Download Clinical Epidemiology:Thyroid disease and test results

Clinical Epidemiology:
Thyroid disease and test results
Wiley D. Jenkins, PhD, MPH
Research Assistant Professor
Southern Illinois University School of Medicine
Department of Family and Community Medicine
Who I am
• My name is Wiley D. Jenkins and I am currently
Research Assistant Professor at the SIU-SOM
Department of Family and Community Medicine.
Prior to this I spent 13 years in the state health
department laboratory.
• I received my MPH-Epidemiology from Tulane
University in 2002. This was followed by my PhD
in Health policy from the University of Illinois at
Chicago in 2007.
• Much of my research and work experience has
concerned laboratory testing, STDs and the
quality of laboratory data.
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Learning objectives
• To understand the concepts of test sensitivity,
specificity, positive predictive value and
negative predictive value.
• To understand how these factors effect the
utility of individual tests when diagnosing a
condition.
• To understand how these factors are
manipulated by targeting screening tests to
specific populations.
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Performance objectives
• To be able to calculate the sensitivity,
specificity, positive predictive value and
negative predictive value for a given test.
• To be able to determine if a test’s result is
useful given its calculated values.
• To be able to show how screening guidelines
should be adjusted to increase positive and
negative predictive values to maximize result
usefulness.
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There is always uncertainty
• Our common language incorporates uncertainty.
– “Usually” implies error bars
• Physics tells us that in an infinite universe, anything is
possible. Some things are just more or less likely.
• Heisenberg uncertainty principle:
– statement that locating a particle in a small region of space
makes the momentum of the particle uncertain; and
conversely, that measuring the momentum of a particle
precisely makes the position uncertain
• As a matter of practicality, some things are essentially
“100%” or “always” something. HOWEVER, its
important to know when this is not the case, and that is
not always obvious.
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Quick review of terms
• Sensitivity – the ability of a test to correctly
identify those who have a condition
• Specificity – the ability of a test to correctly
identify those who do not have a condition
• Positive predictive value – the number of
individuals who have a condition from all those
who test positive
• Negative predictive value - the number of
individuals who do not have a condition from all
those who test negative
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The 2 x 2 table
• You’ll use this a lot later in life…
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Sensitivity
• 90% sensitivity implies that of all those who have the disease, 10% will not be
identified by the test. If prevalence is 20% of the population…
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Specificity
• 75% specificity implies that of all those who do not have the disease, 25%
will not be identified by the test. If prevalence is 20% of the population…
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Positive/negative predictive value
•
We complete the remaining marginals and find:
– PPV for our example test is 180/380 = 47%
– NPV is 600/620 = 97%.
– What do we draw from this about the usefulness of the test?
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Time for a clinical example
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27-year-old woman
10 lb weight loss in past two months, not trying
Some difficulty sleeping
Never had anything like this before
No signs/symptoms of depression
Meds: Oral contraceptive pills
1-cm, firm, smooth nodule in right lobe of thyroid
BMI = 20
Skin slightly dry
Remainder of physical examination normal
• What do you think?
• What should we do?
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Lab tests and results
Test
Results
TSH (thyroid stimulating hormone) Low normal
Total T4 (thyroxine)
High
Free T4 (no protein attachment) High
Total T3 (triiodothyronine)
High normal
Free T3 (no protein attachment; Normal
0.5%)
TBG (thyroxine binding globulin)
High
Thyroid Antibodies
Normal
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What next?
• Order more tests?
• Schedule for surgery?
• Prescribe medication, therapy, hamburgers…?
• 1st, let’s see what the tests are really telling us.
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Thyroid stimulating hormone
• Our patient has a (low) normal TSH
– Sensitivity = 92%
– Specificity = 94%
– Are these good values?
• Assume prevalence for thyroid disease of 4% in large
populations
• Calculate PPV and NPV for TSH
• Do we care more about the PPV or NPV for this scenario?
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TSH 2 x 2 table
Exposure/Test
• Complete the table and calculate the PPV and NPV
assuming: sens = 92%, spec = 94% and prevalence = 4%
Disease/Condition
(+)
(-)
(+)
(-)
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TSH 2 x 2 table - completed
•
We find:
– PPV = 37/95 = 31%
– NPV = 902/905 = 100%
– Which do we care about and what conclusions do we draw?
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Free T4
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Our patient has an elevated Free T4
Sensitivity = 82%
Specificity = 94%
Assume prevalence for thyroid disease of 4% in large
populations
• Calculate PPV and NPV for Free T4
• Do we care more about the PPV or NPV for this scenario?
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Free T4 table
Exposure/Test
• Complete the table and calculate the PPV and NPV
assuming: sens = 82%, spec = 94% and prevalence = 4%
Disease/Condition
(+)
(-)
(+)
(-)
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Free T4 table - completed
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We find:
– PPV = 33/91 = 36%
– NPV = 902/909 = 99%
– Which do we care about and what conclusions do we draw?
