in the Supplement).

    Most children (10 841 [86.1%]) were identified exclusively through CKD administrative codes. Among 8145 children (64.7%), CKD was not categorized (Table 2). Median (IQR) eGFR was 70 (50-95) mL/min/1.73 m2. Mean (SD) systolic and diastolic blood pressure were 104 (16) mm Hg and 61 (11) mm Hg, respectively. Measurements of albuminuria and proteinuria were available in 520 (4.1%) and 798 (6.4%) children, respectively.

    Median (IQR) eGFR in adults at risk of CKD was 90 (77-103) mL/min/1.73 m2, and albuminuria and proteinuria measurements were recorded in 51 470 (2.6%) and 10 285 (0.5%), respectively (eTable 2 in the Supplement). Mean (SD) systolic and diastolic blood pressure values were 135 (18) mm Hg and 79 (12) mm Hg, respectively. When participants with diabetes or prediabetes who were at risk for CKD were analyzed separately, frequency of ascertainment for albuminuria or proteinuria was 7% or less in all groups (eg, among 317 648 patients with diabetes and hypertension, albumin to creatine ratio measurements were available for 21 697 patients [6.8%]; among 187 499 patients with prediabetes and hypertension, protein to creatine ratio measurements were available in 907 [0.5%]) (eTable 4 in the Supplement).

    Prevalence of and Temporal Trends in CKD Among Adults

    A total of 12 669 700 patients received care at PSJH (10 793 550 [85.2%]) and UCLA Health (1 876 150 [14.8%]) between January 1, 2006, and December 31, 2017 (eFigure in the Supplement). During this period, 606 064 adults (4.8%) met the CURE-CKD registry entry criteria for CKD. However, when CKD was determined by at least 2 eGFR measurements of less than 60 mL/min/1.73 m2 at least 90 days apart, unadjusted prevalence among adults was 26.1% (420 678 of 1 612 737), and adjusted CKD prevalence was 22.6%. When determined by 1 eGFR measure, unadjusted CKD prevalence was 34.4% (873 642 of 2 542 393), and adjusted prevalence was 32.9% (Table 3). Diagnostic coding for CKD was recorded among 171 011 patients (40.7%) with CKD determined by 2 eGFR measurements at least 90 days apart and among 240 630 patients (27.5%) with CKD determined by 1 eGFR measurement.

    Temporal trends in CKD prevalence were determined for the 3 following periods: 2006 to 2009, 2010 to 2013, and 2014 to 2017. CKD prevalence rates by CURE-CKD registry criteria increased over time (2006-2009, 93 644 of 6 011 129 [1.6%]; 2010-2013, 393 455 of 6 903 084 [5.7%]; and 2014-2017, 683 574 of 8 179 860 [8.4%]). Prevalence rates adjusted for age, sex, and race/ethnicity and based on eGFR classification alone were higher and stable over time among patients with 2 or more eGFR measurements at least 90 days apart (20.8%, 22.6%, and 21.2%, respectively), while increasing adjusted prevalence was observed among patients with 1 eGFR measurement (22.3%, 27.8%, and 28.5%, respectively). Rates of administrative coding for CKD increased progressively at both PSJH and UCLA Health (Table 3). For example, among patients with 2 eGFR measurements of less than 60 mL/min/1.73 m2 at least 90 days apart, 2766 of 87 225 (3.2%) were identified by administrative code during 2006 to 2009 and 124 897 of 238 750 (52.3%) were identified by administrative code during 2014 to 2017. When CKD categories were analyzed by at least 2 eGFR measurements at least 90 days apart, unadjusted prevalence rates and prevalence rates adjusted by age, sex, and race/ethnicity showed progressive increases for categories 3a and 3b with declines in categories 4 and 5 (eg, category 3a: 2006-2009, 22 805 [prevalence, 26.1%; adjusted prevalence 26.1%]; 2014-2017, 96 449 [prevalence 40.4%; adjusted prevalence, 38.2%]; category 4: 2006-2009, 22 338 [prevalence, 25.7%, adjusted prevalence, 19.4%], 2014-2017, 42 065 [prevalence, 17.6%; adjusted prevalence, 16.1%]) (Figure 1).

