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testing for diabetes
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Josh Rubenschuh
Diagnostic Trial Review:
HbA1c as a Screening Tool for Type 2 Diabetes
Clinical Epidemiology
Professor Brent Faught
Introduction
Over the past 20 years the western world has seen obesity rates rise exponentially.
This unprecedented rise in obesity prevalence has been mirrored by a parallel rise in the
prevalence of type 2 diabetes mellitus. Type 2 diabetes is a metabolic disorder that is
characterized by high blood glucose resulting from peripheral insulin resistance. As of 2010
there were approximately 285 million people with the disease world wide as compared to only
30 million in 1985 (Guthridge, 2007). If left untreated diabetes can lead to many clinical
pathologies: hyperglycemia can contribute to the development of cardiovascular diseases like
myocardial infarction or stroke and the irregular glucose metabolism caused by diabetes can
lead to kidney and liver malfunction caused by ketoacidosis in the bloodstream.
The aetiology for type 2 diabetes is currently unknown, but it is hypothesized that
obesity causes insulin resistance, because adipose tissue stimulates a peripheral inflammatory
response, which is said to impair the sensitivity of insulin receptors throughout the body. When
peripheral insulin receptors function inadequately, the body cannot respond to insulin, and thus
the body is unable to regulate cellular glucose uptake and metabolism (Guthridge, 2007).
Currently, universal screening is not performed for type 2 diabetes as there is no
evidence that such a program would improve health outcomes. Screening for type 2 diabetes is
traditionally performed when a patient presents with high blood pressure or symptoms of
glucosuria, polyuria or polydipsia (Bennet, 2006). The diagnostic algorithm for diabetes
traditionally begins with a Fasting Plasma Glucose Test (FPGT), which measures the bodies
ability to metabolize a set amount of glucose within a defined period of time. If the FPGT
suggests impaired glucose metabolism then a blood sample is taken to confirm the presence of
insulin. If insulin is present in the blood it can be assumed that the body has become resistant to
insulin’s action, but if insulin is not present it can be assumed that the body’s insulin
production from the pancreas has failed (Bennet, 2006). In situations where the results of the
FPGT are inconclusive an Oral Glucose Tolerance Test can be performed, this test is more
expensive then the FPGT, but it is more sensitive and specific with regard to its diagnostic
abilities. There are tertiary levels of testing for type 2 diabetes that can diagnose the condition
with relative certainty, such as the hyperinsulinemic-euglycemic clamp test, but these tests are
rarely used in clinical practice because they are incredibly expensive and time consuming and
the secondary testing methods are adequately able to screen for diabetes (Bennet, 2006).
Type 2 diabetes is highly manageable and in some cases the condition can even be
cured. Diabetes management focuses on lifestyle interventions, lowering other cardiovascular
risk factors, and maintaining blood glucose levels within the normal range. Exercise is the
primary recommendation made to individuals suffering from type 2 diabetes because exercise
can stimulate insulin-independent glucose uptake into the muscles, which can control blood
glucose levels even in the presence of insulin receptor dysfunction (Guthridge, 2007). A low
glycemic diet and proper exercise program are usually sufficient to manage diabetes
effectively, but in cases where exercise is not possible or exercise is ineffective
pharmacological interventions are available. Biguanides and Thiazolidinediones are both drug
classes of insulin sensitizers that can effectively increase the insulin receptors affinity for the
hormone (Bennet, 2006).
With proper intervention and management strategies the prognosis for an individual
with type 2 diabetes is excellent. The patient can essential function unhindered by the
condition, but they may have to be cautious surrounding their dietary intake, because they will
be susceptible to adverse events if they consume high sugar foods in large quantities, or if they
fail to adhere to an exercise regimen (Bennet, 2006).
Question 1: Is This Test Potentially Relevant to my Practice?
In order for the HbA1c test to be a valuable screening tool it must
be useful within a general practitioners scope of practice, because primary
care facilities are the context in which diabetes screening would take place.
When using a screening tool, the clinician needs to ask whether or not the
screening test is relevant to their practice? This question is exceedingly
important because a clinician needs to evaluate whether the screening tool
in question is the best tool available in terms of utility and cost-
effectiveness. None of the articles directly addressed this question, instead
they focused on comparing the HbA1c test to the FPGT to determine which
test had higher efficacy and validity.
