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1Running head: CONTINUOUS GLUCOSE MONITORING, BETTER CONTROL
Continuous Glucose Monitoring:
Diabetes Patients Gain Better Control
Nina Jo Hibbard
Auburn University/ Auburn Montgomery
2Running head: CONTINUOUS GLUCOSE MONITORING, BETTER CONTROL
Abstract
Background
The long term effect of patients with Type 1 and Type 2 diabetes are decreased when using a
continuous glucose monitor (CGM) as compared to checking blood sugars two to four times
daily. The Evidence Based Practice (EBP) evaluation in this paper is to investigate the outcomes
as it relates to changes in the hemoglobin A1C (A1C) and GlycoMark (GM) lab tests in the short
term and in complications in the long term.
Method
The method used for this project will be a retrospective study of 20-50 type 1 or type 2 diabetes
patients who have worn a CGM for seven days. A1C and GM lab values will be evaluated prior
to and post wearing the device. Known evidence will be taken to make this determination. The
ACE Star Model will be the approach to evaluating the basis for possible evidence change. The
potential project possibilities regarding implementation would be to encourage more patients to
wear GGM devices for better glucose control. The evaluation methods would include a small
test of change with current patients who have worn the device temporarily.
Results
The result of the study show 56 patients had improved GM and A1C lab values after wearing the
CGM.
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Conclusion
The outcome as it related to changes in the A1C and GM lab tests in the short term were proven
to be beneficial to the patient as evidence by lab value improvements. Long term outcomes need
further evaluation.
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Continuous Glucose Monitoring: Diabetes Patients Gain Better Control
Diabetes has been identified as one of the top 20 priority areas for national action
according to the Institute of Medicine (IOM, 2003). The prevalence of diabetes has risen more
than ever in 2010. According to the American Diabetes Association (ADA), new statistics reveal
that there are a total of 25.8 million children and adults in the United States which is 8.3% of the
population that have diabetes. There are 18.8 million diagnosed and 7.0 million undiagnosed
people with diabetes. What is even more profound is that 79 million people have prediabetes.
There have been 1.9 million new cases of diabetes which are diagnosed in people aged 20 years
and older. In contrast to the 2007 National Diabetes Fact Sheet, which used fasting glucose data
to estimate undiagnosed diabetes and prediabetes, the 2011 National Diabetes Fact Sheet uses
both fasting glucose and A1C levels to derive estimates for undiagnosed diabetes and
prediabetes. These tests were chosen because they are most frequently used in clinical practice.
Complications
Diabetes complications are also wide range and consist of multiple areas of concern. Heart
disease and stroke are 4 times more likely with a diagnosis of diabetes. In 2004, heart disease
was noted on 68% of diabetes-related death certificates among people aged 65 years or older. In
2004, stroke was noted on 16% of diabetes-related death certificates among people aged 65 years
or older. Adults with diabetes have heart disease death rates about 2 to 4 times higher than
adults without diabetes. In addition, the risk for stroke is 2 to 4 times higher among people with
diabetes. Another complication is high blood pressure. In 2005-2008, of adults aged 20 years or
older with self-reported diabetes, 67% had blood pressure greater than or equal to 140/90 mmHg
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or used prescription medications for hypertension. Blindness is a large concern as well for
individuals. Diabetes is the leading cause of new cases of blindness among adults aged 20–74
years. Diabetes is the leading cause of kidney failure, accounting for 44% of new cases in 2008.
In 2008, 48,374 people with diabetes began treatment for end-stage kidney disease in the United
States. In 2008, a total of 202,290 people with end-stage kidney disease due to diabetes were
living on chronic dialysis or with a kidney transplant in the United States. Neuropathy is another
complication patients face when dealing with diabetes. About 60% to 70% of people with
diabetes have mild to severe forms of nervous system damage. Further, more than 60% of
nontraumatic lower-limb amputations occur in people with diabetes. In 2006, about 65,700
nontraumatic lower-limb amputations were performed in people with diabetes.
Cost of Diabetes
The total cost of diagnosed diabetes in the United States in 2007 was $174 billion dollars.
$116 billion was for direct medical costs and$58 billion for indirect costs such as disability, work
loss and premature mortality. After adjusting for population age and sex differences, average
medical expenditures among people with diagnosed diabetes were 2.3 times higher than what
expenditures would be in the absence of diabetes. Factoring in the additional costs of
undiagnosed diabetes, prediabetes, and gestational diabetes brings the total cost of diabetes in the
United States in 2007 to $218 billion (American Diabetes Association, 2011).
Control of the Disease
Diabetes can be controlled in numerous ways. Type 1 diabetes is controlled by insulin
100% of the time. Type 2 diabetes patients can be controlled in a variety of ways. Some
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patients can monitor their diet and exercise, while others sometimes need to add an oral
medication for added control. If these regimens fail, insulin can be added, from one injection per
day for basal control up to intensive multiple injections including basal bolus therapy. Another
alternative is Continuous Insulin Infusion (CSII). This mechanism of control is used for Type 1
and Type 2 diabetes patients. There is another vital part of controlling diabetes which is by
checking blood sugars several times per day. A patient with diabetes must maintain optimal
blood sugar control by testing several times a day. This is referred to as self-monitoring blood
glucose (SMBG). Good times to check blood sugars are fasting, before meals, two hours after
meals, bedtime and 2-3am. Other times necessary are before driving, before exercise or during a
sickness. A recent survey of Medicare patients shared they check two to four times a day. This
is due partly to inconvenience of remembering all the different times. It is also due to guidelines
regarding the number of testing strips per month allowed by insurance providers (Medicare
Guidelines, 2011).
The latest improvement in diabetes control is continuous glucose monitoring (CGM). The
Diabetes population this author works with on a daily basis varies in glucose control thus
resulting in a wide range of A1C and GM lab results in the short term which leads to long term
complications. A1C is a lab test that allows clinicians to be able to see what the patient’s
average blood glucose is over the past 3 month period of time (see Table 1). The glycomark test
allows doctors to evaluate what the patient’s average blood sugar has been over the last two
weeks postprandial (see Table 2). A1C values between 6.5 and 8.5% can be misleading. Up to
half of the patients in this range may not be in “near normal glycemic control” and may still be at
significant risk of complications of diabetes. GlycoMark® is uniquely positioned to differentiate
these patients from those who actually are in good control.
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The Diabetes Control and Complications Trial (DCCT) and The United Kingdom
Prospective Diabetes Study (UKPDS) both show that lowering A1C from 8.5 to below 6.5%
significantly reduces the risk of complications from diabetes. The UKPDS showed that each 1%
reduction in A1C was associated with a 14% reduction in risk of MIs. Risk reductions were also
noted in the DCCT with cardiovascular (41%), neuropathy (60%), retinopathy (63%),
nephropathy (54%), decreasing when A1C drops from 9 to 7.2%. Unfortunately, less than half of
diabetic patients were able to achieve the targeted goal of an A1C less than 7.0% and fewer than
30% of those who reached this goal were able to maintain it over time (Dungan, 2008). 1, 5-
anhydroglucitol (GlycoMark) as a marker of short-term glycemic control and glycemic
excursions. Most patients have been successful in lowering A1C to below 8.5% by diet and
therapy; however, achieving levels below 6.5% is quite challenging. The GM can provide a clear
picture of actual glycemic control not provided by either A1C or fructosamine thereby allowing
you to effectively manage your patient through modifications of diet and/or therapy.
GlycoMark is unique among diabetes diagnostic markers. GM uniquely reflects all
postprandial hyperglycemia above the renal threshold (>180 mg/dl. serum glucose) over the prior
one to two weeks. Most hyperglycemia occurring at A1C less than 8.5% is postprandial glucose.
A1C and fructosamine (FA) average mean fasting glucose over 2 – 3 months and 2 – 3 weeks,
respectively these can produce misleading information regarding actual glycemic control.
GlycoMark is the trade name for a FDA approved blood test for 1, 5-anhydroglucitol (1, 5-AG),
a close analog of glucose. The normal serum 1, 5-AG range in adults and children is 10 to 30
µg/ml. The diagnostic range is from <2 to 10 µg/ml. Pharmaceutical companies use GlycoMark
to monitor the effects of postprandial anti-hyperglycemic therapy.