    Prescription Medication Use and Temporal Trends in Adults With CKD

    Angiotensin-converting enzyme (ACE) inhibitors or angiotensin receptor blockers (ARBs) were prescribed to 127 574 adults (20.5%) with CKD, with slightly higher use of these agents among those with CKD and hypertension (112 449 of 434 657 [25.9%]) (eTable 5 in the Supplement). By contrast, 204 307 participants (33.7%) with CKD had prescriptions for nonsteroidal anti-inflammatory drugs (NSAIDs) or proton pump inhibitors (PPIs). Statins and aspirin were prescribed to 107 445 (17.7%) and 110 335 (18.2%) individuals, respectively. The most commonly prescribed antihyperglycemic agents among patients with CKD and diabetes or prediabetes were insulin (38 278 [10.0%]), metformin (30 393 [7.9%]), and sulfonylureas (16 989 [4.4%]). Medications prescribed among the cohort of participants at risk of CKD were generally similar to the CKD cohort, except for more common use of insulin (83 363 [16.3%]) among those with diabetes and of NSAIDs (701 493 [35.5%]) and PPIs (295 804 (15.0%]) overall (eTable 6 in the Supplement).

    Temporal trends in prescription medications were determined for participants with CKD determined by 2 eGFR measurements of less than 60 mL/min/1.73 m2 at least 90 days apart for the 3 periods. Use rates of ACE inhibitors, ARBs, NSAIDs, and PPIs across CKD categories 3a to 5 all increased (ACE inhibitors: 2006-2009, 5654 [2.0%]; 2010-2013, 46 921 [5.1%]; 2014-2017, 81 601 [7.6%]; ARBs: 2006-2009, 2461 [0.9%]; 2010-2013, 21 791 [2.4%]; 2014-2017, 47 233 [4.4%]; NSAIDs: 2006-2009, 7009 [2.4%]; 2010-2013, 57 705 [6.3%]; 2014-2017, 113 251 [11.0%]; PPIs: 2006-2009, 5331 [1.8%]; 2010-2013, 44 362 [4.9%]; 2014-2017, 83 340 [7.7%]) (Figure 2). Sodium-glucose cotransporter 2 inhibitors were rarely prescribed, but use increased over time (2006-2009, 0; 2010-2013, 22 [0.002%]; 2014-2017, 1002 [0.093%]).

    Discussion

    More than 2.6 million adults and children who received care at PSJH and UCLA Health from 2006 to 2017 had CKD or were at risk of CKD. Overall, CKD prevalence among adults in the health care systems was 4.8%, as determined by a combination of eGFR, albuminuria and proteinuria measures, and administrative code criteria. However, adult CKD prevalence adjusted for age, sex, and race/ethnicity was 22.6% based on persistently low eGFR alone. Adults with CKD were more likely to be older, women, and non-Latino white individuals. In this study, CKD category 3 was most frequent, with a clear drop-off in prevalence at more advanced categories. Kidney protective agents (ie, renin-angiotensin system inhibitors) were prescribed to approximately one-fifth of adults with CKD, while potential nephrotoxins (ie, NSAIDs and PPIs) were prescribed to more than one-third of adults with CKD. Albuminuria and proteinuria testing for CKD assessment was rarely reported.

    The CURE-CKD registry is among the most comprehensive CKD registries worldwide. A unique feature is the extensive amount of patient-level data on laboratory measures, prescriptions, and vital signs, combined with administrative codes, to identify CKD and major risk factors according to guideline-based criteria.21,24,25 Previous registries were restricted by containing primarily administrative data, ESKD, primary care practices, single health care systems, older adults, or men.4,26-32 In contrast, CURE-CKD participants represent the life span, from children to adults, and include women and men and a wide spectrum of races and ethnicities across an expansive region of the western United States that has not been previously involved in large-scale epidemiologic studies of CKD. Moreover, PSJH and UCLA Health care for patients in a variety of settings that include academic, primary care, and specialty practices as well as community health and safety-net systems. Rural patients were well represented in the geography covered by PSJH. Thus, CURE-CKD provides in-depth identification of patients with and at risk for CKD in contemporary US health care systems.

