Decision support tools, disease registries, and patient engagement materials can improve population-based chronic kidney disease care.
Objectives: To determine if electronic health record (EHR) tools and patient engagement can improve the quality of chronic kidney disease (CKD) care.
Study Design: Randomized controlled trial.
Methods: We enrolled 153 primary care physicians caring for 3947 high-risk and 3744 low-risk patients with stage III CKD across 13 ambulatory health centers in eastern Massachusetts. Intervention physicians received a set of electronic alerts during office visits recommending risk-appropriate CKD care. Patients of intervention physicians also received tailored educational mailings. For high-risk patients, we assessed for a visit with a nephrologist and prescription of an angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) during the 12-month study period. For low-risk patients, we assessed for a urine microalbumin screening and prescription of an ACE inhibitor or ARB during the 12-month study period.
Results: Among high-risk patients, those in the intervention arm were significantly more likely to have an office visit with a nephrologist compared with those in the control arm (45% vs 34%; P <.001). Among low-risk patients, those in the intervention arm were significantly more likely than those in the control arm to have received urine microalbumin testing (45% vs 21%; P <.001). There was no difference between the intervention and control arms in rates of prescription of an ACE inhibitor or ARB in either the high-risk patient group (76% vs 79%; P = .17) or the low-risk patient group (64% vs 65%; P = .57).
Conclusions: A combined program of EHR tools and patient engagement improved some areas of CKD care, but substantial gaps remain.
Am J Manag Care. 2018;24(4):e107-e114Takeaway Points
We randomized 153 primary care physicians caring for nearly 8000 patients with chronic kidney disease (CKD) to receive an intervention that combined electronic decision support tools, patient engagement materials, and collaboration between primary care and nephrology.
Chronic kidney disease (CKD) affects more than 25 million Americans, or more than 10% of the adult population.1 Effective management of moderate-stage CKD is needed to reduce the high mortality rates and extensive costs associated with progression to more advanced kidney failure.2-7
Many challenges exist to improving care for CKD,8,9 which remains a frequently unrecognized condition by both primary care physicians (PCPs)10-14 and their patients.1,15-17 Just 12% of patients with stage III or IV CKD are aware of their diagnosis, and just 63% of PCPs can correctly identify the presence of CKD.18 However, monitoring for disease progression, using appropriate medications, and involving nephrologists early can improve CKD outcomes.19
This highlights the need for healthcare systems to develop a systematic approach to treating this condition that supports primary care providers and nephrologists.7 Electronic health records (EHRs) present an opportunity to deliver appropriate care by identifying patients with CKD, stratifying the patient population, and facilitating tailored treatment and care coordination among patients, primary care, and nephrology.20 We conducted a randomized controlled trial to assess the impact of a comprehensive set of EHR tools and patient engagement materials to improve the management of CKD.
This 18-month trial was conducted from 2011 to 2013, with patient enrollment occurring during the initial 6 months and all patients followed for 12 months after enrollment. We randomly assigned PCPs to receive alerts within the EHR during office visits for patients with CKD and mailed educational materials to patients of physicians in the intervention arm. The Human Studies Committee at Brigham and Women’s Hospital approved the study protocol, and a waiver of informed consent was approved for physicians and patients. The trial was registered at ClinicialTrials.gov (ID NCT01203813).
We conducted our study at Harvard Vanguard Medical Associates, an integrated multispecialty group practice in eastern Massachusetts caring for approximately 300,000 adult patients. The system has significant experience in population health management, such as participation as a Pioneer Accountable Care Organization. The practices use a common EHR (Epic Systems; Verona, Wisconsin) that captures clinical notes, electronic diagnosis codes, specialty referrals, medication prescriptions, and laboratory test results. This system has delivered automated reporting of estimated glomerular filtration rate (eGFR), computed using the Modification of Diet in Renal Disease (MDRD) Study equation. The EHR does not provide decision support for patients with CKD. Nephrology services are provided by 8 nephrologists employed by the group practice.
We enrolled 153 physicians across 13 health centers and 7691 patients aged 18 to 80 years with an established diagnosis of stage III CKD (Figure). The diagnosis was based on meeting each of the following criteria: 1) the presence of an office visit with a PCP within the group practice within the prior 18 months, 2) the presence of at least 2 eGFR results between 30 and 60mL/min/1.73m2 within the prior 5 years, 3) the qualifying abnormal eGFR results were separated by at least 90 days, and 4) the most recent eGFR was less than 60 mL/min/1.73m2.