Focusing on the test itself, a prevailing consensus is starting to
emerge concerning the utility of Haemoglobin A1c (HbA1c) testing as a
front-line screening tool for diabetes (Saudek, 2008). After investigating the
efficacy and validity of the HbA1c test Herdzik et al. and Colagiuri et al.
have suggested that HbA1c is a better screening tool for diabetes than the
fasting plasma glucose test that is currently used for screening patients. In
contrast, Mannucci et al. determined that the HbA1c test did not warrant
inclusion in the screening protocol for diabetes.
HbA1c testing is an alternative to direct glucose measurements,
when screening patients for type 2 diabetes. HbA1c levels do not fluctuate
with day to day variations in plasma glucose levels; HbA1c levels are
representative of long term plasma glucose concentration (Saudek, 2008).
Haemoglobin A1c is produced in response to prolonged hyperglycaemia;
prolonged hyperglycaemia up-regulates the gene expression of HbA1c,
because this haemoglobin isoform is better than traditional haemoglobin at
transporting oxygen in glycaemic conditions. Increased production of the
HbA1c protein only occurs after prolonged glycaemic stress is placed on the
oxygen transport system.
HbA1c testing would improve a physicians ability to ascribe the
correct diagnosis of diabetes. Current glucose testing methods can be
rendered inaccurate by a person's recent diet and exercise regimen,
however HbA1c levels are indicative of long term glucose levels, so short
term lifestyle changes will not undermine the correct diagnosis when using
HbA1c testing (Saudek, 2008).
The HbA1c test also has excellent utility because type 2 diabetes is
highly treatable. Insulin sensitivity is inversely proportional to whole-body
adipose tissue content and plasma glucose levels (Saudek, 2008). The
detection of diabetes results in a patient being proscribed an exercise
regimen to decrease their whole body fat content and decrease their blood
sugar through insulin-independent muscle glucose uptake. Usually exercise
alone is sufficient to control or even eliminate type 2 diabetes. If adherence
to an exercise plan is unfeasible or if exercise proves ineffective, there are
also pharmacological interventions that can be employed.
Thiazolidinediones and Biguanides are drugs that sensitize the insulin
receptor, so that insulin mediated glucose uptake can return to normal and
diabetes can be controlled (Krentz, 2006). Exercise or drug interventions
are critical because prolonged untreated diabetes can have severe clinical
manifestations.
A series of practical considerations favour the use of HbA1c over
the Fasting Plasma Glucose Test, when screening for type 2 diabetes:
Firstly, the current screening tests for diabetes: the FPG test
require that the patient fast for a minimum of 8 hours prior to testing, but
the HbA1c test does not require any type of fasting (Saudek, 2008). Many
patients necessitate afternoon doctors appointments, therefore under the
current protocol patients would need to fast for the majority of the day,
which is both inconvenient and unhealthy. The need for a fasting blood
sample decreases the opportunity for a diagnosis of diabetes, because
some patients will want to avoid prolonged fasting, so they will avoid being
tested.
Secondly, HbA1c levels are not affected by short-term lifestyle
changes. A few days of dieting or increased exercise in preparation for a
doctor visit can significantly affect FPG, but HbA1c accurately reflects long-
term glycaemia (Saudek, 2008). Human metabolism, specifically blood
glucose regulation is highly dynamic and highly variable, which
necessitates that the screening test for diabetes must maintain high
validity in the face of sporadic fluctuations in glucose levels. HbA1c
maintains high validity in the face of lifestyle changes that may alter
glucose metabolism, while FPG is easily manipulated by short-term lifestyle
changes.
Thirdly, the HbA1c test is not a new or novel test within the realm
of a general practitioners practice, the HbA1c test is currently utilized by 78
percent of Canadian general practitioners for monitoring glycaemia in
diabetes patients. In the past HbA1c testing was not used in the screening
process because standardized and reliable diagnostic cut-offs values were
not available, but these standardized diagnostic values have since been
established (Saudek, 2008). The HbA1c would not require additional
training for physicians, so most physicians would not resist expanding the
use of HbAc1 within their practice.