    In CURE-CKD, the progressive increase in adult CKD prevalence was largely driven by diagnostic coding. Among adults with persistently low eGFR, use of CKD administrative codes increased from 3.2% to 52.3% between the periods of 2006 to 2009 and 2014 to 2017, while overall CKD prevalence estimates, adjusted for age, sex, and race/ethnicity, were essentially stable between 20.8% and 22.6%. Although the upward trend in CKD recognition represents a clinically meaningful improvement, nearly one-half of patients with low eGFR remained undiagnosed in the most recent period. The present findings from CURE-CKD point to the critical need for quality improvement and research at the point of care.

    Although nearly two-thirds of the adults with CKD had diabetes, hypertension, or prediabetes, rates of laboratory testing for albuminuria or proteinuria and of prescribing ACE inhibitors or ARBs were low. Potentially nephrotoxic agents (ie, NSAIDs and PPIs) were used more commonly than renin-angiotensin system inhibitors. Given the most common cause of death in CKD is cardiovascular disease, the low use of preventive agents, such as statins and aspirin, is also concerning.33,34 Compared with participants in the National Health and Nutrition Examination Survey, patients with CKD in CURE-CKD received ACE inhibitors or ARBs much less often during approximately the same period.35 Although CURE-CKD found an increase in uptake of renin-angiotensin system inhibitors in adults with CKD categories 3a to 5, NSAID and PPI use also increased over time. However, these prescription rates were lower than in the overall CKD cohort, perhaps because of concerns about adverse effects with more advanced CKD. While this may seem counterintuitive for renin-angiotensin system inhibitors, these agents may be avoided because of fear of complications such as hyperkalemia or acute kidney injury, especially in acute care settings. In Ontario, Canada, primary care practices reported ACE inhibitor or ARB use in three-fourths of patients with CKD, but the metric was confined to those with diabetes and albuminuria or adults older than 66 years.30,31 Nevertheless, rates of albuminuria testing in the overall CKD population were comparably low, although avoidance of NSAIDs was better among patients in Canada than in CURE-CKD.30 A recent Canada-wide study32 from an EHR-based surveillance system in primary care found that only 4 of 12 quality indicators for CKD care were met, with ACE inhibitor or ARB use among approximately one-third of patients with diabetes or proteinuria. Contrasts exist between reports from health care systems, community screenings, primary care practices, and countries, but they consistently illuminate major gaps in CKD care and the need for more comprehensive surveillance to uncover actionable trends.

    In comparison with patients treated at PSJH and UCLA Health, the Kidney Early Evaluation Program and the National Health and Nutrition Examination Survey have reported lower frequencies of individuals at risk for CKD in community screenings.36 Moreover, associations between risk factors and CKD are remarkably complex. For example, although a primary contributor to CKD is diabetes, CKD in patients with diabetes greatly amplifies cardiovascular risks.37 Additionally, nearly one-fifth of patients with CKD in the CURE-CKD registry had prediabetes. The prediabetes phenotype of CKD appears less severe than the diabetes phenotype of CKD, as reflected by fewer patients with advanced CKD categories, albuminuria, or proteinuria and prediabetes. Nevertheless, consistent with findings from the National Health and Nutrition Examination Survey, findings from CURE-CKD support the observation that subdiabetic hyperglycemia may contribute to kidney damage before overt diabetes ensues.13 The CURE-CKD registry contains abundant longitudinal data that will be invaluable for elucidating CKD incidence among individuals at risk as well as progression and complications in those with CKD. Given its vast scope, CURE-CKD is also ideally suited to generate and validate CKD risk prediction models.38