Randomization and Interventions
The intervention was randomized at the individual physician level. Within each health center, we paired clinicians based on their number of eligible patients with CKD and then randomly assigned 1 physician in each pair to the intervention arm.
Based on local consensus and emerging data on the importance of both eGFR and albuminuria,21 we stratified patients with stage III CKD according to their risk of complications and identified relevant treatment targets. Our local consensus was achieved prior to the publication of recent guidelines by the Kidney Disease: Improving Global Outcomes (KDIGO) initiative7 and involved gathering input from both primary care and nephrology leadership within our multispecialty group practice using current data on predictors of mortality for patients with CKD. “High-risk” patients were defined as those with either 1) at least 1 eGFR of less than 45 mL/min/1.73m2 in the prior 5 years or 2) at least 1 eGFR of at least 45 mL/min/1.73m2 but less than 60 mL/min/1.73m2, in combination with the presence of diabetes or albuminuria (urine microalbumin >30 mcg/mg or spot protein to creatinine ratio >0.15 mcg/mg). All other patients—specifically, those with an eGFR of at least 45 mL/min/1.73m2 but less than 60 mL/min/1.73m2 and no history of diabetes or albuminuria—were considered “low-risk”. We identified our treatment targets using the same process, and for high-risk patients, we recommended use of an angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB), as well as referral to a nephrologist. For low-risk patients, we recommended use of an ACE inhibitor or ARB, as well as annual monitoring of urine microalbumin to assess for disease progression and risk stratification.
All EHR alerts were displayed when physicians accessed the electronic ordering module of the patient chart. During office visits with high-risk patients, PCPs received up to 2 alerts. The first alert recommended a referral to a nephrologist if no such specialty office visit had occurred in the prior 12 months (eAppendix A [eAppendices available at ajmc.com]). The second alert recommended prescription of an ACE inhibitor or ARB if the patient carried a diagnosis of hypertension or albuminuria, had not been prescribed the medication in the last 12 months, and had no documented allergy to such medication.
During office visits with low-risk patients, PCPs received up to 2 alerts. The first was the same ACE inhibitor alert used for high-risk patients. The second recommended overdue annual laboratory tests, including those for urine microalbumin, serum creatinine, low-density lipoprotein (LDL) cholesterol, 25-hydroxy (OH) vitamin D, parathyroid hormone, calcium, phosphorus, and hemoglobin.
Our intervention included a mailed outreach program to promote patient engagement. We encouraged PCPs in the intervention arm to enroll patients using 2 strategies. First, all electronic reminders also prompted physicians to enroll patients in the mailed outreach program (eAppendix B). Second, for those physicians who did not respond to the request to enroll a patient via the electronic alert, we sent a follow-up postcard within 1 month of the office visit requesting them to enroll the patient. We required physicians to enroll patients to ensure that patients received a diagnosis of CKD from their care team prior to receiving any mailings.
The outreach program consisted of quarterly mailings to patients that provided tailored treatment recommendations based on detailed extracts from their EHR. The mailings were based on educational materials developed by the National Kidney Disease Education Program (eAppendix C).22 These mailings provided recommendations specific to CKD for managing blood pressure, appropriate use of ACE inhibitors or ARBs, education on current medications, and recommendations for overdue laboratory tests or follow-ups on previously abnormal results.
Physicians in the control group received no EHR alerts, and their patients were not eligible to receive the mailed outreach program.
Outcomes and Follow-Up
The primary study end points were based in part on the KDIGO guidelines and extracted from the EHR. Among high-risk patients, the primary end points included an office visit to a nephrologist during the 12-month study period and the prescription of an ACE inhibitor or ARB during the 12-month study period for those with hypertension and/or microalbuminuria and no documented allergy. The primary study end points among low-risk patients included the presence of a urine protein test during the 12-month study period and the prescription of an ACE inhibitor or ARB for those with hypertension and/or microalbuminuria and no documented allergy. We also assessed secondary outcomes of rates of annual serum creatinine, LDL cholesterol, hemoglobin, phosphorus, 25-OH vitamin D, calcium, and parathyroid hormone testing. Physicians and patients were not blinded to intervention status, although all outcomes data were collected without respect to intervention status.
