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Chronic kidney disease in an Alaska Native/American Indian statewide healthcare network.

Goal

To determine the occurrence of chronic kidney disease (CKD) in a Alaskan Native/ American Indian healthcare network by comparing two differing diagnostic methods.

Objectives

1. Discuss the differences between various methodologies for determining CKD.

2. Explain how CKD is defined by the NKF (2002) KDOQI guidelines.

3. Determine the impact of this study on nephrology nursing practice.

Chronic kidney disease (CKD) affects between 6.3% and 9.2% of American adults depending upon the method used for diagnosis (United States Renal Data System [USRDS], 2013a). The incidence rate of CKD in the Medicare population increased to 10% in 2011, which represents an increase from 8.5% in 2009 and from 3.3% in 1998 (USRDS, 2013a). Despite this rise, little is known about the occurrence of CKD in specific cultural groups, and because data are more readily available, researchers have focused on studying end stage renal disease (ESRD). As a result of those efforts, the USRDS (2013a) report indicates that Alaskan Native/American Indian people have been diagnosed with ESRD more than other ethnic groups (Caucasian, African American, or Asian). For example, Alaskan Native/ American Indian people are 1.6 times more likely to experience ESRD than Caucasians; however, the comparable numbers for the precursor condition, CKD, are not known in this population.

This study highlights two methods of diagnosing CKD. Appropriate diagnosis is defined by the National Kidney Foundation (NKF) (2002) Kidney Disease Outcome Quality Initiative (KDOQI) clinical guideline, which states that two abnormal measures (blood and/or urine tests) indicate impaired renal function. Persistent proteinuria can represent a kidney disorder requiring further testing; however, it may represent a benign self-limiting condition, such as symptomatic urinary tract infection (Carter, Tomson, Stevens, & Lamb, 2006), or it may be the result of vigorous exercise (Poortmans, 1984). The USRDS (2013a) determines prevalence based in part on ICD-9 codes reported with patient claims data (Medicare and insurance) and from National Health and Nutrition Examination Survey (NHANES) data summarized for the years 2005 to 2010; however, the use of ICD-9 codes presumes that the diagnosis was applied in accordance with the NKF KDOQI guidelines. It requires knowledge of CKD occurrence in the population for clinicians to monitor outcomes of care for patients with CKD. There is a need to understand the difference in occurrence of CKD determined using a single measure (the method used in large published research) versus the occurrence using multiple measures (the method consistent with current clinical guidelines and diagnostic practice).

CKD is largely silent, progressive, and unrecognized in the general population because many necessary screening tests are not ordered in high-risk groups. For example, even though urine protein should be monitored in those at risk of CKD (NKF, 2002), the USRDS (2013a) reports that patients with hypertension or cardiovascular disease are five to six times more likely to receive serum creatinine testing rather than measurement of urine protein. The more effective urine albumin testing is often not ordered for those at risk. Ineffective screening of those at risk contributes to CKD not being recognized. In spite of the large portion of unrecognized disease, CKD represents 18% of Medicare expenditures, which is up from 3.8% in 1993 (USRDS, 2013a). As the disease progresses, CKD has been associated with deadly complications, such as cardiovascular disease, stroke, and infection (USRDS, 2013a). Finally, progressive CKD leads to ESRD, a condition that is very debilitating and costly.

This study sought to answer the following research questions. For a sample of Alaskan Native/American Indian people who have had laboratory data collected in a tertiary referral center:

* What is the occurrence of CKD?

* What is the difference in occurrence of CKD using one measure versus repeat/multiple measures?

Literature Review

Diagnosis of CKD

The focus of this study was CKD, which is defined by the NKF (2002) KDOQI guidelines as either normal or slightly decreased estimated glomerular filtration rate (eGFR) with evidence of kidney damage or eGFR of less than 60 mL/min/1.73[m.sup.2] regardless of evidence of kidney damage. The guidelines further state that these markers must be persistent, meaning they must be present for at least three months. Two clinical markers that determine the stage of kidney disease are eGFR and proteinuria/ albuminuria.