    Strengths and Limitations

    Strengths of the CURE-CKD registry include the large sample size, long observation duration, and curated patient-level data from 2 US health care systems. However, this study has limitations. First, CURE-CKD is limited by differences in documentation methods across and between health care systems and varying attrition rates based on insurance, socioeconomic factors, and geolocation. Variation in platforms even within a common EHR system also presents a limitation to the creation of interinstitutional registries, highlighting the importance of collaboration in identifying data elements, structures, and synchronization. Lack of information on over-the-counter medications underestimates the usage rates for NSAIDs, PPIs, and other potential nephrotoxins. Data in CURE-CKD on sodium-glucose cotransporter 2 inhibitor use, recently recommended for diabetes and CKD, came from an era before this new indication. It will be important to follow this trend to ensure sodium-glucose cotransporter 2 inhibitors are delivered to patients who may benefit. Another limitation of EHR-based registries is undercoding and miscoding. To mitigate this limitation, patient-level data for laboratory values, vital signs, and prescriptions were used to classify CURE-CKD registry participants and their care, which allowed for the use of guideline-based criteria for persistence of low eGFR or elevated albuminuria or proteinuria levels. Although CURE-CKD produced a lower range estimate of overall CKD prevalence compared with other US reports, this prevalence rate is similar to that found in Canadian primary care.4,32 A higher range estimate for CKD based solely on eGFR could be because of more frequent testing in patients with higher risk who were treated by both specialty and primary care practices at PSJH and UCLA Health. Ascertainment bias is an inherent limitation of EHR-based registries, and information about CKD will also be missed from patients receiving care elsewhere or not receiving testing. The actual prevalence of overall CKD likely lies between the low (4.8%) and high (22.6%) range estimations from CURE-CKD. Nevertheless, these detailed prevalence estimates are strengths that represent the complexity and composition of patients treated in typical US health care systems.

    Conclusions

    In conclusion, the CURE-CKD registry reveals a burgeoning number of patients with CKD and major risk factors of diabetes, hypertension, and prediabetes. Rates of identification and use of kidney protective agents were low, while nephrotoxin use was widespread, underscoring the pressing need for practice-based improvement in CKD prevention, recognition, and treatment. These real-world data lay the groundwork for the development of more effective strategies to deliver care that enhances wellness and survival for patients with and at risk for CKD.

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    Article Information

    Accepted for Publication: November 4, 2019.

    Published: December 20, 2019. doi:10.1001/jamanetworkopen.2019.18169

    Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2019 Tuttle KR et al. JAMA Network Open.

    Corresponding Author: Katherine R. Tuttle, MD, Providence St Joseph Health, Providence Medical Research Center, 105 W Eighth Ave, Ste 6050 W, Spokane, WA 99204 (katherine.tuttle@providence.org).

    Author Contributions: Dr Tuttle had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: Tuttle, Jones, Daratha, Nicholas, McPherson, Bell, Mangione, Norris.

    Acquisition, analysis, or interpretation of data: Tuttle, Alicic, Duru, Jones, Daratha, McPherson, Neumiller, Bell, Norris.

    Drafting of the manuscript: Tuttle, Alicic, Jones, Nicholas, McPherson, Norris.

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

    Statistical analysis: Tuttle, Jones, Daratha, McPherson, Bell.

    Obtained funding: Tuttle, Magione, Norris.

    Administrative, technical, or material support: Tuttle, Alicic, Daratha, McPherson, Neumiller, Bell, Norris.

    Supervision: Tuttle, Magione, Norris.

    Conflict of Interest Disclosures: Dr Tuttle reported receiving personal fees from Eli Lilly and Co, Boehringer Ingelheim, AstraZeneca, Gilead Sciences, Goldfinch Bio, and Novo Nordisk outside the submitted work. Dr Nicholas reported receiving grants from Goldfinch Bio, Bayer, the US Centers for Disease Control and Prevention, and Terasaki Research Institute; serving as national leader of a phase 3 clinical trial for the George Clinical Institute of Global Health; and receiving consulting fees from Janssen Pharmaceuticals and Amgen outside the submitted work. Dr McPherson reported receiving grants from the US Department of Health and Human Services and US Centers for Disease Control and Prevention during the conduct of the study. Dr Norris reported receiving grants from the National Institutes of Health during the conduct of the study. No other disclosures were reported.