Lastly, the HbA1c test costs approximately 68 dollars which is
significantly less than the 134 dollar cost of the FPGT (APRTG, 2012). Any
cost reducing strategy within the current healthcare funding structure is
highly beneficial. The inherent savings that would result from utilizing
HbA1c testing over the current tests could expand the number of people
who receive screening without increasing costs for the system. An increase
in the volume of patients screened could potentially unmask many of the
latent cases of type 2 diabetes within Canada.
According to the majority of literature surrounding HbA1c testing,
the HbA1c test should be placed into the screening protocol for diabetes
and from an economic perspective it should replace the FPGT, because it
outcompetes this test in terms of clinical efficacy and utility (Herdzik, 2002
& Coliguri, 2004).
Question 2: Has the Test Been Compared to a True Gold Standard ?
The hyperinsulinemic-euglycemic clamp is the ‘True Gold Standard’
for diagnosing type 2 diabetes, this "clamp" technique requires a steady IV
infusion of insulin to be administered into the left arm. The serum glucose
level is "clamped" at a normal fasting concentration by administering a
variable IV glucose infusion into the right arm. Numerous blood samples are
then taken over the course of the procedure to monitor serum glucose so
that a steady "fasting" level can be maintained (Castracane, 2003). In
theory, the IV insulin infusion should completely suppress hepatic glucose
production, thus gluconeogenesis and lipolysis should not confound the
test's ability to quantify how sensitive target tissues are to insulin. The
degree of insulin resistance should be inversely proportional to the glucose
uptake by target tissues during the procedure (Castracane, 2003). In other
words, the less glucose that's taken up by tissues during the procedure, the
more insulin resistant a patient is.
The hyperinsulinemic-euglycemic clamp is rarely used in the
clinical setting because it is very time consuming and exceptionally
expensive for a screening tool, with the materials and labour costing in
excess of 700 dollars per test. The high cost and technical difficulty
associated with the hyperinsulinemic-euglycemic clamp makes it
exceedingly difficult to use in a diagnostic trial (Castracane, 2003). All of
the diagnostic trials under investigation in this review chose to use the Oral
Glucose Tolerance Test (OGTT) as their ‘reference standard’ because this
test is highly valid and it is commonly used in the clinical setting because it
only costs about 130 dollars per test (APRTG, 2012).
All 3 studies under investigation compared the HbA1c test to the
‘reference standard’ Oral Glucose Tolerance Test. The OGTT encompasses
the administration of a standard dose of glucose orally and then 2 hours
later the plasma levels of glucose are measured to quantify insulin
dependant glucose uptake (Castracane, 2003). The problem with
comparing the HbA1c test to the OGTT reference standard is the fact that
the OGTT does not have 100 percent sensitivity and 100 percent specificity.
The reference standard, like almost every test is going to have inherent
error when attempting to determine the disease status of screened
patients. It is important to note that, in situations where the “True Gold
Standard” can not be administered, it is an acceptable practice to utilize
the most highly valid alternative test as the reference standard (Greenhalg,
1997). However, without definitively knowing the disease state of the
screened patients it could be erroneous to compare the congruency of
results between the OGTT and the HbA1c test. The congruency between the
OGTT and the HbA1c test may provide some insight into the efficacy of the
HbA1c test, but these results must be approached with caution, because
the OGTT has a relatively large chance of misdiagnosing and missing
diagnoses.
Question 3: Did this validation study include an appropriate spectrum of subjects?
