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Accuracy and Precision of Four Value-Added Blood
Glucose Meters: the Abbott Optium, the DDI Prodigy,
the HDI True Track, and the HypoGuard Assure Pro
Catherine A. Sheffield, Pharm.D.,
1
Michael P. Kane, Pharm.D., F.C.C.P., B.C.P.S.,
2
Gary Bakst, M.D.,
3
Robert S. Busch, M.D., F.A.C.E.,
3
Jill M. Abelseth, M.D., F.A.C.E.,
3
and Robert A. Hamilton, Pharm.D.
2
Abstract
Purpose: This study compared the accuracy and precision of four value-added glucose meters.
Methods: Finger stick glucose measurements in diabetes patients were performed using the Abbott Diabetes
Care (Alameda, CA) Optium, Diagnostic Devices, Inc. ( Miami, FL) DDI Prodigy
, Home Diagnostics, Inc.
(Fort Lauderdale, FL) HDI True Track Smart System
, and Arkray, USA (Minneapolis, MN) HypoGuard
Assure
Pro. Finger glucose measurements were compared with laboratory reference results. Accuracy was
assessed by a Clarke error grid analysis (EGA), a Parke EGA, and within 5%, 10%, 15%, and 20% of the
laboratory value criteria (w
2
analysis). Meter precision was determined by calculating absolute mean differences
in glucose values between duplicate samples (Kruskal-Wallis test).
Results: Finger sticks were obtained from 125 diabetes patients, of which 90.4% were Caucasian, 51.2% were
female, 83.2% had type 2 diabetes, and average age of 59 years (SD 14 years). Mean venipuncture blood glucose
was 151 mg=dL (SD 65 mg=dL; range, 58–474mg=dL). Clinical accuracy by Clarke EGA was demonstrated in
94% of Optium, 82% of Prodigy, 61% of True Track, and 77% of the Assure Pro samples (P<0.05 for Optium and
True Track compared to all others). By Parke EGA, the True Track was significantly less accurate than the other
meters. Within 5% accuracy was achieved in 34%, 24%, 29%, and 13%, respectively (P<0.05 for Optium,
Prodigy, and Assure Pro compared to True Track). Within 10% accuracy was significantly greater for the
Optium, Prodigy, and Assure Pro compared to True Track. Significantly more Optium results demonstrated
within 15% and 20% accuracy compared to the other meter systems. The HDI True Track was significantly less
precise than the other meter systems.
Conclusions: The Abbott Optium was significantly more accurate than the other meter systems, whereas the
HDI True Track was significantly less accurate and less precise compared to the other meter systems.
Introduction
Self-monitoring of blood glucose (SMBG) is recog-
nized as an integral adjunctive tool for diabetes manage-
ment.
1
SMBG provides for real-time glucose measurements,
which better enable patients and clinicians to adjust diabetes
regimens and reach glycemic targets. Intensive treatment
of type 1 diabetes mellitus, with an emphasis on four-point
SMBG (e.g., preprandial and postprandial glucose monitor-
ing), was shown to significantly lower the risk of diabetes-
related microvascular complications by 50–76% in the
Diabetes Control and Complications Trial.
2
Several other
studies have also provided evidence that SMBG plays a
crucial role in glycemic control and subsequent glycosylated
hemoglobin (A1C) lowering.
3–5
A recent meta-analysis of
SMBG in type 2 diabetes concluded that its use is associated
with significant decreases in A1C for this population.
6
SMBG
complements the use of A1C testing and is endorsed by spe-
cialty organizations to optimize glycemic control.
7,8
Current
recommendations by the American Diabetes Association
(ADA) propose that SMBG be performed at least three times
daily for type 1 and gestational diabetes patients and as clin-
ically necessary for type 2 patients to reach glycemic goals.
7
A recent global consensus conference of diabetes experts
recently recommended that SMBG be used by all patients
with diabetes.
9
1
Department of Pharmacy Practice, Dayton Veterans Administration Medical Center, Dayton, Ohio.
2
Department of Pharmacy Practice, Albany College of Pharmacy and Health Sciences; and
3
The Endocrine Group, LLP, Albany, New York.
DIABETES TECHNOLOGY & THERAPEUTICS
Volume 11, Number 8, 2009
ªMary Ann Liebert, Inc.