    Funding/Support: The CURE-CKD registry was supported by institutional funding from Providence St Joseph Health and the University of California, Los Angeles. Dr Tuttle is supported by grants 4UL1TR00426-10, 1U2CDK114886-01, 1U54DK083912, and 2UC4DK101108-02 from the National Institutes of Health. Drs Tuttle and Alicic are supported by grants 5UM1DK100846-03 and 2U01DK10086-07 from the National Institutes of Health. Drs Tuttle, Alicic, Jones, Daratha, and McPherson are supported by grant 75D301-19-Q-69877 from the US Centers for Disease Control and Prevention. Drs Duru, Bell, Mangione, and Norris are supported by grant UL1TR000124 from the National Institutes of Health. Drs Duru, Mangione, and Norris are supported by grant P30AG021684-15S2 from the National Institutes of Health. Dr Nicholas is supported by grant UL1TRR001881 from the National Center for Advancing Translational Science. Dr McPherson is supported by grants P20MD006871, UG1DA013714, R01EY027476, N44DA162246, R01AA022070, R01AA020248, P60AA026112, R41AA026793, N44DA171210, and R01AG042467 from the National Institutes of Health and grant I01HX002518 from the US Department of Veterans Affairs. Dr Mangione is supported by the Barbara A. Levey and Gerald S. Levey Endowed Chair.

    Role of the Funder/Sponsor: The funding sources 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.

    Additional Contributions: Carol Miceli, BS, Robert Follett, BS, and Theona Tacorda, MS (Providence St Joseph Health), extracted data from electronic health records, and Art Gongora (Providence St Joseph Health) provided administrative support. They were compensated for their time.