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So…
• We have:
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A symptomatic woman on OCPs with a thyroid nodule
A normal TSH
An elevated Total T4
An elevated Free T4
• What next?
– Scintigraphy?
– Fine Needle Aspiration Biopsy?
– Excisional Biopsy?
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Fine needle aspiration biopsy
• Indeterminate result
• 15-20% false positive rate (assume 20% for calculations to
follow)
• 3% false negative rate
• If we assume a 4% prevalence of thyroid cancer, calculate the
sensitivity and specificity of the biopsy.
• Calculate the positive and negative predictive value.
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The FNAB 2 x 2 table
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What do we know?
– Prevalence – 4%
– False positive rate – 20%
– False negative rate – 3%
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The FNAB 2 x 2 table
• False positives = FP rate x all negatives = 0.20 x 960 = 192
• False negatives = FN rate x all positives = .03 x 40 = 1
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The FNAB 2 x 2 table - completed
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We find:
– PPV = 39/231 = 17%
– NPV = 768/769 = 100%
– Which do we care about and what conclusions do we draw?
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Clinical course
• The patient was referred to a surgeon for excisional
biopsy.
• Nodule was removed, was a benign colloid goiter, no
malignancy and no evidence of Hashimoto’s or other
disease.
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Lab results
Test
Results
Interpretation
TSH
Low normal
Real because T4 suppressing TSH
Total T4
High
Real – OCPs increase TBG
Free T4
High
False positive
Total T3
High normal
Real
Free T3
Normal
Real
TBG
High
OCP Effect
Thyroid
Antibodies
Normal
Fine needle
aspiration biopsy
Indeterminate
Real
False Positive
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How do laboratory tests contribute to medical errors?
• Are not always right
• May result in unnecessary further testing
• May result in unnecessary surgery
– With attendant complications
• If we assume that tests are correct 95% of the time, what is
the likelihood that, in a battery of 20 tests, one will be a
false result?
• So, for every Chem 20 you order (or other battery of 20
tests), 1 will be either a FALSE POSITIVE or a FALSE
NEGATIVE.
• Need to know how to work with sensitivity and specificity
in order to know what to believe.
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Time for a population example
• Why, because we like you! (M – I – C…)
• Seriously though, population-level studies are translated into
clinical guidelines.
• In 2006, the number of reported cases of Chlamydia trachomatis
(Ct) in the US exceeded 1,000,000 for the 1st time.
• The great majority of cases (~70% in women) are entirely
asymptomatic.
• Upwards of 40% of untreated Ct progress to PID; followed by
chronic pelvic pain, ectopic pregnancy and infertility.
• How do we address this?
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Chlamydia trachomatis screening
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Diagnostic companies have spent considerable money developing rapid and
accurate tests for the detection of Ct.
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Current tests offer
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~95% sensitivity
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~98% specificity
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So, do we just test everyone……? Lets’ see. (~150,000,000 women) x
(~$10/test) = need for other alternative.
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Who has Ct?
• 0.35% all Americans
• 0.52% women
• 0.17% men
• 1.76% Black women
• 0.24% White women
• 2.9% women aged 15-19
• 2.8% women aged 20-24
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The Ct 2 x 2 table - completed
• For the general population (0.35%) we find:
– PPV = 33/233 = 14%
– NPV = 9765/9767 = 100%
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The Ct 2 x 2 table - completed
• For all women (0.52%) we find:
– PPV = 49/248 = 20%
– NPV = 9749/9752 = 100%
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The Ct 2 x 2 table - completed
• For all women aged 16-24 (2.9%) we find :
– PPV = 276/470 = 59%
– NPV = 9516/9530 = 100%
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Utility of targeted testing
• By purposefully targeting our testing to at-risk populations, we
increase the PPV of the test and better allocate resources.
– General population
• Prevalence = 0.35%
PPV = 14%
– All women
• Prevalence = 0.52%
PPV = 20%
– Women aged 16-24
• Prevalence = 2.9%
PPV = 59%
– Females admitted into juvenile detention centers??
• Prevalence = 12-20%
PPV = >90%!
– Other risk factors important.
• This works for clinical guidelines for screening, such as
mammography, prostate exams, cholesterol…
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Take away items
• Not a good practice to order tests “just because we can” or for
“fishing expeditions.”
• Costs can quickly become quite significant (e.g. compare HC
expenditure for US versus other industrialized countries and
resultant health outcomes).
• Utility of the results is directly impacted by the
population/person to which they are given.
• Multiple tests increase the likelihood of a correct diagnosis.
– E.g. Ct in 16-24, PPV = 59%
– Additional test on just these positives (e.g. 59% prevalence) with same
sens/spec results in PPV of 99%!
• In the absence (always) of the “ultimate test”, use multiple
results to arrive at the best conclusion.
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Questions or comments??
Contact info:
Wiley D. Jenkins, PhD, MPH
[email protected]
217-545-8717
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