DOI: 10.1089=dia.2008.0143
1
DIA-2008-0143-Kane_1P
Type: original-article
DIA-2008-0143-Kane_1P.3D 06/13/09 6:01pm Page 1
The cost of SMBG supplies is a significant barrier for some
patients. Value-added (generic or lower cost) meter blood
glucose testing systems are available to patients as a reduced-
cost alternative to popular name-brand meters and test strips.
Three commonly used value meter systems are the HDI True
Track Smart System
(Home Diagnostics, Inc., Fort Lauder-
dale, FL), the HypoGuard Assure
Pro (Arkray, USA, Min-
neapolis, MN), and the DDI Prodigy
Autocode (Diagnostic
Devices, Inc., Miami, FL). The Abbott Optium(Abbott
Diabetes Care, Alameda, CA) is a newer value meter blood
glucose testing system. To date no direct comparisons of these
meters have been published.
The primary purpose of this study was to compare the
accuracy and precision of finger stick blood glucose mea-
surements obtained with the Abbott Optium, the DDI Prod-
igy Autocode, the HDI True Track, and the HypoGuard
Assure Pro to one another and to a laboratory reference value
obtained via venous puncture. A secondary objective was to
evaluate the relationship of any variation between finger
glucose measurements (compared to venous results) with
regard to time of the patient’s previous meal or insulin ad-
ministration.
Research Design and Methods
Design and methods
Laboratory sheets of adult patients presenting to the phle-
botomy area of this ambulatory care endocrinology practice
were reviewed for identification of patients scheduled for
venipuncture for glucose measurement. After explanation of
the study’s purpose and testing procedure, patient demo-
graphic information (age, gender, and type of diabetes), in-
formation regarding patient use of insulin, and time elapsed
since the patient’s most recently ingested meal was obtained
from consenting adults. The study was approved by the Al-
bany College of Pharmacy (Albany, NY) Institutional Review
Board prior to its initiation, and written informed consent was
obtained prior to each patient’s participation. Pregnant
women and children (individuals under 18 years of age) were
excluded from the study. According to results of previous
brand-name glucose meter accuracy and precision studies,
attainment of zone A results (meter results within 20% of the
standard) using an error grid analysis (EGA) has varied from
67% to 97%.
10
Based upon these data and the fact that Inter-
national Organization for Standardization requirements
mandate a meter accuracy rate of 95% of meter results falling
within 20% of the standard measurement (e.g., venipuncture
results) for glucose concentrations 75 mg=dL,
11
a sample
size of 100 patients would be required to detect a statistical
difference of zone A results of 97% and 85%. A total of 125
diabetes patients were recruited for this study. A nominal
honorarium was offered to assist in patient recruitment.
Consenting adults underwent venipuncture as well as
finger glucose testing. All venipuncture samples were
centrifuged within 15 min of being drawn and were analyzed
for glucose within 2 h of collection. A control solution check
was performed on each meter prior to patient sampling each
day of testing, according to the manufacturers’ instructions.
Within 5 min following venipuncture, duplicate finger blood
glucose measurements were taken from each of four fingers
(in order: pinkie, ring, middle, and index fingers) ipsilateral to
the venipuncture site. Blood samples from the same lance
were used for each set of duplicate tests. Meter sequence was
randomly assigned, and the same investigator (C.A.S.) per-
formed all measurements, according to the manufacturers’
instructions. All finger blood samples were obtained using a
single-use capillary blood sampling device (Unistik
2,
Owen-Mumford, Oxford, UK). Test strips with identical lot
numbers (Abbott Optium, 99135-36 40894; DDI Prodigy,
TD06J223-C22(13); HDI True Track, RG1922; HypoGuard
Assure Pro, 04267B) were used for the study. Results of finger
glucose measurements were compared with the laboratory
reference value obtained via venipuncture (hexokinase=and
glucose-6-phosphate dehydrogenase method [Cobas Integra,
Roche Diagnostics, Indianapolis, IN]). Study meters and
supplies were donated by the manufacturer (Abbott Optium)
or were purchased at a local pharmacy (DDI Prodigy, HDI
True Track, and the HypoGuard Assure Pro).