    References
    1.
    Levin  A, Tonelli  M, Bonventre  J,  et al; ISN Global Kidney Health Summit participants.  Global kidney health 2017 and beyond: a roadmap for closing gaps in care, research, and policy.  Lancet. 2017;390(10105):1888-1917. doi:10.1016/S0140-6736(17)30788-2PubMedGoogle ScholarCrossref
    2.
    Jha  V, Garcia-Garcia  G, Iseki  K,  et al.  Chronic kidney disease: global dimension and perspectives.  Lancet. 2013;382(9888):260-272. doi:10.1016/S0140-6736(13)60687-XPubMedGoogle ScholarCrossref
    3.
    Bello  AK, Levin  A, Tonelli  M,  et al.  Assessment of global kidney health care status.  JAMA. 2017;317(18):1864-1881. doi:10.1001/jama.2017.4046PubMedGoogle ScholarCrossref
    4.
    United States Renal Data System.  2018 USRDS Annual Data Report: Epidemiology of Kidney Disease in the United States. Bethesda, MD: National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases; 2018.
    5.
    Xie  Y, Bowe  B, Mokdad  AH,  et al.  Analysis of the Global Burden of Disease study highlights the global, regional, and national trends of chronic kidney disease epidemiology from 1990 to 2016.  Kidney Int. 2018;94(3):567-581. doi:10.1016/j.kint.2018.04.011PubMedGoogle ScholarCrossref
    6.
    Bowe  B, Xie  Y, Li  T,  et al.  Changes in the US burden of chronic kidney disease from 2002-2016: an analysis of the Global Burden of Disease study.  JAMA Netw Open. 2018;1(7):e184412. doi:10.1001/jamanetworkopen.2018.4412PubMedGoogle Scholar
    7.
    Couser  WG, Remuzzi  G, Mendis  S, Tonelli  M.  The contribution of chronic kidney disease to the global burden of major noncommunicable diseases.  Kidney Int. 2011;80(12):1258-1270. doi:10.1038/ki.2011.368PubMedGoogle ScholarCrossref
    8.
    International Diabetes Federation. IDF Diabetes Atlas. https://www.diabetesatlas.org/en/. Accessed November 13, 2019.
    9.
    Alicic  RZ, Rooney  MT, Tuttle  KR.  Diabetic kidney disease: challenges, progress, and possibilities.  Clin J Am Soc Nephrol. 2017;12(12):2032-2045. doi:10.2215/CJN.11491116PubMedGoogle ScholarCrossref
    10.
    Thomas  MC, Cooper  ME, Zimmet  P.  Changing epidemiology of type 2 diabetes mellitus and associated chronic kidney disease.  Nat Rev Nephrol. 2016;12(2):73-81. doi:10.1038/nrneph.2015.173PubMedGoogle ScholarCrossref
    11.
    Fryar  CD, Ostchega  Y, Hales  CM, Zhang  G, Kruszon-Moran  D.  Hypertension prevalence and control among adults: United States, 2015-2016.  NCHS Data Brief. 2017-2016;2017(289):1-8.PubMedGoogle Scholar
    12.
    NCD Risk Factor Collaboration (NCD-RisC).  Worldwide trends in blood pressure from 1975 to 2015: a pooled analysis of 1479 population-based measurement studies with 19.1 million participants.  Lancet. 2017;389(10064):37-55. doi:10.1016/S0140-6736(16)31919-5PubMedGoogle ScholarCrossref
    13.
    Ali  MK, Bullard  KM, Saydah  S, Imperatore  G, Gregg  EW.  Cardiovascular and renal burdens of prediabetes in the USA: analysis of data from serial cross-sectional surveys, 1988-2014.  Lancet Diabetes Endocrinol. 2018;6(5):392-403. doi:10.1016/S2213-8587(18)30027-5PubMedGoogle ScholarCrossref
    14.
    Dharmarajan  SH, Bragg-Gresham  JL, Morgenstern  H,  et al; US Centers for Disease Control and Prevention CKD Surveillance System.  State-level awareness of chronic kidney disease in the US.  Am J Prev Med. 2017;53(3):300-307. doi:10.1016/j.amepre.2017.02.015PubMedGoogle ScholarCrossref
    15.
    Tuot  DS, Diamantidis  CJ, Corbett  CF,  et al.  The last mile: translational research to improve CKD outcomes.  Clin J Am Soc Nephrol. 2014;9(10):1802-1805. doi:10.2215/CJN.04310514PubMedGoogle ScholarCrossref
    16.
    Boulware  LE, Troll  MU, Jaar  BG, Myers  DI, Powe  NR.  Identification and referral of patients with progressive CKD: a national study.  Am J Kidney Dis. 2006;48(2):192-204. doi:10.1053/j.ajkd.2006.04.073PubMedGoogle ScholarCrossref
    17.
    US Department of Health and Human Services. Advancing American kidney health. https://aspe.hhs.gov/pdf-report/advancing-american-kidney-health. Accessed July 20, 2019.
    18.
    Norris  K, Duru  OK, Alicic  RZ,  et al.  Rationale and design of a multicenter Chronic Kidney Disease (CKD) and at-risk for CKD electronic health records-based registry: CURE-CKD.  BMC Nephrol. In press.Google Scholar
    19.
    Equator Network. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. http://www.equator-network.org/reporting-guidelines/strobe. Accessed October 16, 2019.
    20.
    Matsushita  K, Mahmoodi  BK, Woodward  M,  et al; Chronic Kidney Disease Prognosis Consortium.  Comparison of risk prediction using the CKD-EPI equation and the MDRD study equation for estimated glomerular filtration rate.  JAMA. 2012;307(18):1941-1951. doi:10.1001/jama.2012.3954PubMedGoogle ScholarCrossref
