Transcript Slide 1

Diagnosis
A diagnostic test is ordinarily understood to mean a test
Performed in laboratory ,but the principles discussed
apply equally well to clinical information obtained from
History, physical examination ,and imaging procedures.
They also apply when a constellation of findings serves as a
diagnostic test.
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clinicians generally reduce the data to simpler
from to make them useful in practice. Example
:heart murmurs
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More often. Complex data are reduced to a
simple dichotomy(for example. Present/absent,
abnormal/normal, or diseased/well)
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Exampel :blood pressure
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The doctor’s certnainty or uncertainty about
a diagnosis has been expressed by using
terms such as rule out or possible before a
clinical diagnosis
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A simple way of looking at the relationships
between a test’s resulst and true diagnosis is
shown in next figure
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The accuracy of a test result
The doctor’s certnainty or uncertainty
about a diagnosis has been expressed by
using terms such as rule out or possible
before a clinical diagnosis
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More often, one must turn to relatively
elaborate, expensive, or risky tests to be
certain whether the disease is present or
absent
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Sometimes the standard of accuracy is itself a
relaltively simple and inexpensive test such
as a throat culture for group A streptococus
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For disease that are not-limited and ordinarily
become overt in matter of few years after they
are first suspected,the resulsts of follow up
can serve as a gold standard such as cansrs
and coronic diseases
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Clinicians and patients prefer simpler tests to
the rigorous gold standard
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The goal of all clinical studies
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Most information about the value of a diagnostic
test is obtained from clinical,and not reaserch
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Data on the number of true negative versus false
negetives generated by a test tend to be much
less complete in the medical literature than data
collected about positive test resultes
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For some conditions,there are simply no hard
and fast criteria for diagnosis : angina pectoris
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The validity of laboratory test is established by
comparing its results to a clinical diagnosis
based on a careful history of symptoms and a
physical examination
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physician must choose as their standard of
validity another test that admittedly is
imperfect,but is considered the best avalible
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If a new test is compaired with an old standard
test,the new test may seem worse even
thought it is actually better
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Sensitivity is defined as the proportion of people
with the disease who have a positive test for the
disease.A sensitive test will rarely miss people
with the disease
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Specificity is the proportion of people without
the
diseases
who
have
negative
test.
A
specificity test will rarely misclassify people as
having the disease when they do not.
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when there is an important penalty for missing a
disease such as TB, syphilis; hodgkin’disease
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when a great many possibilities are being
considered; to reduce the number of possibilities.
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A highly sensitive test is most helpful to the
clinician when the test result is negative.
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Spesific test useful to confirm for rule out
diagnosis that has been suggested by other
data
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Before
patients
are
subjected
to
cancer
chemotherapy
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In sum, a highly specific test is most helpful
when the test result is positive
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Another way to express the relationship between sensitivity
and specificity for a given test is to construct a curve, called
receiver operator characteristic(ROC) curve
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The ROC curve shows how severe the trade-of between
sesitivity and spesitivity is for a test and can be used to help
decide where the best cutoff point should be
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ROC
curves are particularly valuable ways of comparing
alternative tests for the same diagnosis.
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The overall accuracy of a test can be described as the area
under the ROC curve
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At the crudest level, Sensitivity and specificity
may be inaccurately because an improper gold
standard has be chosen .
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Two other issue related to the selection of
diseased and nondiseased patients can
profoundly affect the determination of sensitivity
and specificity
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Spectrum/bias
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Difficulties may arise when the patients used to describe the
test’properties are somehow different from those to whom the
test will be applied in clinical practice
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Example: CEA in diagnosing colorectal cancer
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The sensitivity and specificity of test are independent of the
prevalence of diseased(in theory)
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Several characteristics of patient,such as stage and severity of
disease may be related to the sensitivity and specificity of a
test and to the prevalence(in practice)
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The sensitivity and specificity of a test should be
established independently of the means by which
the true diagnosis is established
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All the biases discussed tend to increase the
agreement between the test and the gold
standard. That is, they tend to make the test
seem more useful than it actually is
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Values for sensitivity and specificity are
usually estimated from observations on
relatively small samples of people of peaple
with and without the disease of interest
Reported value for sensitivity and specifity
shoud not be taken too literally if a small
number of patients is studied
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The probability of disease,given the results of a test is
called the predictive value of the test
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Positive predictive value is the probability of disease in
a patient with a positive test
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Negative predictive value is the probability of not having
the disease when the test result is negative
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Predictive
value
:
“if
my
patient’
test
result
is
positive,what are the chance that my patient does have
the diseases?