Clinical and statistical analysis
Accuracy was assessed by comparing the glucose results of
the first finger stick obtained with each meter to the laboratory
reference value. The following methods were used for this
analysis: (1) a Clarke EGA of meter results compared with
venipuncture results, (2) a Parkes EGA of meter results com-
pared with venipuncture results, and (3) a comparison of the
percentage of results obtained with each meter falling within
5%, 10%, 15%, or 20% of the laboratory value (w
2
analysis).
10,12
Meter precision was determined by calculating the absolute
mean percentage differences in glucose values between the
first and second finger test results. Differences in precision
between the meters were assessed by comparing these abso-
lute differences using the Kruskal-Wallis test.
The potential effect of time since the patient’s most recent
meal on meter accuracy and the effect of time since the pa-
tient’s most recent use of insulin on meter accuracy were as-
sessed by comparing the average differences in blood sugar
results obtained between finger results with the laboratory
values at predetermined time intervals (analysis of variance).
The predetermined time intervals were: 0–2, 2.1–4, 4.1–6, and
>6 h since the previous meal.
Results
Blood samples were obtained from 125 patients during 4
days of testing. Patient demographic information is provided
in bT1
Table 1. The study population consisted primarily of el-
derly, Caucasian, type 2 diabetes patients. Mean venipunc-
ture blood glucose was 151 mg=dL (SD 65 mg=dL; range,
58–474 mg=dL). Insulin use was reported by 66 of the
Table 1. Patient Demographics
Characteristic
Number of patients 125
Age (years) 59 14
a
Gender (% female) 64 (51.2%)
Ethnicity (% white) 113 (90.4%)
Type 2 diabetes mellitus (%) 104 (83.2%)
Insulin use (%) 66 (52.8%)
a
Mean SD value.
2 SHEFFIELD ET AL.
DIA-2008-0143-Kane_1P.3D 06/13/09 6:01pm Page 2
125 patients (52.8%), including 52 patients (41.6%) who re-
ported using premeal insulin. Finger stick blood samples for
glucose testing were obtained in duplicate in 124 of the 125
patients. Despite three attempts for a second reading, error
messages occurred in one patient during testing with the HDI
True Track.
T2 cTable 2 lists the percentage of glucose results in
each of the glucose ranges according to the ISO 15197 proto-
col.
11
No test results were below 50 mg=dL glucose.
Table 3 lists
T3 cthe results of the Clarke EGA accuracy analysis.
Clinically acceptable results (zones A and B) were similar
among the four meters; however, clinical accuracy (zone A
results) was significantly greater with the Abbott Optium
(94%), compared to the DDI Prodigy (82%), HDI True Track
(61%), and the HypoGuard Assure Pro (77%) (P<0.05). Using
these criteria, the HDI True Track was significantly less ac-
curate than all of the other meters.
Table 4 lists the
T4 cresults of the Parkes EGA accuracy analy-
sis. Using these criteria, the Abbott Optium and DDI Prodigy
Autocode showed greater accuracy compared to the other
two meters, whereas the HDI True Track was significantly
less accurate than the other three meters.
Figure 1 illustrates
F1 cthe results of the within 20%, 15%, 10%,
and 5% accuracy analyses. Accuracy rates based on meter
values falling within 5% of the laboratory reference values
were 42% (Abbott Optium), 24% (DDI Prodigy Autocode),
29% (HypoGuard Assure Pro), and 13% (HDI TrueTrack),
respectively (P<0.05 for the HDI True Track compared to the
other meters). Accuracy rates based on meter values falling
within 10% of the laboratory reference values were 59%
(Abbott Optium), 54% (DDI Prodigy Autocode), 43% (Hy-
poGuard Assure Pro), and 30% (HDI TrueTrack), respectively
(P<0.05 for the HDI True Track compared to other meters
and for the HypoGuard Assure Pro compared to the Optium).
Significantly more results with the Abbott Optium met the
within 15% and 20% accuracy criteria compared to the other
meters. The HDI True Track was associated with significantly
less accuracy compared to the other meters.
The HDI True Track was significantly less precise than the
other meters. There were no significant differences in preci-
sion among the remaining three meters ( bT5
Table 5). Glucose
results varied 9–15 mg=dL (approximately 6–10%), on aver-
age, between the first and second finger test results.