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Several clinical studies are underway to assess GlycoMark as a screening test for diabetes
and as a marker for cardio-vascular disease. Major diagnostic laboratories provide GlycoMark
has a premier test for 2008.GlycoMark is a straightforward and stable assay and is adaptable to
any automated open chemistry analyzer (Dungan et al., 2006).
In using a CGM that checks blood sugars 200-300 times daily, a patient becomes more
aware and much more likely to take action on abnormal blood sugars than if the patient randomly
checks two to four times per day. This technique allows the patient to insert a sensor once every
3 to 7 days and monitors blood sugars every 3 to 5 minutes (Dexcom, 2010). This monitoring
calculates to approximately 200 to 300 times per day. Patients can achieve much better control
with tighter blood glucose monitoring. Short and long term complications can be minimized,
prolonged and even avoided with detailed blood sugar control. While the benefits are enormous,
the risk of hypoglycemia is very high with efforts for tight control which can lead to critical issue
including the possibility of death. A balance must be found in order for a positive outcome.
In research, findings were remarkable for decreasing the A1C result as well as increased
compliance for daily care of the disease. Patients report the inconvenience of wearing the device
sometimes plays a factor as well as the expense of the sensors. Overall, patients are willing to
use the device due to decrease in anxiety regarding hypoglycemic episodes during the night.
Spouses and parents also report the satisfaction of the patient wearing the device to alleviate
worry about the spouse or child during the night (Body, Beck, & Xing, 2009).
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PICO Question
After establishing the need for better control of diabetes, the following PICO question
was developed.
In patients with Type 1 and Type 2 diabetes, how does using a continuous glucose
monitor compared to self-monitoring glucose affect patients of their diabetes control as evidence
by improving A1C and GM lab values and improving daily glucose control in the short term as
well as lowering the risk of chronic complications in the long term?
Each component is further explained in the following:
P – In patients with Type 1 and Type 2 diabetes
This population is in great need for trying to prevent and delay short and long term complications
from the chronic disease.
I – using a continuous glucose monitor
This device is FDA approved to guide patients in the interstitial monitoring of glucose checking
200-300 times daily and show results on the transmitter so the patient can be aware of fluctuating
blood sugars and possibly take action.
C – Compared to standard self-monitoring glucose
This is a method currently used by diabetes patients that checks blood sugar via finger stick
method and on average is done from two to four times daily.
O – Affect patients to achieve better control of their diabetes by reducing A1C and GM lab
values in the short term as well as lowering the risk of complications in the long term?
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By making patients continuously aware of glucose control they are more likely to treat abnormal
blood sugars more often thus resulting in improved A1C and GM lab values in the short term as
well as daily improvement of blood sugars. This ultimately reduces long term complications.
Purpose of the Project
The purpose for choosing this project is to investigate the outcomes as it relates to better
glucose control. Is the use of a CGM beneficial in the short term and long term by improving
daily glucoses, improving A1C and GM results and preventing or further delaying long term
complications? The purpose would also include improved overall compliance as it relates to the
patients daily DM care. The epidemic of diabetes is growing stronger every day and evidence
suggests more complications are happening due to poor control. It is critical to maintain optimal
control for this reason.
Goal of the Project
The goal for the project is to improve diabetes control in patients. The downloaded
patient data has demonstrated to be crucial in the plan of care at office visits (See Figure 1).
Without this information provided to the doctor, diabetes regimens to provide improved control
at the times of day needed would be near impossible to predict. Evidence provided is to move
this suggestion into evidence-based practice.
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Figure 1
Target Population
The target population will be adult Type 1 and Type 2 diabetes patients who have been
diagnosed for at least 6 months. The age range is 19 years old and above and used a temporary
CGM in the months of January 2011 through December 2011.
Framework
The EBP framework that will guide the development and implementation of this project
is the ACE Star model (See Figure 2). The Ace Star Model is a model for understanding the
cycles, nature, and characteristics of knowledge that are utilized in various aspects of evidence-
based practice (EBP).
Demonstration Patient Report
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Figure 2
The Star Model organizes both
old and new concepts of improving care into a whole and provides a framework with which to
organize EBP processes and approaches (Stevens 2004). Known as the ACE Star Model, it is a
simple, parsimonious depiction of the relationships between various stages of knowledge
transformation, as newly discovered knowledge is moved into practice. Configured as a simple
5-point star, the model illustrates five major stages of knowledge transformation: 1) knowledge
discovery, 2) evidence summary, 3) translation into practice recommendations, 4) integration
into practice, and 5) evaluation. Evidence-based processes and methods vary from one point on
the Star Model to the next. Underlying premises of knowledge transformation include:
Knowledge transformation is necessary before research results are useable in clinical
decision making.
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Knowledge derives from a variety of sources. In healthcare, sources of knowledge
include research evidence, experience, authority, trial and error, and theoretical
principles.
The most stable and generalizable knowledge is discovered through systematic processes
that control bias, namely, the research process.
Evidence can be classified into a hierarchy of strength of evidence. Relative strength of
evidence is largely dependent on the rigor of the scientific design that produced the
evidence. The value of rigor is that it strengthens cause-and-effect relationships.
Knowledge exists in a variety of forms. As research evidence is converted through
systematic steps, knowledge from other sources (expertise, patient preference) is added,
creating yet another form of knowledge.
The form ('package') in which knowledge exists can be referenced to its use; in the case
of EBP, the ultimate use is application in healthcare.
The form of knowledge determines its usability in clinical decision making. For example,
research results from a primary investigation are less useful to decision making than an
evidence-based clinical practice guideline.
Knowledge is transformed through the following processes: summarization into a single
statement about the state of the science, translation of the state of the science into clinical
recommendations, with addition of clinical expertise, guides from theory, and client preferences
integration of recommendations through organizational and individual actions evaluation of
impact of actions on targeted outcomes.
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Each Stage Explanation
STAR POINT 1. Discovery
This is a knowledge generating stage. In this stage, new knowledge is discovered through
the traditional research methodologies and scientific inquiry. Research results are generated
through the conduct of a single study. This may be called a primary research study and research
designs range from descriptive to correlational to causal; and from randomized control trials to
qualitative. This stage builds the corpus of research about clinical actions Stevens, 2004). During
the knowledge generating stage of this project, the writer gained a better understanding of what
literature review and research had taken place on the particular topic of CGM devices.
STAR POINT 2. Evidence Summary
Evidence summary is the first unique step in EBP - the task is to synthesize the corpus of
research knowledge into a single, meaningful statement of the state of the science. The most
advanced EBP methods to date are those used to develop evidence summaries (i.e., evidence
synthesis, systematic reviews, e.g., the systematic review methods outlined in the Cochrane
Handbook) from randomized control trials. Some evidence summaries employ more rigorous
methods than others, yielding more credible and reproducible results. This stage is also
considered a knowledge generating stage, which occurs simultaneously with the summarization.
Evidence summaries produce new knowledge by combining findings from all studies to identify
bias and limit chance effects in the conclusions. The systematic methodology also increases
reliability and reproducibility of results. The following terms are used to refer to various forms of
evidence summaries: evidence synthesis (Agency for Healthcare Research and Quality),
systematic review (Cochrane Collaboration), meta-analysis (a statistical procedure), integrative
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review, review of literature, and state of the science review (less rigorous and therefore less
reliable summary processes). This field of science is referred to as the 'science of research
synthesis'. The rigorous evidence summary step distinguishes EBP from the old paradigm of
research utilization. Largely due to the work of the Cochrane Collaboration, rigorous methods
for systematic reviews have been greatly advanced, using Meta analytic techniques and
developing other statistical summary strategies, such as Number Needed to Treat (NNT). The
evidence summary for this project reveals extensive research has been performed regarding the
use of CGM devices (Melnyk, 2011).
STAR POINT 3. Translation
The transformation of evidence summaries into actual practice requires two stages:
translation of evidence into practice recommendations and integration into practice. The aim of
translation is to provide a useful and relevant package of summarized evidence to clinicians and
clients in a form that suits the time, cost, and care standard. Recommendations are generically
termed clinical practice guidelines (CPGs) and may be represented or embedded in care
standards, clinical pathways, protocols, and algorithms. CPGs are tools to support informed
clinical decisions for clinician, organization, and client. Well-developed CPGs state benefits,
harms, and costs of various decision options. The strongest CPGs are developed systematically
using a process that is explicit and reproducible. Summarized research evidence is interpreted
and combined with other sources of knowledge (such as clinical expertise and theoretical guides)
and then contextualized to the specific client population and setting. Evidence-based CPGs
explicitly articulate the link between the clinical recommendation and the strength of supporting
evidence and/or strength of recommendation (Melnyk, 2011). The translation for this project
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would be placed in recommendation format for the Endocrinologist office staff to review and
then a plan would be developed to phase in the change of practice.