Patient and Physician Surveys
We surveyed all patients in the intervention arm who were enrolled in the outreach program (n = 1002) by their PCP. Patients used a 4-point ordinal scale from “definitely yes” to “definitely no” to report on whether the mailings gave them choices to think about for treating CKD, helped them set specific CKD treatment goals, and helped them understand their medications for CKD. Patients also reported on whether their doctor or another health professional had told them that they had weak or failing kidneys, and they used a 5-point ordinal scale from “strongly agree” to “strongly disagree” to report agreement with their diagnosis of CKD. Finally, patients used a 5-point scale from “excellent” to “poor” to rate the CKD care they received. The survey was administered via a single mailing at the end of the intervention and achieved a 24% (n = 242) response rate.
We surveyed 153 study physicians at the completion of the intervention. Physicians used a 5-point ordinal scale from “always” to “never” to report on the frequency with which they informed patients of a new diagnosis of CKD once they recognized it was present. Physicians also reported on the eGFR threshold at which they felt comfortable informing their patients of a diagnosis of CKD. Intervention physicians also rated the effectiveness of the electronic alerts, patient mailings, and collaboration with nephrology on improving the quality of CKD care among their patients (“very effective,” “somewhat effective,” or “not effective”). The survey was implemented via an initial paper mailing, followed by a reminder email to nonresponders and a final paper mailing at 4 weeks, achieving a 73% (n = 111) response rate.
Balance between patient demographic characteristics in the intervention and control arms was checked using a t test for patient age, Fisher exact tests for binary variables, and χ2 tests for categorical variables. We analyzed the impact of the intervention by fitting logistic regression models using the generalized estimating equation approach to account for clustering of patients within clinics, with performance of each of our prespecified outcomes as the dependent variable and intervention status as the primary independent variable. The models were implemented using the GENMOD procedure in SAS version 9.3 (SAS Institute; Cary, North Carolina).
We conducted post hoc analyses to understand the importance of exposure to the intervention components. These included the subset of patients in the intervention arm who received the outreach mailings, as well as patients with varying numbers of office visits (0, 1-3, and >3) to their PCP during the intervention period. For the outreach mailing analyses, we used propensity score stratification to compare the appropriate set of patients in the control arm with the subset of patients in the intervention arm who received mailings. A propensity score model was created separately for each clinic, using the following variables as predictors of receiving a mailing: patient sex; race/ethnicity; insurance type; prior nephrology visit; current treatment with an ACE inhibitor or ARB; baseline eGFR; and presence of diabetes, cardiovascular disease, or hypertension. Patients from the intervention arm who received a mailing were then compared, through stratification, with patients from the control arm who had a similar probability (ie, were within the same 5% propensity interval) of receiving a mailing. Outcomes among patient groups were compared using the same clustered logistic regression models described earlier, adjusting for correlation within clinicians, time on study, and propensity strata.
We randomized 153 PCPs caring for 7691 adult patients with stage III CKD, including 3947 high-risk patients and 3744 low-risk patients (Table 1). The median number of patients enrolled per PCP was 47 (interquartile range, 26-69).
Among high-risk patients, those in the intervention arm were significantly more likely to have an office visit with a nephrologist during the 12-month study period compared with those in the control arm (45% vs 34%; P <.001) (Table 2). Among low-risk patients, those in the intervention arm were significantly more likely than those in the control arm to have received urine microalbumin testing in the prior 12 months (45% vs 21%; P <.001). There was no difference between the intervention and control arms in rates of prescribing an ACE inhibitor or ARB in either the high-risk patient group (76% vs 79%; P = .17) or the low-risk patient group (64% vs 65%; P = .57).
Among both high- and low-risk patients, those in the intervention arm had higher rates of annual testing compared with those in the control arm for phosphorus, vitamin D, and parathyroid hormone. High-risk patients also had higher annual testing rates for calcium in the intervention arm compared with the control arm (Table 2).
Exposure to Intervention Components
Intervention physicians enrolled 1002 (26%) patients into the patient mailing program, including 647 (32%) high-risk patients and 355 (19%) low-risk patients. With the exception of ACE inhibitor or ARB therapy and testing of microalbumin in high-risk patients, both high- and low-risk patients in the intervention arm who received patient mailings were significantly more likely than propensity-stratified control arm patients to achieve all primary and secondary study outcomes (Table 3).