An analysis of the effect of time since the patient’s most
recent meal (analysis of variance) found no appreciable effect
on meter accuracy. There was no greater variability of finger
glucose results compared to venipuncture glucose results
when results were compared according to the predetermined
time intervals of 0–2 (n¼20), 2.1–4 (n¼55), 4.1–6 (n¼22), and
>6h (n¼28) since the previous meal. In addition, there was
no greater variability of finger glucose results compared to
venipuncture glucose results in patients using premeal insulin
(absolute difference ¼19.9 mg=dL, n¼52) versus those not
using premeal insulin (18.1 mg=dL, n¼73).
Discussion
The beneficial impact of tight glycemic control on micro- and
macrovascular complications of diabetes is well established
based on the results of the Diabetes Control and Complications
Trial,
2
the Epidemiology of Diabetes Interventions and Com-
plications Trial,
13
and the United Kingdom Prospective Dia-
betes Study.
14
In concordance with these data, current practice
guidelines from the ADA
7
and American Association of Clin-
ical Endocrinologists (AACE)
8
recommend glycosylated he-
moglobin goals of <7% and <6.5%, respectively.
Despite data supporting intensive therapy and the avail-
ability of agents with multiple glucose-lowering mechanisms,
overall glycemic control in the United States is poor. According
to 1999–2002 National Health and Nutrition Examination Sur-
vey data,
15
only 49.8% of adult Americans with diabetes mel-
litus achieved the ADA A1C goal of <7%. The 2003–2004 data
indicate that only one-thirdof patients with type 2 diabetes met
the AACE’s more stringent A1C goal of <6.5%.
16
SMBG is an integral part of diabetes management, en-
dorsed by the ADA, American Association of Clinical En-
docrinologists, and a consensus global panel of diabetes
experts.
1,7,8,9
It allows a patient to appreciate the effects of
adjustments in diet, exercise, and medication therapy on
blood glucose levels and helps them to make treatment de-
cisions. SMBG may help patients gain better overall glycemic
control, ultimately helping to reduce the risk of complications.
Daily four-point SMBG has been associated with a significant
50–76% reduction in microvascular complications when used
in conjunction with intensive insulin therapy in patients with
type 1 diabetes.
2
Table 2. ISO 15197 Protocol Glucose Standards
Glucose level
(mg=dL)
ISO standard
(%)
Study
results (%)
<50 5 0
51–80 15 4
81–120 20 32
121–200 30 47.2
201–300 15 12.8
301–400 10 3.2
>400 5 0.8
Table 3. Meter Accuracy: Clarke EGA
Zone
Meter A B C D E
Abbott Optium 118
a
70 0 0
DDI Prodigy Autocode 103 19 0 3 0
HDI True Track 76
a
44 3 2 0
HypoGuard Assure Pro 96 28 1 0 0
a
P<0.05 versus all others (w
2
analysis).
Table 4. Meter Accuracy: Parke EGA
Zone
Meter A B C D E
Abbott Optium 119 6 0 0 0
DDI Prodigy Autocode 111 14 0 0 0
HDI True Track 94
ab
30 1 0 0
HypoGuard Assure Pro 106
a
19 0 0 0
a
P<0.05 versus Optium (w
2
analysis).
b
P<0.05 versus Prodigy (w
2
analysis).
FOUR VALUE-ADDED GLUCOSE METERS 3
DIA-2008-0143-Kane_1P.3D 06/13/09 6:01pm Page 3
The utility of SMBG for patients using insulin is widely
accepted; however, its role in the management of type 2 dia-
betes patients treated with oral medication is more contro-
versial. Recent data indicate that SMBG can help lower A1C in
this population in a cost-effective manner. Care plans for type
2 diabetes patients that include SMBG are associated with
greater A1C reductions than plans that do not.
3–5
A recent
meta-analysis concluded that the use of SMBG by patients
with type 2 diabetes is associated with a significant A1C re-
duction of 0.39%.
6
A computer modeling study based on in-
formation from a large managed care database indicated that
SMBG is cost-effective for type 2 diabetes patients on oral
medications. Although SMBG was associated with an in-
crease in direct costs, these were offset by reductions in
complications and complication-associated costs, along with a
slight increase in quality-adjusted life years.
17
Multiple barriers to SMBG exist, however. A survey by
Karter et al.
