Clinical Epidemiology: Thyroid disease and test results

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Clinical EpidemiologyThyroid disease and test
results

• Wiley D. Jenkins, PhD, MPH
• Research Assistant Professor
• Southern Illinois University School of Medicine
• Department of Family and Community Medicine

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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 tests 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

• Youll 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

• 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 0.5) Normal
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, lets 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

• Complete the table and calculate the PPV and NPV
  assuming sens 92, spec 94 and prevalence
  4

Disease/Condition Disease/Condition Disease/Condition Disease/Condition
Exposure/Test () (-)
Exposure/Test ()
Exposure/Test (-)
Exposure/Test
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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

• 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

• Complete the table and calculate the PPV and NPV
  assuming sens 82, spec 94 and prevalence
  4

Disease/Condition Disease/Condition Disease/Condition Disease/Condition
Exposure/Test () (-)
Exposure/Test ()
Exposure/Test (-)
Exposure/Test
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Free T4 table - completed

• 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
• 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

• 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

• 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 Hashimotos or
  other disease.

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Lab results
Test Results
TSH Low normal
Total T4 High
Free T4 High
Total T3 High normal
Free T3 Normal
TBG High
Thyroid Antibodies Normal
Fine needle aspiration biopsy Indeterminate
Interpretation
Real because T4 suppressing TSH
Real OCPs increase TBG
False positive
Real
Real
OCP Effect
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

• Diagnostic companies have spent considerable
  money developing rapid and accurate tests for the
  detection of Ct.
• Current tests offer
• 95 sensitivity
• 98 specificity
• So, do we just test everyone? Lets see.
  (150,000,000 women) x (10/test) need for
  other alternative.
• 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 gt90!
• 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 wjenkins_at_siumed.edu 217-545-8717