STAR POINT 4. Integration
Integration is perhaps the most familiar stage in healthcare because of society's long-
standing expectation that healthcare be based on most current knowledge, thus, requiring
implementation of innovations. This step involves changing both individual and organizational
practices through formal and informal channels. Major aspects addressed in this stage are factors
that affect individual and organizational rate of adoption of innovation and factors that affect
integration of the change into sustainable systems (Melnyk, 2011). The formal and informal
channels of change through the Endocrinologist office would begin with the physician and office
manager and integrate to the office staff and patients.
STAR POINT 5. Evaluation
The final stage in knowledge transformation is evaluation. In EBP, a broad array of
endpoints and outcomes are evaluated. These include evaluation of the impact of EBP on patient
health outcomes, provider and patient satisfaction, efficacy, efficiency, economic analysis, and
health status impact. As new knowledge is transformed through the five stages, the final outcome
is evidence-based quality improvement of health care (Melnyk, 2011). The new process would
be continuously evaluated on a monthly basis at the Endocrinologist office to evaluate change
effectiveness and modify changes as needed in the new standard of practice implemented.
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Rationale
The rationale for using this model is because of the inclusive of familiar processes and
also emphasizes the unique aspects of EBP. The ACE Star Model places nursing's previous
scientific work within the context of EBP, serves as an organizer for examining and applying
EBP, and mainstreams nursing into the formal network of EBP. The Star Model depicts various
forms of knowledge in a relative sequence, as research evidence is moved through several cycles,
combined with other knowledge and integrated into practice. The ACE Star Model provides a
framework for systematically putting evidence-based practice processes into operation (Stevens,
2004).
Review of Literature
The literature researched and reviewed for this project are multifaceted and broad in
selection. The review of research is important to be able support an idea based on evidence.
Randomized Control Trials (RCT) were the majority of the evaluation of evidence followed by a
systemic review.
Body, Beck and Xing (2009) had information with the purpose of sustained benefit in use
of CGM on A1C, glucose profiles, and hypoglycemia in adults with Type 1 diabetes. The
objective was to evaluate long-term effects of continuous glucose monitoring (CGM) in
intensively treated adults with type 1 diabetes. The research and design methods used were a
study of 83 of 86 individuals 25 years of age with type 1 diabetes who used CGM as part of a 6-
month randomized clinical trial in a subsequent 6-month extension study. The results found were
after 12 months, median CGM use was 6.8 days per week. Mean change in A1C level from
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baseline to 12 months was 0.4 0.6% (P 0.001) in subjects with baseline A1C 7.0%. A1C
remained stable at 6.4% in those with baseline A1C 7.0%. The incidence rate of severe
hypoglycemia was 21.8 and 7.1 events per 100 person-years in the first and last 6 months,
respectively. Time per day with glucose levels in the range of 71–180 mg/dl increased
significantly (P =0.02) from baseline to 12 months. The conclusion in intensively treated adults
with type 1 diabetes, CGM use and benefit can be sustained for 12 months. In a 6-month
randomized trial of intensively treated individuals with type 1 diabetes and baseline A1C7.0%,
adults 25 years of age benefited from use of continuous glucose monitoring (CGM) compared
with adults using conventional blood glucose monitoring (1). In a contemporaneous parallel
study of individuals with type 1 diabetes that had A1C levels7.0%, those in the CGM group had
a reduction in biochemical hypoglycemia compared with those in the control group while
maintaining A1C levels in the target range. This report describes the 12-month follow-up of
adult subjects in the two randomized trials’ CGM groups. The research and design method was
described by grafts included in the article. The protocol has been analyzed in a 12-month
follow-up data for 83 of the 86 adults (25 years of age) who were initially randomized to the
CGM group in either the 7.0% (n 49) or 7.0% n 34) baseline A1C cohorts; 2 subjects
discontinued study participation during the first 6 months and one after completion of the 9-
month visit. An insulin pump was used by 75 (90%) subjects and multiple daily injections
(MDIs) of insulin by 8 (10%). Subjects were provided with either a DexCom SEVEN (DexCom,
San Diego, CA), MiniMed Paradigm Real-Time System (Medtronic MiniMed, Northridge, CA),
or Freestyle Navigator (Abbott Diabetes Care, Alameda, CA). Follow-up visits during the
extension study occurred at 9 and 12 months post randomization. The reduction in A1C occurred
mainly in the first 8 weeks and then remained relatively stable through the next 44 weeks.
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Subjects with baseline A1C 7.0%, A1C remained within the target range over the entire 12
months of the study (6.4, 6.3, and 6.4% at baseline, 6months, and 12 months, respectively (Body,
Beck, & Xing, 2009).
The discussion of CGM continues with the review of an article by Ehrhardt, Chellappa,
Walker, Fonda, & Vigersky, (2011). Real-time continuous glucose monitoring (RT-CGM)
improves hemoglobin A1c (A1C) and hypoglycemia in people with type 1 diabetes mellitus and
those with type 2 diabetes mellitus (T2DM) on prandial insulin; however, it has not been tested
in people with T2DM not taking prandial insulin. They evaluated the utility of RT-CGM in
people with T2DM on a variety of treatment modalities except prandial insulin. The method used
in this particular study had a prospective 52-week, two-arm, randomized trial comparing RT-
CGM (n = 50) versus self-monitoring of blood glucose (SMBG) (n = 50) in people with T2DM
not taking prandial insulin. Real-time continuous glucose monitoring was used for four 2-week
cycles (2 weeks on/1 week off). All patients were managed by their usual provider. This article
reports on changes in A1C 0–12 weeks. The results found include a mean (±standard deviation)
decline in A1C at 12 weeks was 1.0% (±1.1%) in the RT-CGM group and 0.5% (±0.8%) in the
SMBG group (p = .006). There were no group differences in the net change in number or dosage
of hypoglycemic medications. Those who used the RT-CGM for =48 days (per protocol) reduced
their A1C by 1.2% (±1.1%) versus 0.6% (±1.1%) in those who used it <48 days (p = .003).
Multiple regression analyses statistically adjusting for baseline A1C, an indicator for usage, and
known confounders confirmed the observed differences between treatment groups were robust (p
= .009). There was no improvement in weight or blood pressure. In conclusion of this article the
Real-time continuous glucose monitoring significantly improves A1C compared with SMBG in
patients with T2DM not taking prandial insulin. This technology might benefit a wider
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population of people with diabetes than previously thought (Ehrhardt, Chellappa, Walker, Fonda,
& Vigersky, 2011).
The purpose of the STAR 3 Study was to evaluate improvements in metabolic control in
subjects with type 1 diabetes placed on sensor-augmented insulin pump therapy (SAP). These
subjects had previously failed to meet glycemic targets with multiple daily injections (MDI)
therapy and conventional blood glucose monitoring. This was an unmasked, randomized,
controlled trial conducted at 30 diabetes centers in the United States and Canada. Subject
eligibility criteria: Use of MDI for 3 months, documented self-monitoring of blood glucose
(SMBG) 4 times/day for the prior 30 days, 7-70 years of age, type 1 diabetes, and a baseline
A1C of =7.4% to =9.5%. Subjects were required to have access to a computer. Subjects were
randomized to SAP or MDI via block design stratified by site and age group: Adult group: 19-70
years of age Pediatric group: 7-18 years of age. Prior to randomization, all study subjects
received training in insulin diabetes management, carbohydrate counting and correction insulin
bolusing. Training for MDI and SAP subjects included use of diabetes management software
(CareLink® Therapy Management System for Diabetes-Clinical). The SAP subjects were placed
on the MiniMedParadigm®REAL-Time System (Medtronic) with insulin aspart for 2 weeks.