18
indicated that nonadherence with SMBG may
be independently predicted by several factors, including
longer time since diagnosis, less intensive therapy, male sex,
increased age, smoking, excessive alcohol consumption, and
higher out-of-pocket test strip costs. Vincze et al.
19
reported
that a diagnosis of type 2 diabetes (as opposed to type 1) and
environmental barriers, such as lifestyle interference, incon-
venience, painfulness, and cost, impact SMBG adherence.
Generic or ‘‘value added’’ blood glucose strips are a potential
solution to the financial burden of testing, but data evaluating
the degree of accuracy or precision of value added products
are not available.
Patients using SMBG data to make self-care decisions work
under the assumption that meter results accurately reflect
their capillary blood glucose level. Several methods are cur-
rently used to assess meter accuracy. Two error grids, the
Clarke Error Grid
10
and the Parkes Error Grid,
12
have been
developed to take into account the differences between ref-
erence and meter glucose values as well as the clinical sig-
nificance of a treatment decision based on the SMBG result.
These error grids were developed using expert opinion. The
International Organization for Standardization criteria assess
whether or not a glucose meter is capable of producing 95% of
its results within 5, 10, and 15 mg=dL of reference glucose
values <75 mg=dL and within 5, 10, 15, and 20% of the ref-
erence value if the glucose value is 75 mg=dL.
11
Food and
Drug Administration blood glucose meter approval is based
on the International Organization for Standardization criteria,
mandating that manufacturers demonstrate that 95% of meter
readings fall within 20% of actual blood glucose values.
10,20
This accepted standard still allows for substantial variation in
glucose readings, a variation that is arguably too great in light
of modern intense diabetes management regimens as hypo-
glycemia could result in the overcorrection of an otherwise
normal glucose value.
According to the Clarke EGA in this study, the Abbott
Optium was the most accurate of the meters studied, with
94% (P<0.05) of results falling in zone A (representing less
than a 20% deviation from the true blood glucose), whereas
the HDI True Track was the least accurate, with only 61% of
results falling in zone A. There were no significant differences
in clinically acceptable results, defined as the summation of
zone A and zone B (the deviation from true blood glucose is
>20% but leads to no treatment or benign treatment) results,
among the meters.
FIG. 1. Meter accuracy: glucose measurements falling within 5%, 10%, 15%, or 20% of venipuncture results. *P<0.05 versus
all others (w
2
analysis);
{
P<0.05 versus Abbott Optium (w
2
analysis). Color images available online at www.liebertonline
.com=dia.
Table 5. Precision Analysis
Mean SD precision
Relative
(%)
Absolute
(mg=dL)
95% CI
(mg=dL)
Abbott Optium 6 5910 8–11
DDI Prodigy Autocode 8 61110 10–13
HDI True Track 10 13
a
15 18
b
12–19
HypoGuard Assure Pro 7 71116 8–14
a
P<0.05, Kruskal-Wallis test, compared to Abbott Optium and
HypoGuard Assure Pro.
b
P<0.05, Kruskal-Wallis test, compared to all.
4 SHEFFIELD ET AL.
DIA-2008-0143-Kane_1P.3D 06/13/09 6:01pm Page 4
The Parkes Error Grid analysis was developed as the result
of a consensus of 100 endocrine clinicians in the attempt to
construct an unbiased tool to analyze the clinical significance
of SMBG measurement errors. Zone A results are defined as
results causing no effect on clinical action, whereas zone B
results are defined as results resulting in altered clinical action
or having little or no effect on clinical outcome. Using the
Parkes Error Grid analysis, the Abbot Optium and DDI
Prodigy Autocode were found to be significantly more clini-
cally accurate (zone A results) than the HDI True Track. The
Abbott Optium was also more accurate than the HypoGuard
Assure Pro (P<0.05). There were no significant differences
among the meters regarding clinically acceptable glucose re-
sults (summation of zone A and B results).
Using the stricter International Organization for Standar-
dization accuracy criteria, the Abbott Optium continued to
perform significantly better than the other meters, with 85% of
its readings falling within 15% of the reference values. The
Abbott Optium and DDI Prodigy Autocode had significantly
more results falling within 10% of the reference values than
the HypoGuard Assure Pro and the HDI True Track, and the
Abbott Optium, DDI Prodigy Autocode, and the DDI Prodigy
Autocode had significantly more results falling within 5% of
the reference values than the HDI True Track (Fig. 1). The HDI
True Track was significantly less accurate compared to all
other meters using the more strict accuracy criteria. The HDI
True Track was also significantly less precise compared to the
other meters.