Both the National Kidney Disease Education Program (NKDEP) (2012) and the NKF (2002) KDOQI guidelines define CKD as eGFR of less than 60 mL/min/1.73[m.sup.2] (to diagnose Stages 3 to 5). To diagnose CKD Stages 1 and 2, eGFR along with markers of kidney damage are used. The NKF KDOQI clinical guideline defines kidney damage as structural abnormalities of the kidney that can lead to reduced function. The guideline further states that persistent proteinuria is a marker of kidney damage that does not require clinical correlation. Since persistent proteinuria (present greater than three months) is a marker of kidney damage, this value can be used to diagnose CKD Stages 1 and 2 (NKF, 2002). For example, the eGFR may be within normal limits, but the presence of persistent proteinuria or albuminuria is diagnostic for CKD Stage 1. CKD Stage 2 is only mildly decreased eGFR with the presence of kidney damage, such as proteinuria (NKF, 2002). Table 1 shows NKF KDOQI diagnostic guidelines, as well as the recommended actions for providers.

The use of eGFR to estimate kidney function has limitations. The Modification of Diet in Renal Disease (MDRD) equation has not been validated for Alaskan Native/American Indian people. Herget-Rosenthal, Bokenkamp, and Hofmann (2007) point out that the MDRD equation was validated in those with stable CKD (eGFR of approximately 40 mL/min/1.73[m.sup.2]). Thus, the eGFR is underestimated (meaning kidney function is better than reflected by the number) in those with potentially healthy kidneys (eGFRs above 60mL/min/1.73[m.sup.2]). Conversely, the eGFR is overestimated (meaning kidney function is worse than reflected by the number) for those with advanced CKD (eGFRs below 20 mL/min/ 1.73[m.sup.2]) and in those with low muscle mass or nephrotic-range proteinuria. Even with these limitations, the MDRD equation is the tool used in the NKF KDOQI guideline and has been accepted by nephrology specialists universally (NKDEP, 2012). In 2012, the USRDS published data using a more precise equation to estimate glomerular function, the CKD Epidemiology Collaboration Equation (CKDEPI) (USRDS, 2013a). It uses the same variables to determine eGFR as the MDRD equation, but it more accurately identifies individuals with respect to long-term clinical risk (Levey et al., 2009). This new equation has not been accepted widely and has not replaced the MDRD in practice nor in the clinical guidelines. Therefore, this study applied the MDRD equation to the data collected.

The USRDS 2013 annual report includes data from several sources to describe CKD nationwide with NHANES and claims data (insurance and Medicare) (USRDS, 2013a). NHANES data are a compilation of physical examination findings and interview responses (regarding health and habits) from a "snapshot" or an assessment at one point in time across the country (Centers for Disease Control and Prevention [CDC], 2011). Data are analyzed by multiple researchers to determine health outcomes and disease prevalence. In the case of NHANES data, the use of a single measure to diagnose CKD is not consistent with clinical guidelines. In the case of Medicare/insurance claims data, there is no way to verify/validate guideline adherence in making the diagnosis. Accurate diagnosis of CKD requires both repeat measures and awareness of the current clinical guidelines; therefore, the method of diagnosing CKD can influence the prevalence in Medicare/insurance claims and NHANES data included in the USRDS report.

CKD and Alaskan Native/American Indian People in the Literature

Information about the characteristics associated with CKD in Alaskan Native/American Indian people living in the northern United States is lacking. Considering national sources, NHANES findings do not include Alaskan Native/American Indian subgroups because the sample size is too small, and thus, suppressed (Klein, Proctor, Boudreault, & Turczyn, 2002). Further, the USRDS Renal Data Extraction and Referencing System (USRDS, 2013b), which includes a searchable database, displays only 78 cases when searching for kidney disease due to any cause in Native Americans residing in Northern States.

Published studies of CKD in the aggregate on the Native American population have been conducted on Southwestern reservations (Lucove, Vupputuri, Heiss, North, & Russell, 2008; Narva, 2008; Pavkov et al., 2006; Scavini et al., 2007). Multiple studies have described risk factors for CKD, including diabetes and heart disease in Alaskan Native/American Indian people (Carter, MacCluer et al., 2006; Lucove, et al., 2008; Nobmann et al., 2005). While Jolly et al. (2009) studied risk factors for CKD in Alaskan Native/American Indian people using the NKF national database, no descriptive studies were found on Alaskan Native/American Indian people living in this Alaskan Native/American Indian with a diagnosis of CKD.