Among all study patients, 41% had 4 or more office visits to their PCP during the study period, 51% had 1 to 3 visits, and 7% had no primary care visits. Regardless of intervention status, rates of annual nephrology visits, receiving prescription of ACE inhibitor or ARB therapy, and annual urine protein monitoring were all significantly lower among patients with no primary care visits compared with either group of patients with at least 1 visit (Table 4). In addition, the intervention effect varied according to the number of PCP office visits during the study period, demonstrating no significant intervention effect among those patients with 0 visits and larger intervention effect sizes for those with at least 1 visit.
Physician and Patient Surveys
More than half (61%; n = 138) of intervention patients who received outreach mailings reported being told by a doctor or health professional that they had weak or failing kidneys. In logistic regression models that considered patient age, sex, and race; comorbid conditions (diabetes, hypertension, and cardiovascular disease); and CKD features (high- vs low-risk status and nephrology consultation), the absence of diabetes (odds ratio [OR], 1.9; 95% CI, 1.1-3.2), a nephrology visit prior to the intervention period (OR, 2.6; 95% CI, 1.6-4.3), and a nephrology visit during the intervention period (OR, 3.5; 95% CI, 2.1-5.9) were all associated with patients reporting being told that they had weak or failing kidneys.
More than half (63%; n = 142) of intervention patients strongly or somewhat agreed with their diagnosis of CKD, whereas 18% (n = 41) strongly or somewhat disagreed with their diagnosis of CKD. Two-thirds (67%; n = 136) of intervention patients rated their care for CKD as excellent or very good. A majority (89%; n = 177) of patients reported that the outreach mailings definitely or somewhat gave them choices to think about for treating their CKD, 82% (n = 162) felt the mailings helped them set specific goals for CKD treatment, and 81% (n = 153) felt the mailings helped them understand their medications for CKD.
Intervention and control physicians were similarly likely to report that they always or usually informed their patients of a diagnosis of CKD (87% vs 75%; P = .12). A higher percentage of intervention physicians compared with control physicians reported feeling comfortable establishing a diagnosis of CKD using a threshold eGFR of less than 60 (56% vs 39%; P = .07, adjusted for within-clinic correlation), although the difference was not statistically significant. Three-quarters (75%) of physicians in the intervention group reported that our electronic reminders were somewhat or very effective at improving the quality of CKD care among their patients, 84% reported the patient mailings were somewhat or very effective, and 92% reported that collaboration with nephrology was somewhat or very effective.
In a large randomized controlled trial of patients with stage III CKD, we demonstrated that a quality improvement program consisting of electronic decision support combined with mailed patient self-management support tools significantly improved quality of care, including use of nephrology referrals and laboratory testing. In particular, our intervention resulted in increased screening rates for urine microalbumin, identifying patients who warrant more aggressive management given the importance of microalbuminuria in predicting disease progression.
Our study findings demonstrated that a large population of patients with CKD can be effectively triaged, with care being shared between primary care and nephrology. Prior interventions to improve CKD care have involved small sample sizes, lacked a randomized design, or showed only modest intervention effects. Southern California Kaiser Permanente implemented a large population-based program to improve CKD care but observed an increase in visits to nephrologists from 20% to just 24% over a 5-year period.23 Similarly, a study of electronic prompts recommending referral to a nephrologist for patients with eGFR of less than 45 found that the prompts did not impact referral patterns.24 A study of a paper-based CKD checklist found that it was associated with improvement in CKD care, although it involved only 4 PCPs within a single health center.25
Our study findings also highlighted the importance of patient engagement in the management of CKD. We found that a large proportion of patients responding to the survey had not been informed of their CKD, which is consistent with prior research.15 In our study, nephrology consultation was associated with increased patient awareness of their disease. This supports prior findings that accurate diagnosis, likely followed by messaging from a trusted physician, can increase patient awareness.26 The National Kidney Foundation and various federal agencies have also supported population-based programs to improve awareness of CKD, including detection and treatment.27,28 Our program builds on these efforts by combining a program to increase diagnosis and awareness with a set of EHR tools embedded within the workflows of a delivery system to support proactive CKD management.
