• We are sorry, but NCBI web applications do not support your browser and may not function properly. More information
Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Am J Kidney Dis. Author manuscript; available in PMC Apr 1, 2012.
Published in final edited form as:
PMCID: PMC3073568
NIHMSID: NIHMS257509

Recognition and Management of CKD in Primary Care

To the editor

Primary care physicians care for the majority of patients with chronic kidney disease (CKD)1 and bear much of the responsibility for optimizing early management.2 In 2002, guidelines were issued by the National Kidney Foundation’s Kidney Disease Outcomes Quality Initiative (KDOQI) for the classification of CKD based on estimated glomerular filtration rate (eGFR)3 and for the management of patients with CKD. The impact of these guidelines on primary care has not been well studied. Here we report adherence to these guidelines in a large nationwide sample of primary care practices.

This descriptive study was performed in the Practice Partner Research Network, a practice-based research network of primary care providers that use a common electronic medical record (EMR)4, comprising 120 practices submitting data as of March 31, 2009. These practices represent 481 physicians and 141 mid-level providers in 38 U.S. states; of these practices, 72% are family medicine, 20% internal medicine, and 8% combined primary care practices. All active patients over 18 years of age with a serum creatinine, age and gender documented in their EMR within a year as of March 31, 2009 were included. The Institutional Review Board at the Medical University of South Carolina approved the study. Age, gender, race (if available), and most recent measurement of serum creatinine, blood pressure, and low density lipoprotein cholesterol were obtained from the database. Diagnoses of hypertension, diabetes mellitus and CKD either obtained from documentation on a progress note title, problem list, or diagnosis code in the EMR were ascertained. We also determined whether an ACE inhibitor, ARB, lipid-lowering medication or non-steroidal anti-inflammatory drug had been recorded in the EMR medication list in the past year.

eGFR was calculated using the four variable Modification of Diet in Renal Disease (MDRD) Study equation. 3, 5 If a patient’s race was not available, he/she was assumed to be non-black for the eGFR calculation. For these analyses, patients were considered to have CKD if their calculated eGFR was <60 ml/min/1.73 m2, and were further categorized as stage 3a (eGFR 45–59 ml/min/1.73 m2), 3b (eGFR 30–44 ml/min/1.73 m2), 4 (eGFR 15–29 ml/min/1.73 m2) or 5 (eGFR < 15 ml/min/1.73 m2) CKD.

All statistical analyses were performed using SAS v9.1 (www.sas.com). Comparisons between patient subgroups were performed using generalized linear mixed models, which accounted for the clustering of patients within practices.

Among 518,531 active adult patients in 120 practices, 237,906 patients met inclusion criteria; demographics and clinical characteristics are detailed in Table 1. Among the 26.9% of patients with race recorded, 83.1% were white and 8.4% were black. 34,644 (14.6%) had eGFR <60. Compared to patients with eGFR≥60, patients with eGFR <60 were significantly more likely to be older (70.9±12.8 vs 52.9±15.8 years, p<0.001), female (66.6% vs. 55.3%, p<0.001), have a diagnosis of hypertension (67.9% vs. 38.8%, p<0.001), or have a diagnosis of diabetes (28.9% vs. 15.2%, p<0.001). The majority of patients with eGFR <60 had stage 3a or 3b CKD, did not have a documented diagnosis of diabetes, and had serum creatinine values in the near normal range. There was significant (p<0.001) variation among practices with regard to diagnoses of CKD recorded in the EMR. Adherence to recommended practice guidelines and achievement of therapeutic targets is detailed in Table 2. Over half of all patients with eGFR <60 had been prescribed an ACE inhibitor or ARB or a lipid lowering medication, yet a minority of patients met treatment goals for blood pressure or lipid control.

Table 1
Sample Demographic and Clinical Characteristics Stratified by eGFR
Table 2
Adherence to Practice Guidelines and Achievement of Therapeutic Targets in Patients with eGFR <60

Our study has several limitations. By using a patient’s most recent creatinine to calculate eGFR, we may be misclassifying some patients with acute kidney injury as having CKD. Second, we did not obtain data regarding the presence of proteinuria, and we were unable to include an assessment of albuminuria or other markers of kidney damage that may indicate the presence of CKD. Furthermore, because race was only available for 26.9% of our population, patients for whom race was not recorded were assumed to be non-black for the eGFR calculation, potentially leading to underestimation of eGFR and over-diagnosis of CKD in some black patients. Lastly, there is some debate about the validity of using the MDRD Study equation to define CKD, particularly for older female patients.6 There is also uncertainty associated with the estimation in the equation.7, 8

CKD is common in primary care practices. Although the KDOQI guidelines were designed to improve early detection and management, CKD continues to be under-recognized by primary care physicians. Suboptimal achievement of therapeutic targets may be due to under-diagnosis of CKD in this sample. Interventions to improve the recognition and management of patients with CKD in primary care could have a major public health impact.

Acknowledgments

Support: Dr. Litvin has been supported under a Health Services Research Fellowship at the Medical University of South Carolina, funded by a National Research Service Award for Primary Medical Care from the Health Resources Services Administration (T32HP10255). This study was also funded in part by the Agency for Healthcare Research and Quality Ambulatory Safety and Quality Program, Improving Quality through Clinician Use of Health Information Technology (1R18HS017037-01) and by the National Center for Research Resources (UL1RR029882). The funding agencies had no role in the study design; in collection, analysis, and interpretation of data; in writing of the report; and in the decision to submit the manuscript for publication.

Footnotes

Financial Disclosure: Drs. Wessell and Ornstein have been consultants for McKesson Practice Partner, the vendor of the EMR used by practices in this study. The remaining authors declare that they have no relevant financial interests.

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

References

1. 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 Jan-Feb;19(1):54–61. [PubMed]
2. Shahinian VB, Saran R. The role of primary care in the management of the chronic kidney disease population. Adv Chronic Kidney Dis. 2010 May;17(3):246–253. [PubMed]
3. K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Am J Kidney Dis. 2002 Feb;39(2 Suppl 1):S1–266. [PubMed]
4. Ornstein S, Jenkins RG, Nietert PJ, et al. A multimethod quality improvement intervention to improve preventive cardiovascular care: a cluster randomized trial. Ann Intern Med. 2004 Oct 5;141(7):523–532. [PubMed]
5. Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group. Ann Intern Med. 1999 Mar 16;130(6):461–470. [PubMed]
6. Glassock RJ, Winearls C. An epidemic of chronic kidney disease: fact or fiction? Nephrol Dial Transplant. 2008 Apr;23(4):1117–1121. [PubMed]
7. Coresh J, Astor BC, McQuillan G, et al. Calibration and random variation of the serum creatinine assay as critical elements of using equations to estimate glomerular filtration rate. Am J Kidney Dis. 2002 May;39(5):920–929. [PubMed]
8. Levey AS, Stevens LA, Schmid CH, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009 May 5;150(9):604–612. [PMC free article] [PubMed]
PubReader format: click here to try

Formats:

Related citations in PubMed

See reviews...See all...

Cited by other articles in PMC

See all...

Links

  • Compound
    Compound
    PubChem Compound links
  • PubMed
    PubMed
    PubMed citations for these articles
  • Substance
    Substance
    PubChem Substance links