The MDI subjects used both insulin glargine and insulin aspart. All additional scheduled visits
following week 5 were the same in both groups. Sensor glucose values were collected for 1 week
periods at baseline, 6 months and 1 year in both groups. The MDI group used blinded continuous
glucose monitoring to collect sensor data. Subjects were seen at 3, 6, 9, and 12 months for
routine clinic visits. The results include 495 patients that were randomized; 10 lacked any
follow-up A1C values and were not included in the data analysis. Analyses were performed
using the intent-to-treat cohort comprised of these 485 subjects. There were no significant
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differences in baseline characteristics between the two study groups except for weight among
adults. The difference in A1C between study groups favored the SAP group and was statistically
and clinically significant in both adult and pediatric subjects. The researchers concluded the
decrease in A1C levels in the SAP group was achieved at 3 months and sustained throughout the
1 year study. The improvement in A1C levels was achieved without an increase in the rate of
severe hypoglycemic events and without an increase in the time spent at an AUC <70 mg/dL. A
significantly greater number of adults and pediatric subjects in the SAP group reached ADA age
specific A1C targets (Bergenstal, Tamborlane, & Ahmann, 2010).
Bode, Beck, & Xing (2008), performed a study to evaluate the efficacy and safety of
continuous glucose monitoring (CGM) in adults and children with type 1 diabetes. Data
regarding CGM in both groups after the 26-week visit were used to estimate the amount of time
per day the subject’s glucose level was hypoglycemic (<70 mg/dL or <50 mg/dL),
hyperglycemic (>180 mg/dL or >250 mg/dL), and in the target range (71 to 180 mg/dL). The
study was a randomized, controlled trial that was conducted between February and December
2007 in 10 centers. Subject eligibility criteria included: age =8 years, type 1 diabetes =1 year,
use of either an insulin pump or =3 daily insulin injections, a baseline HbA1c value of 7.0%-
10.0%, and the completion of a blinded CGM run-in phase. Subjects were randomly assigned to
either the CGM group or the control group. Subjects assigned to the CGM group were provided
with one of the following devices: the DexCom™ SEVEN®, the Minimed Paradigm® REAL-
Time Insulin Pump and CGM System, or the Abbott FreeStyle Navigator™. Subjects were
instructed to use the device daily and to verify the device glucose measurement with a home
blood glucose meter before making treatment decisions. Control group subjects were asked to
perform self-monitoring of blood glucose (SMBG) at least 4 times per day. Subjects in the CGM
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and the control groups were given written instruction on how to use CGM and SMBG data,
respectively, how to make insulin dose adjustments, and how to use computer software to review
data retrospectively to alter insulin dosing. Although it was not required, all subjects had a home
computer. Clinic visits were conducted at 1, 4, 8, 13, 19, and 26 weeks with one scheduled phone
contact between each visit. The control group used a blinded CGM device for one week
following the 13- and 26-week visits. A1C levels were measured at baseline, 13 weeks, and 26
weeks. Age groups were defined as 8 to 14 years of age, 15 to 24 years of age, and >25 years of
age. A total of 322 children and adults with type 1 diabetes were randomized in the study; 165
subjects were assigned to the CGM group and 157 were assigned to the control group. The
majority of the participants were using an insulin pump, measuring glucose levels >5 times per
day, and had mean A1C levels of <8.0%. Only three subjects dropped out of the study. There
was a significant between-group difference in the change in A1C levels from baseline to 26
weeks in subjects who were =25 years old, favoring the CGM group (mean difference: -0.53%;
95% confidence interval: -0.71% to -0.35%; p<0.001). CGM can lead to lower A1C levels and
tighter glycemic control without a significant increase in hypoglycemia for adults with type 1
diabetes. They observed significant difference in A1C levels in the >25 age group may be related
to substantially greater use of sensors in this group versus in the two younger age groups.
Improved glycemic results were driven by subjects >25 years old who were motivated to both
use the technology and had the capability to incorporate it into their daily diabetes management.
Improvements by the youngest age group were probably due to the parental involvement of their
diabetes management (Bode, Beck, & Xing, 2008).
Hirsch, Abelseth, Bode, Fischer, & Kaufman,( 2008) evaluated the clinical effectiveness
and safety of an insulin pump augmented with real-time continuous glucose monitoring (CGM)
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compared with an insulin pump plus self-monitoring of blood glucose. The study was a
randomized, treat-to-target, 6-month trial that was conducted at 7 centers in the U.S. Enrolled
subjects were between the ages of 12-72 years, had an A1C level of =7.5%, were diagnosed with
type 1 diabetes >1 year prior to study enrollment, and were previously treated with CSII for at
least 6 months. Following initial screening, all subjects wore blinded CGM for 10 days to obtain
baseline data. Subjects were randomized to either the Sensor Group (SG), which used a sensor-
augmented pump, or the Control Group (CG), which used self-monitoring of blood glucose
(SMBG). Other than the communication between the pump and the sensor in the SG, all other
pump functionality was identical. All subjects received intensive diabetes management training.
At week 13, A1C values were obtained and insulin pump data were downloaded. At the end of
the study (week 26), the CG wore two blinded CGM sensors for two consecutive 3-day periods.
Results included a total of 146 adults and adolescents with type 1 diabetes were enrolled and
randomized in the study; 72 subjects were assigned to the SG and 74 subjects were assigned to
the CG. Of the 138 subjects completing the study, 40 were adolescents 12 to 17 years of age and
98 were adults aged 18 years or older. The change in A1C levels from baseline was significant
in both groups (p<0.001). The between-group difference was not significant (p=0.3706). At
week 13, both groups showed a decrease in A1C values, while at the end of the study, A1C
values increased, although not to baseline values. Twenty (30.8%) SG subjects achieved A1C
values of 7.0% by week 13 compared with 8 (11.1%) CG subjects. The between-group
difference was significant (p=0.007). When compared with CG subjects, the number of SG
subjects who reached a 7.0% A1c level at either 13 weeks or at the end of study was greater to a
significant degree (p=0.0031). The between-group difference in adolescents and in adults was
not significant. At the end of the study, 16 subjects (24.2%) in the SG reached the target A1C
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levels of <7.0% versus 12 (19.4%) in the CG on, A1C values were collected and insulin pump
data were downloaded for all subjects (Hirsch, Abelseth, Bode, Fischer, & Kaufman, 2008).
An investigated study has not been done to show the benefit of continuous glucose
monitoring (CGM) at insulin pump initiation in patients with poor metabolic control on multiple
daily injections (MDI). In RealTrend, continuous subcutaneous insulin infusion (CSII) therapy
was randomly initiated with either Paradigm® REAL-Time (PRT) or conventional CSII in
poorly controlled subjects who had been on optimized basal-bolus MDI regimens. Study
highlights included that A1C levels were significantly reduced within the PRT group and the
CSII group. The greatest A1C reduction occurred in the PRT group when using sensors at least
70% of the time in study; -0.96 ± 0.93% vs. -0.55 ± 0.93% (p<0.001). PRT subjects bolused
more frequently after one month of treatment and at study end. A higher percentage of insulin
was delivered as bolus in the PRT group vs. CSII group. Patient Benefits were that there were
twice as many study subjects in the PRT group reported that they made modifications to their
eating habits and lifestyle vs. study subjects using CSII alone. Over 90% of subjects in the PRT
group used CGM data to manage their diabetes by adjusting insulin doses, and 59% used CGM
data to manage glycemic excursions. Alarms and glucose trend information available to the PRT
subjects may have been responsible for more lifestyle modifications and insulin treatment
adjustments (Raccah, Sulmont, & Reznik, 2009).