When researchers design their studies, they attempt to get the
most appropriate spectrum of subjects to their disease of which they are
interested in. The more appropriate the spectrum of subjects in regards to
the disease, the more valid your results will be. What is very important is
prevalence. It is important that the studies have the same or very close
prevalence's of the disease as in general population when dealing with the
primary health care field. In order to be relevant and useful findings to the
general practitioner the prevalence of the study must be close to the
prevalence in the general population. If not, and the prevalence rates are
different it will influence the positive predictive value (PPV) and the
negative predictive value (NPV) and skew the findings. In all 3 of the
articles the prevalence of diabetes was similar to their general population
prevalence. The Mannucci et al (2002) study had a prevalence of 6.5% and
in Italy the prevalence of diabetes is 6.6% (Shaw, 2010); so no prevalence
bias is declared. The Colagiuri et al (2004) study had a prevalence of 7.4%
and in Australia the prevalence of diabetes is 5.7% (AIHW, 2011), so there
is a slight difference in prevalence which would slightly change the PPV and
NPV but not enough to declare a large prevalence bias. Finally, the Herdzik
et al (2002) study had a prevalence of 6.1% and the prevalence of diabetes
in Poland is 6.54% (Badave, 2010) which is similar and thus prevalence bias
is not declared. Overall, all three studies had similar diabetes prevalence's
as seen in the general population.
Furthermore, do theses studies have subjects that can be similar to
the general population and not filled with individuals with risk factors for
type 2 diabetes. If the study does not have an spectrum that is like the
general population there will be no external validity and be essentially
useless for the general practitioner. It has been found that the risk factors
for diabetes are age (older than 40), obesity, aboriginal/african/asian
populations, low SES and heart disease patients (Public Health Agency of
Canada, 2011). From what was given in the study, each study will be
analyzed in terms of their subject spectrum. The Mannucci et al study
subjects were all undiagnosed subjects, no smokers, majority of
commuters, volunteers from newspaper and television ads,
weight/height/sex/family history were all taken but not given in the study
except for a large number of obese individuals. This study did not have an
appropriate spectrum and thus the results may not be externally valid. We
don't know the risk factor distributions that may have an effect on the
results such as weight, family history and socioeconomic status. The issue
with not having this data means that the results might work in favour of the
researchers and be inaccurate. The Colagiuri et al study had an appropriate
spectrum because their subjects were from various ethnicities, all over 25,
from urban and non urban areas equally and had a sample size of 10,447
which would approximate a normal distribution and resemble the general
population. Thus, the results could be applied to the general practitioners
office scenario where he deals with the general population. The Herdzik et
al article had a somewhat appropriate spectrum in that the majority of risk
factors were distributed although the study consisted of only Caucasians.
Due to there only being Caucasians in this study, this articles findings are
not totally externally valid.
Question 4: Has Workup Bias Been Avoided?
In diagnostic trials it is essential to the validity of the study that the
gold standard or reference standard be done on every subject. There is a
potential bias in studies where this does not occur and this is called work up
bias. When work up bias occurs the gold standard or reference standard is
only done to those who tested positive leading to an overestimation of the
sensitivity. In all three of the articles work up bias has been avoided. All
subjects had the OGTT the reference standard for all three articles. Thus
there are no ramifications for any of the articles in terms of work up bias
because work up bias was absent in all three articles. In this case it was
justified that all individuals should get the OGTT because it is not a majorly
severe or too costly procedure thus all subjects were able to have it done.
Question 5: Has Expectation Bias Been Avoided ?
It is possible that when analyzing the results of a screening test a
researcher could be influenced by peripheral knowledge that is unrelated
to the actually results of the screening tool. For instance if the researchers
investigating the efficacy of HbA1c testing knew the disease state of the
patients they were screening, it is entirely possible that their outcome
expectations might influence the way they interpret the results of the
HbA1c test. Knowing the disease state of screened individuals or even
knowing patient symptoms can lead researchers to make a diagnosis that
may not be congruent with the actual test results.
An excellent way to prevent expectation bias is to blind those
involved with interpreting test results. If the researchers are privy to no
peripheral information about the patients disease status or symptoms,
they are less likely to be influenced by confounding information when
interpreting the test results, thus eliminating expectation bias (Greenhalg,
1997).