We also found that nearly one-fifth of patients did not agree with being diagnosed with CKD at the conclusion of our intervention. This general finding is critical to understanding the foundation of development of CKD management programs: the need to first partner with patients in the diagnosis of kidney disease. Our post hoc analyses identified the patient mailings as being of substantial importance in the effectiveness of the intervention. Patients who received these mailings were more likely to achieve the study end points compared with control-arm patients, and the magnitude of these effects was larger than that observed for intervention-arm patients who did not receive the mailings.
We also need to focus on engaging primary care in the management of CKD. Although physicians endorsed strong support for our intervention, only half of physicians in the intervention arm felt comfortable establishing the diagnosis of CKD based on currently recommended criteria. However, our post hoc analyses highlight the importance of primary care, because patients with at least 1 visit to their PCPs were much more likely to receive higher-quality care.
Although we had significant success with this program, it is important to note that we did not impact prescribing of ACE inhibitors and ARBs among all patients. Our lack of intervention effect may have been due to the relatively high rates of prescribing ACE inhibitors and ARBs in both study arms. This suggests that physicians may not require additional intervention, given that there is less room to demonstrate improvement in prescribing. In addition, it may be that the remaining patients not treated with ACE inhibitors or ARBs were deemed at higher risk of the complications of such treatment, outweighing the estimated benefits.
Our study has important limitations. We focused on a chronic condition in which the clinical recommendations are changing and remain under some debate,29,30 including recommendations around defining high-risk patients, which patients to refer to nephrologists, and the precise monitoring parameters for metabolic bone disease with parathyroid hormone and vitamin D testing. Our internal consensus guidelines did end up being slightly different from the published guidelines.
We used the MDRD equation to estimate GFR, which may tend to underestimate GFR and identify a lower-risk population. A recent analysis by the Kaiser Permanente system, which also employs Epic and the MDRD equation, found that use of the CKD Epidemiology Collaboration equation can identify a more targeted patient population that is at higher risk of long-term complications of CKD.31
We also did not have additional information on PCP characteristics, such as time in clinical practice, that may have played a role in our study outcomes. Our patient survey analyses were limited by the lack of information from patients in the control group, which was due to our desire to avoid surveying patients about a diagnosis they may not have received from their physician team. Future surveys should focus on alternative methods to assess the entire population and achieve higher response rates to ensure representative information. In addition, future interventions should focus on how to reach broader patient populations, including those with limited literacy.
Finally, we did not evaluate long-term outcomes, such as mortality or disease progression, as our intervention was just 12 months’ duration and such outcomes take years to present. We did attempt to apply widely used process measures of CKD care, but we recognize that there is active debate regarding which process measures have the best links to clinical outcomes.
We developed an innovative intervention combining electronic decision support and patient outreach that improved quality of care in some areas. Future work should explore how EHRs can be used to improve provider and patient decision making and further collaboration among patients, PCPs, and specialist physicians as part of a comprehensive effort to improve health outcomes and value.Author Affiliations: Division of General Medicine and Primary Care, Brigham and Women’s Hospital (TDS, AMH, EJO, DWB), Boston, MA; Department of Health Care Policy, Harvard Medical School (TDS), Boston, MA; Partners HealthCare System (TDS, DWB), Boston, MA; Renal Division, Beth Israel Deaconess Medical Center (BMD), Boston, MA; Department of Health Policy and Management, Harvard School of Public Health (DWB), Boston, MA; Harvard Vanguard Medical Associates (BMD), Boston, MA.
Source of Funding: Agency for Healthcare Research and Quality (R18 HS018226).
Author Disclosures: Dr Bates reports board membership, consultancies or paid advisory boards, patents received, and royalties not related to this work. His financial interests have been reviewed by Brigham and Women’s Hospital and Partners HealthCare in accordance with their institutional policies. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.
Authorship Information: Concept and design (TDS, DWB, BMD); acquisition of data (TDS, BMD); analysis and interpretation of data (TDS, AMH, EJO, DWB, BMD); drafting of the manuscript (TDS, AMH, EJO); critical revision of the manuscript for important intellectual content (TDS, AMH, EJO, DWB, BMD); statistical analysis (TDS, AMH, EJO); provision of patients or study materials (TDS); obtaining funding (TDS); and supervision (TDS).