Dungan et al, 2006 introduced a study regarding the GlycoMark test and how it relates to
CGM devices. Postprandial hyperglycemia is often inadequately assessed in diabetes
management. Serum 1, 5-anhydroglucitol (1, 5-AG) drops as serum glucose rises above the renal
threshold for glucose and has been proposed as a marker for postprandial hyperglycemia. The
25Running head: CONTINUOUS GLUCOSE MONITORING, BETTER CONTROL
objective of this study is to demonstrate the relationship between 1, 5-AG and postprandial
hyperglycemia, as assessed by the continuous glucose monitoring system (CGMS) in sub
optimally controlled patients with diabetes. Patients with type 1 or type 2 diabetes and an A1C
between 6.5 and 8% with stable glycemic control were recruited from two sites. A CGMS
monitor was worn for two consecutive 72-h periods. Mean glucose, mean post meal maximum
glucose (MPMG), and area under the curve for glucose above 180 mg/dl (AUC-180), were
compared with 1, 5-AG, fructosamine (FA), and A1C at baseline, day 4, and day 7. 1, 5-AG
varied considerably between patients (6.5 ± 3.2 µg/ml [means ± SD]) despite similar A1C (7.3 ±
0.5%). Mean 1, 5-AG (r = -0.45, P = 0.006) correlated with AUC-180 more robustly than A1C (r
= 0.33, P = 0.057) or FA (r = 0.38, P = 0.88). MPMG correlated more strongly with 1, 5-AG (r =
-0.54, P = 0.004) than with A1C (r = 0.40, P =0.03) or FA (r = 0.32, P = 0.07). The findings
were 1; 5-AG reflects glycemic excursions, often in the postprandial state, more robustly than
A1C or FA. 1, 5-AG may be useful as a complementary marker to A1C to assess glycemic
control in moderately controlled patients with diabetes.
Synthesis of literature
After reviewing the literature and selecting the articles that most applied to the PICO
questions, the following details were noted:
Population consisted of Type 1 DM- all articles and Type 2 DM had 1 article including adults
and children. There were 3 articles from the literature that were specific to the PICO question
which is looking at the benefit of the CGM on A1C versus SMBG control. One article
specifically focused on Type 2 DM who was not taking prandial insulin. Two articles focused
on effects of poor controlled patients being put on the insulin pump for the first time along with a
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CGM. The total participants reviewed equal 1,260 (adults- 841, adolescents-419). All articles
were consistent in their findings that the outcome of the articles concluded improvement in A1C
with the use of a CGM versus a SMBG device. The length ranged from 3articles that were 6
month studies and 3 were 12 month studies. Bias includes 2 studies that no bias appeared to be
present and 4 studies Medtronic was a sponsor of the research article.
It is overwhelming evidence that the use of CGM devices does in fact lower A1C %
points when used diligently up to 70% of the time. Patients are more conscious of the food
selection, remember to bolus for the carbohydrates more often and are more aware of the effects
food, stress and lack of blousing has on the blood sugar. Once a patient is aware of these short
term changes, it will only benefit the long term affects for decreasing risks of complications.
Critical Appraisal of Evidence
The appraisal and evaluation of the literature was found to prove validation for
application to the PICO question. The proposed question was supported in that the use of CGM
in patients with diabetes would result in decrease of A1C. The certainty of knowledge sources
need to continue to be researched because four of the articles received money for research from
Medtronic which indicates bias may exist in the research. More unbiased research needs to be
found in order to make the findings as valid as possible.
Rating the Strength of the scientific evidence: The article by Body, Beck & Xing, (2009)
is ranked at a level 2 which is discussing sustained benefit of continuous glucose monitoring on
A1C, glucose profiles, and hypoglycemia in adults with type 1 diabetes. This is a strong article
recommendation and good evidence that the service improves important health outcomes and
concludes that benefits substantially outweigh harms. After reviewing the article by Ehrhardt,
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Chellappa, Walker, Fonda & Vigeisky (2011) is was found to be a level 2 uses the terms “might
be” beneficial but is biased and vaguer in the study. Findings are significant in A1C reductions
but the population is narrow using only Type 2 DM on non-prandial insulin. The Star Study by
Bergenstal, Tamborance, & Ahmann, (2010) proved to be a level 2 and is also a Randomized
Control Trial (RCT) and has many age groups as well as the largest population in literature
reviewed. It has bias though and could definitely be more beneficial without this factor. Next,
Body, Beck & Xing, (2008), scored a level 2 also had a large population from a RCT study and
divided out the study to be two armed so that there was a control group to compare CGM versus
SMBG while the article by Hirsch, Abelseth, Bode, Fischer & Kaufman (2008) also ranked a
level 2 and had a downfall for this article because of the shorter length of time of 6 months as
opposed to 12 months in some of the other articles. It also has bias noted. Lastly, Raccah,
Sulmaont & Reznik, (2009) had a level 2 article also had a 6 month study time as opposed to a
longer time frame. In researching future articles, it will be beneficial to select at least a 12 month
period of study. In all the articles, it is overwhelming the evidence found in the benefit Please
refer to Appendix A for greater detail on the articles used as evidence.
Recommendations
Based on the evidence presented in the literature review, it is recommended to implement
an evidence based evaluation need to just recommend use –Grade of recommendation- A. The
method of implementation will be discussed later. The lab findings of current A1C and GM
result findings can be documented as well as future results.
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Current State in Organization
The long term effect of patients with Type 1 and Type 2 Diabetes are decreased when
using a continuous glucose monitor (CGM) as compared to checking blood sugars two to four
times daily. The Evidence Based Practice (EBP) evaluation in this project is to investigate the
outcomes as it relates to changes in A1C and GM in the short term and in preventing
complications in the long term. The current state of care delivery process in the organization is
providing temporary CGM devices to patients whose insurance does not cover the device and
allowing them to wear it for 7 days. After the 7 days is complete, the patient returns the device.
It is downloaded and a report is developed for evaluation of the patient’s current control state in
regards to the blood sugar control. Adjustments to current medication regimens are made to
insure proper care of diabetes is being obtained. Also, those patients who have purchased
devices are set up on a schedule to download their devices on a three month rotation so it can be
evaluated and adjustments made prior to their next visit. Opportunities for improvement would
be to assist patients to appeal insurance decisions to not pay for these devices so that it is
available for more patients to use and be paid for through insurance. If this procedure did not
take place in a medical office or facility, patients go 6 months to a year not knowing if their
current diabetes medication regimen is working correctly and benefiting the A1C and GM results
in the short term. This leads to delayed control of DM in the long term resulting in unwanted
complications.
The stakeholders are already on board with the use of CGM devices in the offices for
temporary use as well as encouraging every patient on insulin to apply for the device through
their insurance. The stakeholders are very proactive in the approach to patient education and
preventative care.
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Implementation Plans
The EBP Environment Model has a core listing of key words: persistence, patience and
perseverance. These are constant reminders of what one must do when beginning change in any
environment. First, a vision for EBP must be created. Next, one must engage personnel and other
resources to be involved in the change. Integration of EBP and nourishing the culture then takes
place in the model. Finally, one must evaluate the evidence (Melnyk, 2011).
A practice change intervention for the target population of temporary CGM wearers for
2011 was not applicable in the retrospective study however, evidence and needs assessment
support the idea moving forward to continue promoting CGM device application in the
temporary condition provided by the physician’s office to encourage health promotion and
controlled blood sugars.
The project for implementation included several processes. The first step was to obtain
the list of 2011 temporary CGM patients. After this list was obtained, the next step was to
eliminate any patient who was under the age of 19. After establishing a list, patient charts were
obtained and lab results of A1C and GM prior to wearing the temporary CGM and post CGM
were documented in a controlled collection process. Patients were identified with a patient
number, male or female, type 1 or type 2 and age. The initial plans for implementation was to
perform a retrospective study on Type 1 and Type 2 patients ages 19 and older who wore a
temporary CGM for 7 days from January 2011 to December 2011. The plan was to acquire
approximately 20-50 patients from an Endocrinologist office that met these criteria.
Retrospective review each chart for Pre- CGM A1C and GM lab data and compare it to post
CGM lab data was to be performed The factors that made this a successful implementation was
30Running head: CONTINUOUS GLUCOSE MONITORING, BETTER CONTROL
good documentation of the visits for the year described. A barrier could have been poor
documentation and no way to track these patients or know when they wore the CGM device.
The resources used for this project was the office staff in obtaining charts and lab results, a
personal computer to store the data and excel spread sheet and a flash drive to save data in order
to send it to Auburn University SPSS system for analysis.
The small test of change included a final total of 56 patients. The original timeline was to
start collecting data in October 2011. The study originally was to evaluate patients who had
purchased their own CGM and interview to see what life style changes the patient had made as
well as review labs to see if there was an improvement in blood sugar control. The study
changed in December 2011 to the temporary CGM in a 7 day period because it was new data and
not much research has been done on temporary CGM effects to A1C and GM. Data was
collected late January 2012 and early February 2012. After completion of the data it was sent to
SPSS for analysis.
