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.
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.
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.
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.
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
The 2 x 2 table • You’ll use this a lot later in life…
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…
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…
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?
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?
What next? • Order more tests? • Schedule for surgery? • Prescribe medication, therapy, hamburgers…? • 1st, let’s see what the tests are really telling us.
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?
TSH 2 x 2 table • Complete the table and calculate the PPV and NPV assuming: sens = 92%, spec = 94% and prevalence = 4%
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?
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?
Free T4 table • Complete the table and calculate the PPV and NPV assuming: sens = 82%, spec = 94% and prevalence = 4%
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?
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?
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.
The FNAB 2 x 2 table • What do we know? • Prevalence – 4% • False positive rate – 20% • False negative rate – 3%
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
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?
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.
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.
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?
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
The Ct 2 x 2 table - completed • For the general population (0.35%) we find: • PPV = 33/233 = 14% • NPV = 9765/9767 = 100%
The Ct 2 x 2 table - completed • For all women (0.52%) we find: • PPV = 49/248 = 20% • NPV = 9749/9752 = 100%
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%
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…
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.
Questions or comments?? Contact info: Wiley D. Jenkins, PhD, MPH firstname.lastname@example.org 217-545-8717