Address Correspondence to: Thomas D. Sequist, MD, MPH, Partners Healthcare System, 800 Boylston St, Boston, MA 02199. Email: email@example.com.REFERENCES
1. Coresh J, Selvin E, Stevens LA, et al. Prevalence of chronic kidney disease in the United States. JAMA. 2007;298(17):2038-2047. doi: 10.1001/jama.298.17.2038.
2. Zandi-Nejad K, Brenner BM. Strategies to retard the progression of chronic kidney disease. Med Clin North Am. 2005;89(3):489-509. doi: 10.1016/j.mcna.2004.11.001.
3. Khan S, Amedia CA Jr. Economic burden of chronic kidney disease. J Eval Clin Pract. 2008;14(3):422-434. doi: 10.1111/j.1365-2753.2007.00883.x.
4. 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/NEJMoa041031.
5. Levey AS, Coresh J, Balk E, et al; National Kidney Foundation. National Kidney Foundation practice guidelines for chronic kidney disease: evaluation, classification, and stratification [correction in Ann Intern Med. 2003;139(7):605]. Ann Intern Med. 2003;139(2):137-147. doi: 10.7326/0003-4819-139-2-200307150-00013.
6. Stevens PE, Levin A; Kidney Disease: Improving Global Outcomes Chronic Kidney Disease Guideline Development Work Group Members. Evaluation and management of chronic kidney disease: synopsis of the Kidney Disease: Improving Global Outcomes 2012 Clinical Practice Guideline. Ann Intern Med. 2013;158(11):825-830. doi: 10.7326/0003-4819-158-11-201306040-00007.
7. Vassalotti JA, Centor R, Turner BJ, Greer RC, Choi M, Sequist TD; National Kidney Foundation Kidney Disease Outcomes Quality Initiative. Practical approach to detection and management of chronic kidney disease for the primary care clinician. Am J Med. 2016;129(2):153-162.e7. doi: 10.1016/j.amjmed.2015.08.025.
8. Vest BM, York TR, Sand J, Fox CH, Kahn LS. Chronic kidney disease guideline implementation in primary care: a qualitative report from the TRANSLATE CKD study. J Am Board Fam Med. 2015;28(5):624-631. doi: 10.3122/jabfm.2015.05.150070.
9. Allen AS, Forman JP, Orav EJ, Bates DW, Denker BM, Sequist TD. Primary care management of chronic kidney disease. J Gen Intern Med. 2011;26(4):386-392. doi: 10.1007/s11606-010-1523-6.
10. Minutolo R, De Nicola L, Mazzaglia G, et al. Detection and awareness of moderate to advanced CKD by primary care practitioners: a cross-sectional study from Italy. Am J Kidney Dis. 2008;52(3):444-453. doi: 10.1053/j.ajkd.2008.03.002.
11. 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.2006.04.073.
12. Stevens LA, Fares G, Fleming J, et al. Low rates of testing and diagnostic codes usage in a commercial clinical laboratory: evidence for lack of physician awareness of chronic kidney disease. J Am Soc Nephrol. 2005;16(8):2439-2448. doi: 10.1681/ASN.2005020192.
13. Fox CH, Brooks A, Zayas LE, McClellan W, Murray B. Primary care physicians’ knowledge and practice patterns in the treatment of chronic kidney disease: an Upstate New York Practice-based Research Network (UNYNET) study. J Am Board Fam Med. 2006;19(1):54-61. doi: 10.3122/jabfm.19.1.54.
14. Ravera M, Noberasco G, Weiss U, et al. CKD awareness and blood pressure control in the primary care hypertensive population. Am J Kidney Dis. 2011;57(1):71-77. doi: 10.1053/j.ajkd.2010.08.022.
15. Coresh J, Byrd-Holt D, Astor BC, et al. Chronic kidney disease awareness, prevalence, and trends among U.S. adults, 1999 to 2000. J Am Soc Nephrol. 2005;16(1):180-188. doi: 10.1681/ASN.2004070539.
16. Vassalotti JA, Li S, McCullough PA, Bakris GL. Kidney early evaluation program: a community-based screening approach to address disparities in chronic kidney disease. Semin Nephrol. 2010;30(1):66-73. doi: 10.1016/j.semnephrol.2009.10.004.