The articles by Herdzik et al. and Colagiuri et al. both explicitly
addressed the fact that the biochemists running their HbA1c tests were
completely blinded and had no previous knowledge of patient disease
status or symptoms. The blinding of research analysts suggests that the
results of these studies were not influenced by expectation bias, and thus
they retain relatively high validity. Unfortunately, the article by Mannucci
et al. made no mention of any blinding protocol; it appears that the
researchers administered the OGTT first and established the disease state
of each patient, then the same set of researchers administered the HbA1c
test. The fact that the researchers had a previous knowledge of patient
disease status prior to administering the HbA1c test may have potentially
biased their results based on their personal expectations. The HbA1c test
produces a serum concentration value, if the serum concentration falls
above or below a range of inconclusive values, then a definitive diagnoses
can be made, however there are intermediary ‘inconclusive’ plasma
concentrations where the determination of disease status is left up to the
“expert” analyst (Saudek, 2008). The interpretive nature of this test leaves
it exceptionally vulnerable to expectation bias, because if the test
produces an ‘inconclusive’ concentration, an individual who knows the
expected outcome will most likely classify the patient in accordance with
their preconceived disease status.
Question 6: Was the Test Shown to be Reproducible?
Essentially the test is reliable and reproducible if the same or
different observers perform the same test on two occasions on a subject
whose characteristics have not changed will they get the same result. All
three studies failed to report the validity and reliability of their statistical
measures, but because the HbA1c test is done by scientific analysis of
Hemoglobin A levels through a blood sample as long as the equipment is
working correctly the test should be reproducible with the same results
every time. Therefore, the test should be reproducible based on those
facts, although because the Mannucci et al. paper did not have an
appropriate spectrum the results might not be valid, and if something is
not valid it may not be reliable. Therefore the results from that study
might not be reliable whereas the results from the other two studies that
promote HbA1c use are reliable because they are more valid. McCarter et
al (2004) has tested the variation of the Hba1c test and found that there is
low intra-individual variation which is useful in the early detection of
diabetes, and thus HbA1c is meaningful for clinical workers and has been
shown to have strong reproducibility. The ramifications of not being
reliable or being reproducible will cause error in your statistical measures
because they will not be as precise as what they should be. The actual
specificity might not be actually what they report due to their errors and
unreliability.
Question 7: What are the Features of the Test as Derived From This Validation Study?
In the analysis of diagnostic trials and whether the test is valid, all
the above standards that have been previously talked about may be met
but the test might still be worthless because the sensitivity, specificity,
and other crucial features such as the PPV and NPV are too low, in other
words the test is not valid. What counts as acceptable is up for debate, in
terms of screening we are looking for a high specificity. in the case of
diabetes, we have a relatively common disease and so the specificity is
very important. For screening purposes, a test that produces a large
number of false positives would pose major problems to health
departments and cost to many resources. Thus, a high specificity is
essential. Furthermore, the positive predictive value and negative
predictive value are essential to this situation at the primary care level.
These values are essential because approximately 90% of the time we are
not sure if the individual has the disease but what is known is the outcome
of our clinical test. As well, Positive likelihood ratios and negative
likelihood ratios have clinical importance for the general practitioner. Each
study has different features which will be displayed in a summary table.
Table 1: Statistical features as derived from the Mannucci et al, Colagiuri
et al, and Herdzik et al papers in terms of the HbA1c diagnostic tool with
OGTT as the reference standard.
Statistical Feature Mannuci et al,
2002
Colagiuri et al,
2004
Herdzik et al, 2002
Sensitivity 98% 78.7% 73.7%
Specificity 30% 82.8% 93.2%
False Negative Rate 2% 21.3% 26.3%
False Positive Rate 70% 17.2% 6.8%
Prevalence 6.5% 7.4% 6.54%
Positive Likelihood
Ratio (PLR) 1.3 4.58 10.84
Negative
Likelihood ratio
(NLR)
0.31 0.26 0.28
Accuracy 64% 80.8% 83.45%
Positive Predictive
Value 8.83% 26.7% 42.3%
Negative Predictive
Value 99.4% 97.98% 98.1%
When looking at the PLR the Herdzik et al article has a PLR of 10.84
which essentially means that given a positive test result we can rule in
that they have the disease making the test effective, although Mannuci
claims the test has a PLR of 1.3 meaning there is no change in the
likelihood of having the disease. Colagiuri has a 4.58 PLR which would
agree with Herdzik in that it is an effective screening tool. Furthermore, an
effective screening tool for a common disease like diabetes must have a
strong specificity because we want to correctly identify those people who
do not have diabetes at the expense of not identifying people who actually
have the disease. This is logical in that diabetes in its early stages is not a
very virulent disease that can be treated readily. Colagiuri et al and
Herdzik et al both claim it has a high specificity 82.8% and 93.2%
respectively, whereas Mannuci et al claims the specificity is only 30% and
thus not an effective tool for use at the general practitioners clinic. All
three articles have similar prevalence's which mean, the prevalence won't
have differing effects on the PPV between the different articles; it can be
seen with high prevalence's that the PPV will increase. Thus even though
the previous factors of validity could have been complete in the Mannucci
et al article (which they aren't) the test would still not be valid in their
sense because of the low specificity, PLR and other features.