Because there are currently no data to confirm whether
greater meter accuracy (i.e., more glucose readings falling
within 5%, 10%, 15%, or 20% of the reference values) is as-
sociated with better patient outcomes, prospective studies to
evaluate this topic are needed. Until such data are available,
however, it seems reasonable to surmise that better meter
accuracy could potentially lead to better treatment decisions
by patients, especially if it means patients feel more secure
and confident in their self-care decisions because of their
perception of greater meter accuracy.
Meter accuracy, while extremely important, is not the sole
criterion for meter selection. Factors such as blood sample
size, software capabilities, ease of use, pain associated with
testing, size of the numerical display, frequency of error
messages requiring a retest, and insurance coverage=out-of-
pocket cost also impact this decision. These variables were not
evaluated for the meters in this study and should be taken into
account by the clinician when deciding on a glucose meter for
an individual patient.
Another ‘‘real world’’ clinical consideration with all four
meters in this study is the fact that control solution is not
readily provided with purchase of the meter (unlike most
brand-name meters) but must be ordered separately from the
manufacturer or ordered and purchased at a pharmacy. Thus,
unless patients make the extra effort to obtain the control
solution, they will not be able to properly conduct control tests
on their meters.
Our study had several potential limitations. For each value-
added testing system, only one meter and one test strip lot
was used. This provided standardization to the study, but it
may have introduced a potential source of error. While most
of the study supplies were purchased at a local pharmacy, the
fact that the Abbott supplies for this study were donated by
the manufacturer is a potential source of bias. All finger stick
testing was done by one of the authors (C.A.S.), according to
manufacturers’ instructions. This increased the internal va-
lidity of the study, but it also potentially limits its external
validity as the results may not necessarily be applicable to real
world practice, because some patients do not use control so-
lution or follow all of the manufacturer’s instructions for
SMBG when testing.
A limited number of patients had glucose results in the
extremes of hypoglycemic and hyperglemic ranges according
to ISO 15197 protocol criteria. An assessment of meter accu-
racy and precision in these extremes of glucose ranges were
therefore based on a limited number of results.
Two potential criticisms of this study include the fact that
glucose testing was performed on different blood samples
(venipuncture and four different fingersticks) and on different
types of blood (venous and capillary), differences that re-
portedly may be exacerbated in the nonfasting state.
21
How-
ever, we believe that our methods can be justified on the basis
that (1) although patients test capillary whole blood, most
professionals assess meter accuracy using a laboratory refer-
ence of venous blood glucose values and (2) an analysis of the
effect of time since the patient’s most recent meal and use of
premeal insulin found no additional appreciable effect on
meter accuracy (e.g., no additional differences between ve-
nous and capillary glucose levels were found in these sub-
groups).
Finally, no popular brand-name meters were included in the
present study; therefore the value-added results cannot be di-
rectly compared to results obtained in studies using the popular
brand-name meter systems. Although a direct comparison
across studies is not possible, it is interesting to note that the
accuracy of the best of the value-added meters appear to have
performed at a level comparable to that of the best performing
popular name-brand meters.
22,23
Prospective studies to directly
compare the accuracy and precision of popular brand-name
and value-added meter systems are needed.
Conclusions
The Abbott Optium was significantly more accurate than the
other studied meters, whereas the HDI True Track was signif-
icantly less accurate. The HDI True Track was significantly less
precise compared to the other meters. Further studies are nee-
ded to compare the relative accuracy of the most accurate val-
ue-added meters with that of brand-name meters.
Acknowledgments
This study was supported by an investigator-initiated re-
search grant from Abbott Diabetes Care.
Author Disclosure Statement
No competing financial interests exist.
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Address correspondence to:
Michael P. Kane, Pharm.D., F.C.C.P., B.C.P.S.
Department of Pharmacy Practice
Albany College of Pharmacy and Health Sciences
106 New Scotland Avenue
Albany, NY 12208
E-mail: michael.kane@acphs.edu
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