17. Dharmarajan SH, Bragg-Gresham JL, Morgenstern H, et al; Centers for Disease Control and Prevention CKD Surveillance System. State-level awareness of chronic kidney disease in the U.S. Am J Prev Med. 2017;53(3):300-307. doi: 10.1016/j.amepre.2017.02.015.
18. Chronic Kidney Disease Surveillance System—United States. CDC website. nccd.cdc.gov/ckd. Updated March 19, 2018. Accessed March 4, 2018.
19. Tseng CL, Kern EF, Miller DR, et al. Survival benefit of nephrologic care in patients with diabetes mellitus and chronic kidney disease.Arch Intern Med. 2008;168(1):55-62. doi: 10.1001/archinternmed.2007.9.
20. Drawz PE, Archdeacon P, McDonald CJ, et al. CKD as a model for improving chronic disease care through electronic health records. Clin J Am Soc Nephrol. 2015;10(8):1488-1499. doi: 10.2215/CJN.00940115.
21. Matsushita K, van der Velde M, Astor BC, et al; Chronic Kidney Disease Prognosis Consortium. Association of estimated glomerular filtration rate and albuminuria with all-cause and cardiovascular mortality in general population cohorts: a collaborative meta-analysis. Lancet. 2010;375(9731):2073-2081. doi: 10.1016/S0140-6736(10)60674-5.
22. Chronic kidney disease tests & diagnosis. National Institute of Diabetes and Digestive and Kidney Diseases website. nkdep.nih.gov/learn/testing/understand-gfr.shtml. Published October 2016. Accessed March 4, 2018.
23. Rutkowski M, Mann W, Derose S, et al. Implementing KDOQI CKD definition and staging guidelines in Southern California Kaiser Permanente.Am J Kidney Dis. 2009;53(3 suppl 3):S86-S99. doi: 10.1053/j.ajkd.2008.07.052.
24. Abdel-Kader K, Fischer GS, Li J, Moore CG, Hess R, Unruh ML. Automated clinical reminders for primary care providers in the care of CKD: a small cluster-randomized controlled trial. Am J Kidney Dis. 2011;58(6):894-902. doi: 10.1053/j.ajkd.2011.08.028.
25. Mendu ML, Schneider LI, Aizer AA, et al. Implementation of a CKD checklist for primary care providers. Clin J Am Soc Nephrol. 2014;9(9):1526-1535. doi: 10.2215/CJN.01660214.
26. Szczech LA, Stewart RC, Su HL, et al. Primary care detection of chronic kidney disease in adults with type-2 diabetes: the ADD-CKD Study (awareness, detection and drug therapy in type 2 diabetes and chronic kidney disease). PLoS One. 2014;9(11):e110535. doi: 10.1371/journal.pone.0110535.
27. Narva AS, Briggs M, Jordan R, Pavkov ME, Burrows NR, Williams DE. Toward a more collaborative federal response to chronic kidney disease. Adv Chronic Kidney Dis. 2010;17(3):282-288. doi: 10.1053/j.ackd.2010.03.006.
28. Whaley-Connell AT, Vassalotti JA, Collins AJ, Chen SC, McCullough PA. National Kidney Foundation’s Kidney Early Evaluation Program (KEEP) annual data report 2011: executive summary. Am J Kidney Dis. 2012;59(3 suppl 2):S1-S4. doi: 10.1053/j.ajkd.2011.11.018.
29. Levey AS, Eckardt KU, Tsukamoto Y, et al. Definition and classification of chronic kidney disease: a position statement from Kidney Disease: Improving Global Outcomes (KDIGO). Kidney Int. 2005;67(6):2089-2100. doi: 10.1111/j.1523-1755.2005.00365.x.
30. Levey AS, Inker LA, Coresh J. Chronic kidney disease in older people. JAMA. 2015;314(6):557-558. doi: 10.1001/jama.2015.6753.
31. Sim JJ, Batech M, Danforth KN, Rutkowski MP, Jacobsen SJ, Kanter MH. End-stage renal disease outcomes among the Kaiser Permanente Southern California creatinine safety program (Creatinine SureNet): opportunities to reflect and improve. Perm J. 2017;21:16-143. doi: 10.7812/TPP/16-143.