Question 8: Were Confidence Intervals Given?
Another useful tool in demonstrating the validity in a diagnostic
test and the corresponding statistical features is the use of confidence
intervals to account for statistical ambiguity. Through a basic statistical
concept; such as the following, as a sample size gets larger, the
confidence interval gets smaller it can be seen that it is vitally critical for
smaller studies to include confidence intervals to account for this
uncertainty. Unfortunately none of our studies include confidence intervals
for any of their statistical features, therefore we are uncertain how
accurate they are. Take specificity for example, any of the three articles
could've put down a number like 50 but the actual range could've been
20-80… how accurate is that. Due to this we have to question whether the
features are actually correct. For example, did Mannucci have a hidden
agenda and want to put down the HbA1c tool so that the fasting plasma
glucose test would be used instead at the clinic because him and his
researchers had ties to that test. All in all, if confidence intervals aren't
given we have to question the validity of the results. Although, in larger
studies such as the Colagiuri et al study whose sample size is 10447 the
confidence intervals would be inherently small due to the large sample
size and thus those results would be more precise.
Question 9: Has a Sensible “Normal Range” Been Derived?
When diagnosing diabetes using the HbA1c test, there is no exact
“normal range” that exists. There is simply no clean cut-off between what
would be normal and abnormal… but through PLR's and a ROC we can get
as close as we can to optimize our tests. In fact, Jesudason et al (2003)
and Tavintharan et al (2000) recommend the use cut off value of 6.2% to
be the best predictor of diabetes for the HbA1c test. In the Colagiuri study
the best predictive value was 5.3% calculated from the receiver operating
characteristic (ROC) curve. This cut off value was associated with a
specificity of 82.8% and a sensitivity of 78.7%. Herdzik et al did not claim
how they came up with their cut off value of 6.4%, but they had a
specificity of 93.2% and a sensitivity of 73.7% so the cut off value of 6.4
must be quite high on the ROC. Furthermore, the ROC curve analysis was
used by Mannucci to find a cut off point of 6.6%. Not having a universal
cut-off point is a problem in that studies will use different values this leads
to some studies showing more detection than others (affecting results i.e.
sensitivity/specificity) but also with different cut off points it is hard to
accurately compare the different studies. Furthermore, likelihood ratios
are important here to calculate the right range. Herdzik et al used the cut
off value of 6.4% and found a PLR of 10.84 which is very effective in the
ability to say they have the disease with a positive test result. This cut off
value resembles what other researchers found as the optimum cut off
point for increasing the specificity and PLR which are very essential for the
general practitioner use in the clinic with a common disease such as
diabetes. The ramifications of not having a sensible range would be the
possibility of having too high and too low of a cut-off point and might lead
to too many false negatives or false positives, respectively and also
depending on the circumstances.
Question 10: Has This Test Been Placed in the Context of
Other Potential Tests in the Diagnostic Sequence
The ultimate utility of the HbA1c diagnostic test depends on its
ability to be used in a diagnostic algorithm. The study by Colagiuri et al.
has suggested that the HbA1c test should be placed into the screening
algorithm as the secondary step towards diagnosing type two diabetes.