Methods

This was a descriptive, comparative, retrospective study of CKD occurrence in the Alaskan Native/ American Indian people using two diagnostic methods. Four key variables determined diagnosis of CKD. These included eGFR, two types of proteinuria measurements (urinalysis or spot protein to creatinine ratio, measuring grams of total protein), and albuminuria (using microalbumin to creatinine ratio of 30 mg/G or higher creatinine). Blood and urine samples were collected in the normal course of care for one year and were analyzed in 2009 and 2010. Alaskan Native/American Indian adults with no eGFR (no blood was drawn), only one eGFR, or eGFR result collected less than 90 days apart, but with persistent proteinuria for at least 90 days, were placed into CKD unknown stage. This was done because the exact stage of CKD is correctly diagnosed with two eGFR values collected at least 90 days apart. Additionally, proteinuria occurring at least twice at an interval of 90 days is an indicator of kidney damage even if there are no blood samples collected. Institutional Review Board and appropriate approvals were obtained.

Sample and Setting

The Statewide Native Tribal Health Consortium includes several tribal health networks and the Alaskan Native Medical Center (ANMC), a 150-bed facility in an urban center serving 136,065 Alaskan Native/American Indian people in the statewide healthcare network at the time of this study. To be eligible for care and included in the study, participants had to have documentation of eligibility for care at the center according to facility guidelines. As a tertiary referral center, ANMC provides inpatient and outpatient care, as well as primary and specialty services. When impaired kidney function is suspected, individuals throughout the network are referred to a renal specialist in the urban center for follow-up and treatment.

The sample was a convenience sample of Alaskan Native/American Indian adults over 20 years of age with both inpatient and outpatient lab results in the ANMC computer system for the full calendar year under study and analyzed between 2009 and 2010. Non-Alaskan Native/ American Indian lab results were excluded, and the sample was queried to remove all those with city of residence outside the state. Once the sample was established, it was searched for those with eGFR and proteinuria results. To insure a focus on CKD, those dependent on renal dialysis with ESRD and pregnant women at risk of transient proteinuria were excluded.

Results

Data were analyzed using IBM SPSS version 18. A total of 15,515 Alaskan Native/American Indian individuals met the inclusion criteria and were included in the study. The modal age in this sample was 40 years, with 60.4% female and 39.6% male.

Multiple/Repeat Measures For Diagnosing CKD

After applying the current NKF (2002) KDOQI guideline, CKD (including all stages) occurred in 780 (5%) of 15,515 Alaskan Native/American Indian adults in the sample or 50.3 cases per 1000. Figure 1 depicts the frequency of CKD Stages 1 to 5 among those with CKD. CKD Stage 3 (n = 309, 39.62%) was most common followed by Stage 1 (n= 206, 26.41%) for all age groups. A substantial number of those in the sample (n = 132,16.92%) had persistent proteinuria, indicating kidney damage, but could not be staged due to blood samples not meeting KDOQI diagnostic criteria.

The sample was reported by age in groups of 10 years. Figure 2 displays the frequency of all stages of CKD for each age group starting with age 20. CKD Stage 3 was most prevalent, occurring most frequently in the seventh decades of life, while CKD Stage 1 was most common among the middle-aged group (40 to 59). The youngest age group (20 to 39) had the highest occurrence of the unknown stage of CKD.

In this sample, CKD was more often found in females than in males (65% to 35%, respectively). Figure 3 depicts the various stages of CKD by gender. Most females had CKD Stage 3 followed by CKD Stage 1. Stages 2 and 4 were virtually equal between genders, with more males having CKD Stage 5 in this sample.

Single Measures For Diagnosing CKD

CKD occurrence using the single measure method for all stages was 1,492 of 15,515 (9.6%) or 96.2 cases per 1,000. Figure 4 displays both the repeat/multiple measure occurrence as well as the single measure occurrence.

Multiple Measure vs. Single Measure of Diagnosing CKD

The results indicate that in this sample, CKD was overstated by 91.3% when a single measure was used for diagnosis (9.6% or 96.2 cases per 1,000) compared to the use of multiple measures (5.0% or 50.3 cases per 1,000).