    21.
    Kidney Disease: Improving Global Outcomes (KDIGO) Work Group. KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease. https://kdigo.org/wp-content/uploads/2017/02/KDIGO_2012_CKD_GL.pdf. Accessed November 13, 2019.
    22.
    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. doi:10.1681/ASN.2008030287PubMedGoogle ScholarCrossref
    23.
    Nichols  GA, Desai  J, Elston Lafata  J,  et al; SUPREME-DM Study Group.  Construction of a multisite DataLink using electronic health records for the identification, surveillance, prevention, and management of diabetes mellitus: the SUPREME-DM project.  Prev Chronic Dis. 2012;9:E110. doi:10.5888/pcd9.110311PubMedGoogle Scholar
    24.
    American Diabetes Association.  2. Classification and diagnosis of diabetes: Standards of Medical Care in Diabetes—2019 Diabetes Care. 2019;42(suppl 1):S13-S28. doi:10.2337/dc19-S002PubMedGoogle ScholarCrossref
    25.
    James  PA, Oparil  S, Carter  BL,  et al.  2014 evidence-based guideline for the management of high blood pressure in adults: report from the panel members appointed to the Eighth Joint National Committee (JNC 8).  JAMA. 2014;311(5):507-520. doi:10.1001/jama.2013.284427PubMedGoogle ScholarCrossref
    26.
    Navaneethan  SD, Jolly  SE, Schold  JD,  et al.  Development and validation of an electronic health record-based chronic kidney disease registry.  Clin J Am Soc Nephrol. 2011;6(1):40-49. doi:10.2215/CJN.04230510PubMedGoogle ScholarCrossref
    27.
    Bello  A, Hemmelgarn  B, Manns  B, Tonelli  M; Alberta Kidney Disease Network.  Use of administrative databases for health-care planning in CKD.  Nephrol Dial Transplant. 2012;27(suppl 3):iii12-iii18. doi:10.1093/ndt/gfs163PubMedGoogle Scholar
    28.
    Tuot  DS, McCulloch  CE, Velasquez  A,  et al.  Impact of a primary care CKD registry in a US public safety-net health care delivery system: a pragmatic randomized trial.  Am J Kidney Dis. 2018;72(2):168-177. doi:10.1053/j.ajkd.2018.01.058PubMedGoogle ScholarCrossref
    29.
    Liu  FX, Rutherford  P, Smoyer-Tomic  K, Prichard  S, Laplante  S.  A global overview of renal registries: a systematic review.  BMC Nephrol. 2015;16:31.PubMedGoogle ScholarCrossref
    30.
    Tu  K, Bevan  L, Hunter  K, Rogers  J, Young  J, Nesrallah  G.  Quality indicators for the detection and management of chronic kidney disease in primary care in Canada derived from a modified Delphi panel approach.  CMAJ Open. 2017;5(1):E74-E81.PubMedGoogle ScholarCrossref
    31.
    Nash  DM, Brimble  S, Markle-Reid  M,  et al.  Quality of care for patients with chronic kidney disease in the primary care setting: a retrospective cohort study from Ontario, Canada.  Can J Kidney Health Dis. 2017;4:2054358117703059. doi:10.1177/2054358117703059PubMedGoogle Scholar
    32.
    Bello  AK, Ronksley  PE, Tangri  N,  et al.  Quality of chronic kidney disease management in Canadian primary care.  JAMA Netw Open. 2019;2(9):e1910704. doi:10.1001/jamanetworkopen.2019.10704PubMedGoogle Scholar
    33.
    Go  AS, Chertow  GM, Fan  D, McCulloch  CE, Hsu  CY.  Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization.  N Engl J Med. 2004;351(13):1296-1305. doi:10.1056/NEJMoa041031PubMedGoogle ScholarCrossref
    34.
    McCullough  PA, Li  S, Jurkovitz  CT,  et al; KEEP Investigators.  Chronic kidney disease, prevalence of premature cardiovascular disease, and relationship to short-term mortality.  Am Heart J. 2008;156(2):277-283. doi:10.1016/j.ahj.2008.02.024PubMedGoogle ScholarCrossref
    35.
    Myers  OB, Pankratz  VS, Norris  KC, Vassalotti  JA, Unruh  ML, Argyropoulos  C.  Surveillance of CKD epidemiology in the US: a joint analysis of NHANES and KEEP.  Sci Rep. 2018;8(1):15900. doi:10.1038/s41598-018-34233-wPubMedGoogle ScholarCrossref
    36.
    Murphy  DP, Drawz  PE, Foley  RN.  Trends in angiotensin converting enzyme inhibitor and angiotensin II receptor blocker use among those with impaired kidney function in the United States.  J Am Soc Nephrol. 2019;30(7):1314-1321. doi:10.1681/ASN.2018100971PubMedGoogle ScholarCrossref
    37.
    Afkarian  M, Sachs  MC, Kestenbaum  B,  et al.  Kidney disease and increased mortality risk in type 2 diabetes.  J Am Soc Nephrol. 2013;24(2):302-308. doi:10.1681/ASN.2012070718PubMedGoogle ScholarCrossref
    38.
    Tangri  N, Kitsios  GD, Inker  LA,  et al.  Risk prediction models for patients with chronic kidney disease: a systematic review.  Ann Intern Med. 2013;158(8):596-603. doi:10.7326/0003-4819-158-8-201304160-00004PubMedGoogle ScholarCrossref
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