Dr. Colagiuri and his team of researchers believe the screening protocol
should begin by identifying individuals who have 3 or more risk factors for
diabetes, for example obesity, a family history of diabetes or high blood
pressure. Once susceptible patients have been identified they should be
screened using the HbA1c test and those who test positive according to
the HbA1c test would be screened using the OGTT. It is important to note
that Colagiuri et al. suggested that the HbA1c test replace the FPGT as the
secondary screening tool in the diagnostic algorithm based on their
findings that the HbA1c test is more sensitive and specific than the FPGT
and the HbA1c is less susceptible to confounding by short term lifestyle
changes. The findings of Colagiuri et al. definitely support replacing the
FPGT with the HbA1c test because if the FPGT was retained as the primary
screening tool for diabetes with such a low sensitivity it is entirely possible
that a large number of individuals with diabetes would go undiagnosed.
The primary screening tool needs to be highly sensitive in order to limit
the number of false negatives. Tests with higher specificity can be
employed later in the algorithm in order to screen out any false positives.
The study by Herdzik et al. has also suggested that the HbA1c test
be included within the screening protocol for type 2 diabetes, but they
believe the HbA1c test should follow the FPGT in the diagnostic algorithm.
Dr. Herdzik and his team found that the FPGT had a slightly higher
sensitivity then the HbA1c test, but the Haemoglobin A1c test was much
more specific, therefore they concluded that the HbA1c test should be
used as a secondary confirmatory test following primary screening by the
FPGT. The results obtained by Herdzik et al. support the conclusion that
the FPGT should be retained in the diagnostic algorithm and subsequently
followed by the HbA1c test, because the diagnostic sequence will utilize
the high sensitivity of the FPGT followed by the high specificity of the
HbA1c, their paired validity is higher then each test independently. Also
the researchers concluded that the HbA1c test has the ability to hold its
validity in the face of confounding factors such as short term lifestyle
changes that could influence the FPGT.
Finally, the study by Mannucci et al. argued that the HbA1c test
had no place within the diagnostic algorithm because the current
diagnostic thresholds for the test were inadequate for accurate
diagnostics. Dr. Mannucci and his team found that the FPGT had a higher
sensitivity and specificity then the HbA1c test, so they suggested that the
diagnostic protocol for diabetes remain the same with FPG as the primary
screening tool, followed by the OGTT. Based on the study findings Dr.
Mannucci and his team made the correct assessment because including a
test in the diagnostic algorithm that is superior to none of the current tests
is simply just a waste of resources.
Do the Conclusion and Recommendations fit the Content?
Based on the comprehensive analysis of all 3 diagnostic trials
analyzed within this review, the panel agrees with the conclusions of
Herdzik et al. and Colagiuri et al. These two studies have concluded that
the HbA1c test for screening diabetes should definitely be included within
the diagnostic protocol for the disease. This panel believes that the results
of the Mannucci et al. article conflicted with the other 2 studies due to
multiple errors within the studies methodology. The Mannucci et al. article
did not provide confidence intervals for their outcome measures and they
used a sample size that was unable to illicit sufficient power. Without
confidence intervals or sufficient power it is impossible to determine the
accuracy and precision of the statistical measures. In addition, Dr.
Mannucci and his team did not include an appropriate spectrum of
subjects within their trial and their study prevalence was much lower then
that of the external population. All of these flaws within their study
methodology call their results into question, so their negative measures
assessing the HbA1c test may not be valid or reliable.
Based on the congruent results of the Herdzik et al. and Colagiuri
et al. articles as well as peripheral research, it appears that the HbA1c test
has extremely high utility within the diagnostic protocol for type 2
diabetes. With the HbA1c tests newly established diagnostic cut-offs it
appears to be highly sensitive and specific. The HbA1c test is also
extremely useful in the diagnostic sequence because it retains high
validity in the face of environmental confounders that would influence the
glucose measurement tests. Additionally, the HbA1c test is a cost effective
alternative to the primary testing tools currently utilized within the
diagnostic spectrum for diabetes. Therefore, this panel concludes that the
HbA1c test is an extremely useful screening tool for diabetes mellitus and
it should undoubtedly be placed within the diagnostic algorithm as the
primary screening protocol.
Works Cited
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APRTG. (2012) Biochemical Suppliers Ltd. Online store. Retrieved from: http://www.healthtestingcenters.com/hemoglobin-blood-test.aspx
Badave, Pol-Diab. Sieradzki, J. (2010). Diabetes: The Hidden Pandemic and its Impact on Poland. Diabetes. Novonordisk. 3: 8-15.
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