Discussion

The prevalence in the United States for all stages of CKD is reported using single measures and it ranges between 6.3 and 9.2% (USRDS, 2013a). While this was not an epidemiologic study, the occurrence of CKD for this sample of Alaskan Native/American Indian people is lower than the published national average when using repeat measures (5%) and similar when using single measures (9.6%) for diagnosis.

While every person with CKD does not require care in a nephrology specialty practice, early recognition and awareness of CKD, especially for individuals in Stages 1 and 2, allows for interventions aimed toward preventing progression to higher stages. This sample includes 132 Alaskan Native/ American Indian individuals with persistent proteinuria who lack eGFR measurement or eGFR measures meeting criteria. This makes it impossible to determine the stage of CKD, and it suggests these individuals may not have received proper follow up and treatment, highlighting the need for improvement in provider practices.

Limitations

This study employed a large convenience sample of laboratory data at a tertiary care center; the occurrence may be an over-estimate compared to the general population because the most seriously ill would seek care at this location. The occurrence may also be an underestimate because not all Alaskan Native/American Indian people living in the state seek care at ANMC (e.g., those living closer to the next-largest urban center, those living in rural areas, and those with private insurance). Only one year of lab results was collected and analyzed. Because repeat measures over a three-month span of time were required for appropriate CKD diagnosis, it is possible that Alaskan Native/American Indian individuals were not included due to the cut off at the beginning or the end of the year. It is also possible that data collected with single measures were simply not recognized as requiring a follow-up test to determine persistence of proteinuria. The results cannot be generalized to all Alaskan Native/American Indian people.

Future Research

More research is needed to understand the occurrence of CKD in Alaskan Native/American Indian individuals. Researchers should consider alternative ways to access laboratory data to better reflect CKD occurrence statewide. Strategies could be employed to insure that individuals with proteinuria receive follow-up testing in accordance with published guidelines. Risk factors for CKD, such as hypertension and diabetes, could be better described and disseminated for those with and without CKD.

Additional years of lab results could be queried to study the progression of CKD and contributing factors. Future study could encourage improved identification and treatment of CKD in this population. The eGFR calculation has not been validated in the Alaskan Native/ American Indian people, and future research could help determine whether the current method of estimating glomerular filtration is appropriate in this group. Further study of CKD identification in primary care could reveal the best educational techniques for primary care providers.

Nursing Implications

Clinicians must be aware of current clinical guidelines regarding correct diagnosis and follow up of those with or at risk of kidney disease. This study demonstrates that clinical agencies can develop CKD occurrence benchmarks that are consistent with clinical diagnosis guidelines. However, these benchmarks will not be comparable to the prevalence rates published nationally.

Conclusions

Adhering to clinical guidelines is critical to accurately diagnose CKD. In this study, two methods of diagnosing CKD were compared. Applying clinical practice guidelines to determine CKD resulted in a lower occurrence than published studies. There is a need for clinician education to insure all patients are identified and receive follow up. Finally, this project has tested an important method of testing the effectiveness of nephrology services in a statewide network serving Alaskan Native/ American Indian people.

Acknowledgment: The authors would like to acknowledge Dr. Stefano Emili for manuscript review and Ms. Eileen Miller for information systems support.

References

Carter, E.A., MacCluer, J.W., Dyke, B., Howard, B.V., Devereux, R.B., Ebbeson, S.O., & Resnick, H.E.. (2006). Diabetes mellitus and impaired fasting glucose in Alaska Eskimos: The Genetics of Coronary Artery Disease in Alaska Natives (GOCADAN) study. Diabetologia, 49(1), 29-35. doi:10.1007/s00125005-0071-9

Carter, J.L., Tomson, C.R., Stevens, RE., & Lamb, EJ. (2006). Does urinary tract infection cause proteinuria or microalbuminuria? A systematic review. Nephrology Dialysis Transplantation, 27(11), 3031-3037. doi:10. 1093/ndt/gf1373

Centers for Disease Control and Prevention (CDC). (2011). National Health and Nutrition Examination survey: Welcome NHANES participants. Atlanta: Author. Retrieved from http://www.cdc.gov/nchs/nhanes/pa rticipant.htm

