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Microalbumin Testing in
Patients with Type-2 Diabetes: A SPUR-Net Study
P. Kay Champion, Jr., M.D., Grace M. Kuo,
Pharm.D., Anthony Greisinger, Ph.D., Jeffrey R. Steinbauer, M.D.
Background
Diabetes
mellitus is increasing considerably in the United States. Today, it affects 17 million people or about
6.5% of the population (1). Diabetes is
also the foremost cause of end-stage renal disease (ESRD) in the United States,
accounting for 40% of cases (2). The
incidence of ESRD is expected to double by 2010. ESRD is a costly complication,
in terms of both morbidity and health-care costs. In 1998, ESRD-related health-care costs totaled $12 billion; by
2010, they may rise to $28 billion (3).
Each year, from 5% to 10% of patients with type-2 diabetes and microalbuminuria
(range, 30 mg to 300 mg per 24 hr) develop
diabetic nephropathy (4-6).
Patients with type-2 diabetes and microalbuminuria have a 10 to 20 times
greater risk of developing diabetic nephropathy than do those with
normoalbuminuria (4-6). For patients
with type-2 diabetes and elevated systolic blood pressure, having
microalbuminuria doubles the chances of developing diabetic nephropathy. For these reasons, early identification and
subsequent renal-protective treatment of diabetic patients with
microalbuminuria is critical (7). The
progression of nephropathy can be retarded in diabetic patients who have both
hypertension and microalbuminuria by treating them with either
angiotensin-converting enzyme inhibitors (8, 9) or with angiotensin receptor
blockers (10, 11, 13). Studies have shown that the benefit of these drugs is
independent of their blood pressure-lowering effects, specifically as a
blockade of the rennin-angiotensin system (8-11, 13), and have
supported the finding that antihypertensive treatment has a renoprotective
effect in these patients.
SPUR-Net
conducted this study to assess how often primary care physicians perform
microalbumin testing according to the American Diabetes Association guidelines
and whether diabetic patients with microalbuminuria were being treated
appropriately. We wanted to determine
if primary care physicians in three different diabetes care programs tested for
and treated microalbuminuria differently.
Methods
Three of four constituent members of
SPUR-Net, a primary care practice-based research network in Houston, Texas,
participated in this study.
Microalbumin screening was carried out in the three participating
SPUR-Net clinics in three different ways: 1) by using an automated physician
reminder system without an electronic medical record; 2) by using an electronic
medical record without a physician reminder system; and 3) by using a paper
medical record without a physician reminder system. For the purposes of this study, a microalbuminuria test was
defined as a quantitative test that measured the amount of protein detected in
the urine. A ChemstripÒ MicralÒ Test (Roche), a qualitative test specifically
designed to detect microalbuminuria, was also considered to be an acceptable
test. Conventional qualitative tests
designed to detect albuminuria; however, were not considered to be appropriate
for the detection of microalbuminuria unless the test result was positive for
protein (i.e., protein excretion >300mg/24 hr or >200 mg/min or >300 mg/mg creatinine).
A
random sample of eligible patients was generated for each clinical site. Data were collected by using two
methods: 1) computer database retrieval
to identify patients and to review available computerized clinical laboratory
results, and 2) manual chart review. A
team of research assistants reviewed a total of 309 medical records
(approximately 100 from each organization).
Results
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Table 1. Descriptive Characteristics of the Study Sample
(n=309)
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|
Variables
|
Frequency
|
|
|
|
|
Clinic
|
|
|
1
|
101 (32.7%)
|
|
2
|
100 (32.3%)
|
|
3
|
108 (35.0%)
|
|
Gender
|
|
|
Male
|
109 (35.3%)
|
|
Female
|
200 (64.7%)
|
|
Age (yrs)
|
|
|
40 – 49
|
55 (17.8%)
|
|
50 – 59
|
111 (35.9%)
|
|
60 – 69
|
95 (30.7%)
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|
> 70
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48 (15.5%)
|
|
Race/Ethnicity
|
|
|
White
|
67 (21.7%)
|
|
African-American
|
113 (36.6%)
|
|
Hispanic
|
68 (22.0%)
|
|
Asian
|
4 (1.3%)
|
|
Other
|
1 (0.3%)
|
|
Unknown
|
56 (18.1%)
|
|
Microalbumin test
|
|
|
Yes
|
97 (31.4%)
|
|
No
|
212 (68.6%)
|
|
|
|
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Table 2.