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The predictive value of a test is not a
property of the test alone
It is determined by the sensitivity and
specificity of the test and the prevalence of
disease in the population being tested
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The more sensitive a test is, the better will be its negative
predictive value (the more confident the clinician can be
that a negative test result rule out the disease being
sought)
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The more specific the test is the better will be its positive
predictive value
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Positive results even for a very specific
test,when applied to patients with a low
likelhood of having the disease will be
largely false positive.similarly,negative
results
Therefore as the prevalence of disease in a
population approaches zero,the positive
predictive value of a test also approaches
zero
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As prevalence approache 100% negative
predictive value approaches zero
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Because prevalence is such a powerful
determinant of how useful a diagnostic test will
be, clinicians must consider the probability of
diseases before ordering a test
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Although the resulting estimate of prevalence
are not likely to be very precise,using them to
be more accurate than implicit judgment
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Diagnostic tests are most helpful when the
presence of disease is neither very likely nor
very unlikely
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There are several ways in which the
probablity of a disease can be increased
before using a diagnosis test
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Referral to teaching hospital wards,clinics,
and emergency departments increases the
chance that significant disease will underlie
patient’complains
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…Diagnostic evaluations may need to be
adjusted to suit the specific situation
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Physician can increase the yield of diagnostic
tests by applying them to demographic group
known to be at higher risk for a disease
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a sickle cell test in african american
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They are clearly the strongest influence on the order tests .
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Symptoms,signs,disease risk factors all raise or lower the
probability of finding a disease
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The value of applying diagnostic tests to persons more likely
to have a particular illness is intuitively obvious to most
doctors
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The less selective the approach,the lower the prevalence of
the disease is likely to be and the lower will be the positive
predictive value of the test
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Published description of diagnostic tests often in clude,in
addition to sensitivity and specificity,some conclusion about
predictive value
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The data for these publications are often gathered in
university teaching hospitals where the prevalence of serious
disease is relatively high
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Statements about predictive value in medical literature
may be misleading when the test is applied in less
highly selected settings
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Likehood ratios are an alternative way of
describing the performance of a diagnostic
test that can be used to calculate the
probability of diesease after a positive or
negative test
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Probability used to express sensitivity,
specifity and predictive valuertion
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It is the proportion of people in whom a
particular characteristic
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Odd is the ratio of two probabilities
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The likelihood ratio for a particular value of a
diagnostic test is defined as the probability of
that test result in people with the disease divided
by probability of the result in people without
disease
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If a test yields dichotomous results two types of
likelihood ratio describe its ability to discriminate
between disease and no diseased people
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It can accommodate the common clinical
practice of putting more weight on extremely
high test results than on borderline ones
when estimating the probability that
particular disease is present
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It is particularly well suited for describing the
overall odds of disease when a series of
diagnostic tests is used
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Pretest odds contain the same information as
prior the same as sensitivity/specifity and
post test odds the same as positive predictive
value
The main advantage of likelihood ratio is
possible to go beyond the simple and clumsy
classification of a test result as either
abnormal or normal
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Because clinicians commonly use imperfect diagnostic
tests with less than 100% sensitivity and specificity ,a
single test results in probability of disease that is
neither vary high nor vary low
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The physician is ordinarily bound to raise or lower the
probability of disease substantially in such situations
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When multiple different test are performed and all are
positive or all are negative ,the interpretation is
straighforward
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Multiple diagnostic tests can be applied in two basic
ways
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Multiple diagnostic can be used in parallel testing and
a positive result of any test is considered evidence for
disease
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They can be done in serial testing with the decision to
the next test it based on the previous test and
diagnostic process is stopped with a negative result
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Multiple tests in parallel generally increase
the sensitivity and negative predictive value
above of each individual test
By using the tests in parallel the net effect
is a more sensitive diagnostic strategy
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Physician most often use serial testing
strategies where rapid assessment of
patient is not required
When some of tests are expensive or risky
It leads to less laboratory use than parallel
testing
Maximizes
specificity
and
positive
predictive value but lowers sensitivity and
negative predictive value
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As reach test done, the posttest odds of one
became the pretest odds for the next in the
end a new probability of disease is found that
takes into account the information
contributed by all the tests in the series
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