Herget-Rosenthal, S., Bokenkamp, A., & Hofmann, W. (2007). How to estimate GFR-serum creatinine, serum cystatin C or equations? Clinical Biochemistry, 40, (3-4), 153-161. doi:10. 1016/j.clinbiochem.2006.10.014

Jolly, S.E., Li, S., Chen, S.C., Narva, A.S., Jurkovitz, C.T., Norris, K.C., & Shlipak, M.G. (2009). Risk factors for chronic kidney disease among American Indians and Alaska Natives--Findings from the Kidney Early Evaluation Program. American Journal of Nephrology, 25(5), 440-446. doi: 10.1159/000174857

Klein, RJ., Proctor, S.E., Boudreault, M.A., & Turczyn, K.M. (2002). Healthy People 2010 criteria for data suppression. Statistical Notes, 24. Hyattsville, MD: National Center for Health Statistics. Retrieved from http ://www.cdc.gov/nchs/data/statnt/statnt24.pdf

Levey, A.S., Stevens, L.A., Schmid, C.H., Zhang, Y.L., Castro, A.F., Feldman, H.I., ... Coresh, J. (2009). A new equation to estimate glomerular filtration rate. Annals of Internal Medicine, 750(9), 604-612.

Lucove, J., Vupputuri, S., Heiss, G., North, K., & Russell, M. (2008). Metabolic syndrome and the development of CKD in American Indians: The strong heart study. American Journal of Kidney Disease, 57(1), 21-28. doi: 10.1053/j.ajkd.2007. 09.014

Narva, A. (2008). Reducing the burden of chronic kidney disease among American Indians. Advances in Chronic Kidney Disease, 75(2), 168-173. doi:10.1053/j.ackd.2008.01.011

National Kidney Disease Education Program (NKDEP). (2012). About NKDEP. Bethesda, MD: The National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health. Retrieved from http://www.nkdep. nih.gov/about/index.htm

National Kidney Foundation (NKF). (2002). KDOQI clinical practice guidelines for chronic kidney disease: Evaluation, classification, and stratification. American Journal of Kidney Diseases, 39(2, Suppl. 1), S1-S4. Retrieved from http://www.kidney. org/ professionals/KDOQI/guidelines_ckd/p4_class_g1.htm

Nobmann, E.D., Ponce, R., Mattil, C., Devereux, R., Dyke, B., Ebbesson, S.O.,... Howard, B.V. (2005). Dietary intakes vary with age among Eskimo adults of Northwest Alaska in the GOCADAN study, 2000-2003. The Journal of Nutrition, 755(4), 856-862.

Pavkov, M.E., Knowler, W.C., Bennett, P.H., Looker, H.C., Krakoff, J., & Nelson, R.G. (2006). Increasing incidence of proteinuria and declining incidence of end-stage renal disease in diabetic Pima Indians. Kidney International, 70, 1840-1846. doi: 10. 1038/sj.ki.5001882

Poortmans, J.R. (1984). Exercise and renal function. Sports Medicine, 7(2), 125153. Scavini, M., Stidley, C.A., Paine, S.S., Shah, V.O., Tentori, F., Bobelu, A., ... Zager, P.G. (2007). The burden of chronic kidney disease among the Zuni Indians: The Zuni Kidney Project. Clinical Journal of the American Society of Nephrology, 2(3), 509-516. doi: 10.2215/CJN.02780806

United States Renal Data System (USRDS). (2013a). USRDS annual data report. Volume 7 Atlas of chronic kidney disease in the United States. Bethesda, MD: National Institutes of Health. National Institute of Diabetes & Digestive & Kidney Diseases. Division of Kidney, Urologic, & Hematologic Diseases. Retrieved from http://www.usrds.org/atlas.aspx

United States Renal Data System (USRDS). (2013b). Renal data extraction and referencing. Bethesda, MD: National Institutes of Health. National Institute of Diabetes & Digestive & Kidney Diseases. Division of Kidney, Urologic, & Hematologic Diseases. Retrieved from http://www.usrds.org/odr/xrender_home.asp

Robin Bassett, MS, ANP, RN, is an Advanced Nurse Practitioner, Internal Medicine, Nephrology, Alaska Native Medical Center, Anchorage, AK; a Captain, United States Public Health Service; and a member of ANNA's Northern Lights Chapter. She may be contacted directly via email at rabassett@anthc.org

Maureen O'Malley, PhD, RN, is an Associate Director and Associate Professor, University of Alaska Anchorage School of Nursing, Anchorage, AK.