The vision for change has been established in the office this implementation project is to
take place. The physician is the mentor for my EBP project and is very much on board with
100% patient participation in his practice. Budget needs are not an issue due to the physician’s
office already having the temporary devices.
Evaluation Plan
The role of outcomes when evaluating practice change is vitally important. In 1988, Paul
Ellwood proposed a framework for outcomes management (OM). It was designed to help
patients, payers, and providers make rational medical care- related choices based on better
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insight. In 1997, the Health Outcomes Institute’s Outcomes Management Model was developed
to take the OM framework and put it into actual steps. In the model there are four phases
discussed. The outcome process is identified in phase one (Melnyk, 2011).
1. Measurement of A1C results prior to wearing temporary CGM and post wearing the device for
7 days.
2. Measurement of the GM results prior to wearing temporary CGM and post wearing the device
for 7 days.
The results and outcomes achieved in the small test of change were positive and did show
significant different when wearing the CGM temporarily.
Table 3
Type 1 and Type 2 patients
Paired Samples Statistics
Mean N Std. Deviation Std. Error Mean
Pair 1 GM1 6.3176 51 5.42633 .75984
GM2 7.2216 51 5.71182 .79982
Pair 2 A1C 8.7511 45 2.44230 .36408
A1C2 8.1000 45 1.67698 .24999
Paired Samples Test
32Running head: CONTINUOUS GLUCOSE MONITORING, BETTER CONTROL
Paired Samples Statistics
Mean N Std. Deviation Std. Error Mean
Pair 1 GM1 6.3176 51 5.42633 .75984
GM2 7.2216 51 5.71182 .79982
Pair 2 A1C 8.7511 45 2.44230 .36408
t df Sig. (2-tailed)
Pair 1 GM1 - GM2 -2.777 50 .008 significant
Pair 2 A1C - A1C2 2.984 44 .005 significant
Overall, the GM and A1C improved in after the 7 day use. (See Graph 1). When breaking the
data down to Type 1 and Type 2 results separately the following were noted.
Table 4
Type 1 Data
Paired Samples Statistics
Mean N Std. Deviation Std. Error Mean
Pair 1 GM1 3.7143 21 2.44710 .53400
GM2 4.7667 21 3.70841 .80924
Pair 2 A1C 8.5765 17 2.17788 .52821
A1C2 7.9471 17 1.81422 .44001
Paired Samples Test
t df Sig. (2-tailed)
Pair 1 GM1 - GM2 -1.468 20 .158
33Running head: CONTINUOUS GLUCOSE MONITORING, BETTER CONTROL
Paired Samples Statistics
Mean N Std. Deviation Std. Error Mean
Pair 1 GM1 3.7143 21 2.44710 .53400
GM2 4.7667 21 3.70841 .80924
Pair 2 A1C 8.5765 17 2.17788 .52821
Pair 2 A1C - A1C2 2.554 16 .021
Glycomark was not significant but A1C was significant.
Looking at Type 2 patients, this population had the most significant change in labs. (See graph 2)
Table 5
T-Test – Type 2
Paired Samples Statistics
Mean N Std. Deviation Std. Error Mean
Pair 1 GM1 8.1400 30 6.18823 1.12981
GM2 8.9400 30 6.27269 1.14523
Pair 2 A1C 8.8571 28 2.62275 .49565
A1C2 8.1929 28 1.61519 .30524
Paired Samples Test
34Running head: CONTINUOUS GLUCOSE MONITORING, BETTER CONTROL
Paired Samples Statistics
Mean N Std. Deviation Std. Error Mean
Pair 1 GM1 8.1400 30 6.18823 1.12981
GM2 8.9400 30 6.27269 1.14523
Pair 2 A1C 8.8571 28 2.62275 .49565
t df Sig. (2-tailed)
Pair 1 GM1 - GM2 -3.230 29 .003 Significant
Pair 2 A1C - A1C2 2.071 27 .048 Significant
The Type 2 change revealed the most significant change (See Graph 3).
Findings/Discussion
The summary of findings in the small test of change was significant. If one evaluates the
data in Tables 3-5 and Graphs 1-3, evidence shows that the short term use of CGM device made
significant differences in the patients’ labs pre- and post CGM. This could be related to instant
feedback from the clinician and medicine regimen changes to more align with blood glucose
control as well as the patient possibly becoming more aware of how stress, sickness, food and
lack of medication effects blood sugar control.
Recommendations for future research and practice change
• Further evaluate use of CGM in relation to development of long-term complications –A
long term study of 2-5 years of continuous use of the CGM with pre- evaluation and post
35Running head: CONTINUOUS GLUCOSE MONITORING, BETTER CONTROL
evaluation of micro-vessel and macro -vessel disease, kidney function, nerve conduction
studies, gastric emptying studies, and lipid studies to determine if complications have
decreased as a result of better glucose control with the CGM.
• Evaluate results as to type of medications patient was on pre and post CGM (oral versus
insulin)- This would be interesting to see if Beta cell function returns with better glucose
control long term resulting in decreasing medications, possibly eliminating insulin in
Type 2 patients and less insulin needs in Type 1 patients.
• Evaluate lifestyle changes including dietary changes made as a result of wearing CGM-
Long term evaluation of food diary, weight management and exercise regimen changes or
beginning exercise programs as a result of seeing on the CGM how these changes in
lifestyle positively affect blood glucose control.
Conclusions
In concluding this project, the idea of evidence based practice has a new concept for this
author. Knowledge capacity has increased and a new respect for the process of studying effects
of change has tremendous respect. The key learning experiences learned in this process include
the preparation that takes place before a study can ever begin. The relevance for evidence base
practice when becoming a nurse practitioner is strong. Implementation is vital in the new
capacity this author will hold in the near future. In practicing, the goal is to stay involved in
evidence based research and possibly follow through this project in a larger capacity.
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References
American Diabetes Association, (2011). Website; www.americandiabetesassociation.org.
Bergenstal, R.M., Tamborlane, W.V. & Ahmann, A. (2010). Effectiveness of sensor-augmented
insulin pump therapy in type 1 diabetes. New England Journal of Medicine, doi:
10.1056/NEJoal1002853
Bode, B., Beck, W.R., & Xing, D. (2008). Continuous glucose monitoring and intensive
treatment of type 1 diabetes. The New England Journal of Medicine, 359, 1464-1476.
Body, B., Beck, R.W., & Xing, D. (2009). Sustained benefit of continuous glucose monitoring
on A1C, glucose profiles, and hypoglycemia in adults with type 1 diabetes. Diabetes
Care, 32(11), 2047-2049.
Dungan, K. M., Buse, J. B., Largay, J., Kelly, M. M., Button, E. A., Kato, S., & Whittlin, S.
(2006). 1, 5-anhydroglucitol and postprandial hyperglycemia as measured by continuous
glucose monitoring system in moderately controlled patients with diabetes. Diabetes
care, 29(6), 1214-1219.
Dungan, K. M. (2008). 1, 5-anhydroglucitol (GlycoMark) as a marker of short-term glycemic
control and glycemic excursions. Expert review of molecular diagnostics, 8(1), 9-19.
37Running head: CONTINUOUS GLUCOSE MONITORING, BETTER CONTROL
Ehrhardt, M.D., Chellappa, M., Walker, M.S., Fonda, S.J. & Vigersky, R.A. (2011). The effects
of real-time continuous glucose monitoring on glycemic control in patients with type 2
diabetes mellitus. Journal of Diabetes Science and Technology, 5(3), 668-675.
Hirsch, I.B, Abelseth, J., Bode, B.W., Fischer, J.S., & Kaufman, F.R. (2008). Sensor-augmented
insulin pump therapy: results of the first randomized treat-to-target study. Diabetes
Technology & Therapeutics, 10, 377-383.
Mcgill, J. B., Cole, T. G., Nowatzke, W., Houghton, S., Ammirati, E., Gautille, T., & Sarno, M.
(2004). Circulating 1, 5-anhydroglucitol levels in adult patients with diabetes reflect
longitudinal changes of glycemia: a U.S. trial of the glycomark assay. Diabetes care,
27(8), 1859-1865.
Manly, B.M., & Fineout-Overholt, E. (2011). Evidence-based practice in nursing and
healthcare. Philadelphia, Pennsylvania: Lippincott, Williams & Wilkins.