Microalbumin Testing in Patients with Type-2 Diabetes (n=309)
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Variables
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Total
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Microalbumin
Testing n (%)
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Χ2 p-value
|
|
|
|
|
|
|
Clinic
|
|
|
< 0.0001
|
|
1
|
101
|
35 (34.7)
|
|
|
2
|
100
|
14 (14.0)
|
|
|
3
|
108
|
48 (44.4)
|
|
|
Gender
|
|
|
0.3
|
|
Male
|
109
|
38 (34.9)
|
|
|
Female
|
200
|
59 (29.5)
|
|
|
Age (yrs)
|
|
|
0.8
|
|
40 – 49
|
55
|
16 (29.1)
|
|
|
50 – 59
|
111
|
33 (29.7)
|
|
|
60 – 69
|
95
|
30 (31.6)
|
|
|
> 70
|
48
|
18 (37.5)
|
|
|
Race/Ethnicity
|
|
|
0.6
|
|
White
|
67
|
25 (37.3)
|
|
|
African-American
|
113
|
29 (25.7)
|
|
|
Hispanic
|
68
|
22 (32.4)
|
|
|
Asian
|
4
|
1 (25)
|
|
|
Other
|
1
|
0
|
|
|
Unknown
|
56
|
20 (35.7)
|
|
|
|
|
|
|
Discussion
- Comparing data between institutions can be problematic because of
differences in the definitions of laboratory observation terms and in the
units of measure for laboratory tests.
Some of the laboratory values we collected did not have complete
units of measure, making accurate interpretation of the results
impossible. Consequently, we could
not evaluate whether patients with a confirmed diagnosis of microalbuminuria
or proteinuria were prescribed appropriate medications.
- The chart review improved the rate of identified microalbumin
screening tests at one of the clinical sites that used an electronic
medical record. This outcome
underscores the challenges inherent in using computerized programs to
completely automate the documentation of clinical data, suggesting that
data mined from electronic medical records may need to be validated by
other methods such as chart reviews.
The collection of patient data with an electronic medical record
could be improved by the addition of more refined features that would
permit capture of complete laboratory test results.
- Using an electronic medical record does not necessarily improve
disease management and physician compliance with practice guidelines. Despite
numerous guidelines, physician compliance with recommendations for
nephropathy screening continues to be low (14% to 44%) among the primary
care clinics we studied. How the
incorporation of these practice guidelines into an electronic medical
record would affect that compliance warrants future study.
- Factors associated with the low microablumin screening rate may
include the limited availability of microalbumin screening at the
institutional level, a physician’s lack of knowledge of or belief in
clinical guidelines, or a physician’s thoughts about the practicality of
the clinical guideline. For
example, some physicians already treat type-2 diabetic patients with
antihypertensive medications that can also be used to treat proteinuria;
therefore, ordering a microalbumin screening test does not seem to have
clinical relevance to them.
·
The highest
microalbuminuria screening rate in our study comes from the clinic that uses a
physician-reminder system.
Conclusion
Having a
physician-reminder system may improve physician compliance with screening
recommendations for diabetic nephropathy in primary care clinics.
Acknowledgement:
This project was supported
in part by grants P20 HS11187 and R21 HS13524 from the Agency for Healthcare
Research and Quality and grant D12 HP00042 from the Bureau of Health
Professions of the Health Resources and Services Administration, which provided
infrastructure support for the Southern Primary-care Urban Research Network
(SPUR-Net).
The investigators
appreciate the support of the SPUR-Net Executive Committee and wish to
acknowledge the following organizations for their participation in this
study: Baylor Family Medicine, the
Harris County Hospital District Community Health Centers, and the Kelsey
Research Foundation. We also wish to
acknowledge the following for their contributions: Rachel Orr (the Kelsey Research Foundation) and Cynthia Kao,
Jennie Hong, Lisa DeMars, Joanne Wei, and Jana Davis (Baylor College of
Medicine) for data collection; Cai Wu and Carol Mansyur (Baylor College of
Medicine), Oscar Wehmanen (the Kelsey Research Foundation), and Chris Toronjo
(the Harris County Hospital District) for computer technical support; and
Pamela Paradis Tice, ELS(D), for editorial assistance.
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