Statement of Disclosure: The authors reported no actual or potential conflict of interest in relation to this continuing nursing education activity.

Note: Additional statements of disclosure and instructions for CNE evaluation can be found on page 415.

This offering for 1.3 contact hours is provided by the American Nephrology Nurses' Association (ANNA).

American Nephrology Nurses' Association is accredited as a provider of continuing nursing education by the American Nurses Credentialing Center Commission on Accreditation.

ANNA is a provider approved by the California Board of Registered Nursing, provider number CEP 00910.

This CNE article meets the Nephrology Nursing Certification Commission's (NNCC's) continuing nursing education requirements for certification and recertification.
Table 1
Stages of Chronic Kidney Disease: A Clinical Action Plan

                                  GFR (mL/min/
Stage   Description              1.73 [m.sup.2])   Action *

1       Kidney damage with        90 or higher     Diagnosis and
        normal or increased                        treatment, treatment
        GFR                                        of comorbid
                                                   conditions, slowing
                                                   progres- sion, CVD
                                                   risk reduction

2       Kidney damage with          60 to 89       Estimating
        mild or decreased GFR                      progression

3       Moderate decreased GFR      30 to 59       Evaluating and
                                                   treating
                                                   complications

4       Severe decreased GFR        15 to 29       Preparation for
                                                   kidney replacement
                                                   therapy

5       Kidney failure            Less than 15     Replacement (if
                                  (or dialysis)    uremia present)

Notes: Chronic kidney disease is defined as either kidney damage
or GFR less than 60 mL/min/1.73 [m.sup.2] for three months or
longer. Kidney damage is defined as pathologic abnormalities or
markers of damage, including abnormalities in blood or urine
tests or imaging studies.

GFR = glomerular filtration rate, CVD = cardiovascular disease.

* Includes action from preceding stages

Source: National Kidney Foundation, 2002. Reprinted with permission.

Figure 1
Occurrence of CKD by Stage using KDOQI Repeat Measures
(n = 780)

Stage 1        206      26.4%
Stage 2         79      10.1%
Stage 3        309      39.6%
Stage 4         45       5.8%
Stage 5          9       1.2%
Unknown        132      16.9%

Stages of CKD

Note: Table made from bar graph.

Figure 2
Frequency of CKD by Age Range for All Stages using
KDOQI Repeat Measures (n = 780)

20 to 29       80     10.3%
30 to 39       70      9.0%
40 to 49      124     15.9%
50 to 59      125       16%
60 to 69      142     18.2%
70 to 79      160     20.5%
80 to 89       72      9.3%
90 to 99        7      0.9%

Age Ranges

Note: Table made from bar graph.

Figure 3
Frequency of CKD by Gender for All
Stages using KDOQI Repeat Measures (n = 780)

               Female          Male

1           128    16%       78    10%
2            42   5.4%       37   4.7%
3           202    26%      107    14%
4            23     3%       22   2.8%
5             3   0.4%        6   0.8%
Unknown     112    14%       20   2.5%

Stages of CKD

Note: Table made from bar graph.

Figure 4

CKD Occurrence by Stage: KDOQI Repeat Measure (n = 780)
Compared to Single Measure Occurrence (n = 1,492)

             Single Measures     Repeat Measures

1             503   33.7%          206   26.4%
2             169   11.3%           79     10%
3             714   47.9%          309   39.6%
4              82    5.5%           45    5.7%
5              24    1.6%            9    1.2%
Unknown                            132   16.9%

Note: Table made from bar graph.
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Title Annotation:CNE: Continuing Nursing Education
Author:Bassett, Robin; O'Malley, Maureen
Publication:Nephrology Nursing Journal
Geographic Code:1U9AK
Date:Jul 1, 2014
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