Raccah, D., Sulmont, V., & Reznik, Y. (2009). Incremental value of continuous glucose
monitoring when starting pump therapy in patients with poorly controlled type 1 diabetes:
the real-trend study. Diabetes Care, 32, 2245-2250.
Sousa, V.D., Hartman, S.W., Miller, E.H. & Carroll, M.A. (2009). New measures of diabetes
self-care agency, diabetes self-efficacy and diabetes self-management for treating
individuals with T2DM. Journal of Clinical Nursing.pp. 1305-1314.
Stevens, K. R. (2004). ACE Star Model of EBP: Knowledge Transformation. Academic Center
for Evidence-based Practice. The University of Texas Health Science Center at San
Antonio. www.acestar.uthscsa.edu
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Wojner, A. W. (2001). Outcomes management: Application to clinical practice. St. Louis, MO:
Mosby.
Table 1
Hemoglobin A1C chart and reference numbers
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Table 2
GlycoMark Table reference
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Graph 1
Type 1 and Type 2 Results illustration
41Running head: CONTINUOUS GLUCOSE MONITORING, BETTER CONTROL
Pre CGM Post CGM0
1
2
3
4
5
6
7
8
9
10 56 Total patients
A1CGM
Graph 2
Type 1 Results Chart
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A1C GM0123456789
108.6
3.7
7.9
4.8
TYPE 1 RESULTS22 patients
PRE CGM POST CGM
Graph 3
Type 2 Results
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A1C GM7.27.47.67.8
88.28.48.68.8
99.2
8.9
8.2
7.9
9
TYPE 2 RESULTS29 patients
PRE CGM POST CGM
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Appendix A
Article citation in APA format
Purpose of study/research questions
Design type and methods (sampling method/sample size, description of interventions (if any), and outcomes measured
Major findings/findings relevant to project
Critique of validity, bias and significance
Body, B., Beck, R.W., & Xing, D. (2009). Sustained benefit of continuous glucose monitoring on a1c, glucose profiles, and hypoglycemia in adults with type 1 diabetes. Diabetes Care, 32(11), 2047-2049.
Level of Evidence= ll
The purpose of this study was to evaluate the long-term effects of the continuous glucose monitoring in the intensively treated type 1 diabetes patients.
Design: Randomized clinical trial witha 12-month follow-upMethod: 83 of 86 adults in the age of ≥ 25yrs Randomized to the continuous glucose monitoring system (CGMSSampling method-Convenience Outcomes- CGMS use
The median CGMS use was 7.0 days per week. Among subjects with baseline A1C ≥ 7.0% mean change in A1C from baseline to 12 months was -0.4 ± 0.6% (P< 0.001). The reduction in A1C occurred mainly in the first 8 weeks and then remained relatively stable though the next 44 weeks. Hypoglycemic events fell from 21.8% per 100 person-years during the first 6 months
Weaknesses: Some patients were on insulin pumps and some patients were on multiple daily injections. The different methods of regimen do lead to different outcomes. There were a low number of patient entries which decreases the validity of the significance for my project. The study was only conducted at one institution and decreases the wide variety of sampling in different subjects.
Strengths: Subjects had a sustained benefit of improved glucose
45Running head: CONTINUOUS GLUCOSE MONITORING, BETTER CONTROL
control noted by A1C levels and the amount of time sensor glucose values were in the target range. Low rate of severe hypoglycemic events during the extension phase of the study. A1C of 6.8% was a mean compared to the Diabetes Control and Complications Trial (DCCT) mean A1C of 7.1%. This is the guideline that most primary physicians use to tag a subject “controlled”. This study is very significant to my PICO question.
Significance to my project: This research article made me aware of the different types of continuous glucose monitors. It only studied adults who were randomly assigned to groups and is randomized so the information will help
46Running head: CONTINUOUS GLUCOSE MONITORING, BETTER CONTROL
me in my project. It also helped me identify important outcomes for CGM.
Ehrhardt, M.D., Chellappa, M., Walker, M.S., Fonda, S.J. & Vigersky, RA., (2011). The effects of real-time continuous glucose monitoring on glycemic control in patients with type 2 diabetes mellitus. Journal of Diabetes Science and Technology, 5(3), 668-675.
Level of Evidence= ll
The purpose of this study was to test type 2 diabetes with Real-time continuous glucose monitoring (RT-CGM) on a variety of treatment modalities except prandial insulin.
Design: 52 week, prospective, two-arm, randomized controlled studyMethod: intervention
group compared to control group who used only self-monitoring of blood glucose (SMBG).Outcomes:A1c change at 12 weeks; change in number and dosage of hypoglycemic medications; difference in A1c
The mean (±standard deviation) decline in the A1C was 1.0% (±1.1%) in the RT-CGM group and 0.5% (±0.8%) in the SMBG group (p =0.006). There were no group differences in the net change in number or dosage of hypoglycemic medications. Those who used the RT-CGM for ≥48 days (per protocol) reduced the A1C by 1.2% (±1.1%) versus 0.6% (±1.1%) in those who used it <48 days (p= 0.003). There was no improvement in weight or blood pressure.
Weaknesses: There were a low number of patient entries which decreases the validity of this study. It would not provide adequate significant impact to my study. Only one location was used in this study.
Strengths: Study proved it is possible that those using a variety of therapies and all age groups may benefit from this type of an intervention
Significance for project: My project includes the T2DM population, a focus of this study. I have learned that CGM use has support for being effective in T2 no matter the tx.
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Bergenstal, R.M., Tamborlane, W.V. & Ahmann, A. (2010). Effectiveness of sensor-augmented insulin pump therapy in type 1 diabetes. New England Journal of Medicine, doi: 10.1056/NEJoal1002853
Level of Evidence= ll
The purpose of this study is to evaluate improvements in metabolic control in subjects with type 1 diabetes placed on sensor-augmented insulin pump therapy.
Design: Unmasked randomized, controlled trialMethod: Conducted at 30 diabetes centers in the United States and Canada.Sampling Method: Subjects used of MDI for 3 months, self-monitoring of blood glucose (SMBG) 4Xd for the previous 30 days
485 patients were analyzed. The difference in A1C between study groups favored pump patients on sensors as compared to MDI patients on sensors. The difference in A1C in the SAP group fell rapidly from baseline to 3 months and remained lower than levels in the MDI group for the rest of the study. An increased frequency of sensor use was associated with a greater reduction in A1C values from baseline to 1 year.
Weaknesses: There was no set determining amount of designated time to wear the sensor. It was not outlined in the study.
Strengths: Large number of subjects obtained for this study. Improvement in A1C levels was achieved without an increase in hypoglycemic events. A large number of adults and pediatric subjects in the SAP group reached ADA age specific A1C targets.
Significance to my project:I will definitely use the information obtained from this project mainly because of a large subject population in the study.
48Running head: CONTINUOUS GLUCOSE MONITORING, BETTER CONTROL
Bode, B., Beck, W.R., & Xing, D., (2008). Continuous glucose monitoring and intensive treatment of type 1 diabetes. The New England Journal of Medicine, 359, 1464-1476.
Level of Evidence= ll
The Purpose of this study was to evaluate the efficacy and safety of continuous glucose monitoring (CGM) in adults and children with type 1 diabetes.
Design: Randomized control trialMethod:Eligibility criteria included: age ≥8 years, type 1 diabetes ≥ 1 year, use of either an insulin pump or ≥ 3 daily insulin injectionsSampling Method: Randomly assigned to either the CGM group or the control group.Clinic visits were conducted at 1, 4, 8, 13, 19, and 26 weeksOutcomes: change in glycated hemoglobin (A1C) from baseline to 1 year between the two study groups consisting of SAP and MDI
A mean A1C of <8.0%. There was a significant between-group difference in the change in A1C levels from baseline to 26 weeks in the subjects who were ≥ 25 years old, in favor of the CGM group (mean difference: -0.53%; 95% confidence interval: - 0.71% to -0.35%. CGM can lead to lower A1C levels and tighter control without a significant increase in hypoglycemia for adults with type 1 diabetes. Improvements by the youngest age group were due to more parental involvement.
Weaknesses: All subjects were not required to wear a sensor for the same amount of time. Only includes T1DM subjects which limit the study. T1DM population is only 10% of the diabetes population. The length of the study is not very long, therefore hard to obtain a true outcome.
