Clinical Epidemiology: Thyroid disease and test results - PowerPoint PPT Presentation

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

• 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.

• 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.

• 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.

• 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.

• 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

• You’ll use this a lot later in life…

• 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…

• 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…

• 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?

• 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?

• Order more tests?
• Schedule for surgery?
• Prescribe medication, therapy, hamburgers…?
• 1st, let’s see what the tests are really telling us.

• 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?

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

• We find:
• PPV = 37/95 = 31%
• NPV = 902/905 = 100%
• Which do we care about and what conclusions do we draw?

• 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?

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

• We find:
• PPV = 33/91 = 36%
• NPV = 902/909 = 99%
• Which do we care about and what conclusions do we draw?

• 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?

• 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.

• What do we know?
• Prevalence – 4%
• False positive rate – 20%
• False negative rate – 3%

• False positives = FP rate x all negatives = 0.20 x 960 = 192
• False negatives = FN rate x all positives = .03 x 40 = 1

• We find:
• PPV = 39/231 = 17%
• NPV = 768/769 = 100%
• Which do we care about and what conclusions do we draw?

• 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.

• 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.

• 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?

• 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

• For the general population (0.35%) we find:
• PPV = 33/233 = 14%
• NPV = 9765/9767 = 100%

• For all women (0.52%) we find:
• PPV = 49/248 = 20%
• NPV = 9749/9752 = 100%

• For all women aged 16-24 (2.9%) we find :
• PPV = 276/470 = 59%
• NPV = 9516/9530 = 100%

• 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…

• 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.

Questions or comments??

Contact info:

Wiley D. Jenkins, PhD, MPH

wjenkins@siumed.edu

217-545-8717