Strengths: I like the wide age range included in the study. It allows me to see a true evaluation of the subjects studied. Provided knowledge that CGM does lower A1C without the increase in hypoglycemic events. A large population was studied and it was random. This allows me to trust the outcomes of this study for my project.
49Running head: CONTINUOUS GLUCOSE MONITORING, BETTER CONTROL
Significance for my project: I will use the data from this study because I like the age range that is used as well as the large population studied in this article.
50Running head: CONTINUOUS GLUCOSE MONITORING, BETTER CONTROL
Hirsch, I.B, Abelseth, J., Bode, B.W., Fischer, J.S., & Kaufman, F.R., (2008). Sensor-augmented insulin pump therapy: results of the first randomized treat-to-target study. Diabetes Technology &Therapeutics, 10, 377-383.
Level of Evidence= ll
The purpose of the study was to evaluate the clinical effectiveness and safety of an insulin pump augmented with a real-time continuous glucose monitoring (CGM) compared with an insulin pump plus self-monitoring of blood glucose (SMBG).
Design: Randomized, treat-to-target, 6-month trial Subjects were between the ages of 12-72 years old.Method: Blinded CGM for 10 days to get a baseline data. Subjects were randomized to either the sensor group; which used a sensor-augmented pump, or the control group, which used SMBG.values wereSampling Method: End of the study the control group wore two blinded CGM sensors for two consecutive 3-day periods.Outcomes: AIC changes at 13, 26 weeks
A total of 146 adults and adolescents with type 1 diabetes were enrolled and randomized in the study; 72 subjects were assigned to the sensor group and 74 were assigned to the control group. Of the 138 subjects completing the study, 40 were adolescents 12 to 17 years of age and 98 were adults aged 18 years and older. A1C changes were significant in both groups. Twenty (30.8%) sensor subjects achieved A1C values of 7.0% by week 13 compared with 8 (11.1%) of the control subjects. At the end of the study, 16 subjects (24.2%) in the sensor group reached the target A1C of < 7.0% versus 12 (19.4%) in the control group.
Weaknesses: There were a low number of patients in the study which decreased the validity of the study. There is no way to determine if the improvement is from the intensive education or the continuous glucose monitor.
Strengths: Multiple sites were used for the study- 10 locations. Multiple age groups were studied to allow for more factors, life styles, and activities to be studied. Only subjects in the sensor group improved their blood sugar control without increasing the amount of time spent in the hypoglycemic range. It was found that the greater the sensor utilization, the more significant the glycemic improvement. The study found that patent selection for CGM
51Running head: CONTINUOUS GLUCOSE MONITORING, BETTER CONTROL
therapy should take into account patient willingness and ability to use the technology appropriately.
Significance to my project: The treat-to target concept is vital to making A1C goals. This article speaks to this concept very clearly and I will focus part of my assignment on this article.
52Running head: CONTINUOUS GLUCOSE MONITORING, BETTER CONTROL
Raccah, D., Sulmont, V., & Reznik, Y. (2009). Incremental value of continuous glucose monitoring when starting pump therapy in patients with poorly controlled type 1 diabetes: the real-trend study. Diabetes Care, 32, 2245-2250.
Level of Evidence= II
The purpose of the study wasto investigate the benefit of continuous glucose monitoring (CGM) with the initiation of insulin pump therapy in subjects with poor metabolic control despite optimized basal-bolus insulin injection therapy.
Design: 132 subjects, randomized, parallel-group, two-arm, open-label trial. Eight centers in France (6 adult centers and 2 pediatric centers)Method: 6 month study duration.
Blinded CGM was used by all subjects for biochemical hyperglycemia and hypoglycemia parameters at the beginning and end of the study.Sampling Method: CSII subjects fitted with Minimed pump and agreed to use sensors at least 70% of the time.
A1C levels were significantly reduced within the Paradigm Real-Time (PRT) group and the pump group. The greatest A1c reduction occurred in the PRT group when using sensors at least 70% of the time in study. PRT subjects bloused more frequently after one month of treatment and at study end. A higher percentage of insulin was delivered as bolus in the PRT group vs. CSII group. Twice as many study subjects in the PRT group reported that they made modifications to their eating habits and lifestyle vs. study subjects using CSII alone. Over 90% of subjects in the PRT group used CGM data to manage their diabetes by adjusting insulin doses and 59% used CGM data to manage glycemic excursions.
Weaknesses: The study does not have a minimal SMBG testing frequency prior to the study as a requirement. No education was provided (or at least mentioned) for all patients prior to the study. Short trial duration. Not in U.S. Alarms and glucose trends information available to the PRT subjects may have been helpful for control.
Strengths: The study that continuous glucose monitoring does improve glycemic control. According to this study, no study had investigated the benefit of continuous glucose monitoring (CGM) at insulin pump initiation in patients with poor metabolic control on multiple daily injections (MDI). PRT subjects bloused more frequently
53Running head: CONTINUOUS GLUCOSE MONITORING, BETTER CONTROL
after one month of treatment and at study end.
Significance to my project:The focus of my PICO question is addressed in this article but I feel that the specifics are not strong. Several details, if you note my “weaknesses” comments bring concern to me. I will use some of the information for my project.
54Running head: CONTINUOUS GLUCOSE MONITORING, BETTER CONTROL
Dungan, K. M., Buse, J. B., Largay, J., Kelly, M. M., Button, E. A., Kato, S., & Whittlin, S. (2006). 1, 5-anhydroglucitol and postprandial hyperglycemia as measured by continuous glucose monitoring system in moderately controlled patients with diabetes. Diabetes care, 29(6), 1214-1219.
The purpose of this study is to demonstrate the relationship between 1,5-AG and postprandialhyperglycemia, as assessed by the continuous glucose monitoring system (CGMS) in sub optimallycontrolled patients with diabetes.
The design: Patients with type 1 or type 2 diabetes andan HbA1c (A1C) between 6.5 and 8% with stable glycemic control were recruited from two sites.
A CGMS monitor was worn for two consecutive 72-h periods. Mean glucose, mean post mealmaximum glucose (MPMG), and area under the curve for glucose above 180 mg/dl (AUC-180),were compared with 1, 5-AG, fructosamine (FA), and A1C at baseline, day 4, and day 7.
Results: 1,5-AG reflects glycemic excursions, often in the postprandial state,more robustly than A1C or FA. 1,5-AG may be useful as a complementary marker to A1C toassess glycemic control in moderately controlled patients with diabetes.
Weaknesses: The one weakness seen in this study was the small testing population of only 40 participants. It is hard to establish data on a small patient study.
Strengths: This study directly correlates to the significance of evidence for this project. There is much data regarding A1C lab work but not many on the GM lab results and effects.
Significance to Project: This article is very significant to my project and the contents of the study.
55Running head: CONTINUOUS GLUCOSE MONITORING, BETTER CONTROL
Mcgill, J. B., Cole, T. G., Nowatzke, W., Houghton, S., Ammirati, E., Gautille, T., & Sarno, M. (2004). Circulating 1, 5-anhydroglucitol levels in adult patients with diabetes reflect longitudinal changes of glycemia: a U.S. trial of the glycomark assay. Diabetes care, 27(8), 1859-1865.
Level of Evidence=II
The purpose of this study is to evaluate the ability of 1, 5-AG measurements to monitor glycemic control.
Glycemic control in a cohort of 77 patients with diabetes ( 22 with type 1 DM and 55 with Type 2 diabetes) who presented with suboptimal glycemic control at baseline (defined as A1C) >or= 7%. Each patient received DM education, counseling and addition or dose adjustment of various insulins or oral antihyperglycemic medications. Therapy was targeted to reduce mean A1C by > 1% over the monitoring period. 1,5AG, A1C and random glucose measurements were performed at baseline and at 2, 4 and 8 weeks.
1,5AG, and glucose values progressed significantly toward euglycemia by week 2 of monitoring with median changes of 93, -7, and -13% for 1,5AG and glucose respectively. In contrast, A1C results did not respond significantly to therapy until week 4.
Weakness: Not enough participants to be valid. Needs further testing. No mention of CGM devices.
Strengths: Very closely related to what evidence trial to studying in PICO question.
Relevance to study: Very relevant to this evidence based study as it relates to correlating the importance of A1C results as well as GM.
56Running head: CONTINUOUS GLUCOSE MONITORING, BETTER CONTROL