Diagnostic Cases

Goals & Objectives

• Highlight Bayesian and Boolean processes used in classic diagnosis

• Demonstrate use/misuse of tests for screening vs. diagnosis

• Have fun while learning about some common clinical questions

Seven standards for Tests

• Spectrum compositionage distribution, sex distribution, presenting clinical symptoms and/or disease stage, and

eligibility criteria for study subjects.

• Pertinent subgroups• Avoidance of workup bias• Avoidance of review bias• Precision of results for test accuracy• Presentation of indeterminate test

results• Test reproducibility

From Bandiolier http://www.jr2.ox.ac.uk/ban

dolier/band26/b26-2.html

Out of total= 7 standards recommended

Year of article publication

Case #1

Strep Throat

The cases: Estimate thepretestprobability ofstrep throat(using the Palmtool), in thespace below:

What is the posttestprobability if you have aPOSTIVE strep antigentest? A NEGATIVE strepantigen test?

Indicate in the spacebelow: would you Test,Treat w/o testing, or Wait(no test, no treat)?

Pos=93%A 9 year old boy with fever 103F,whitish exudate on tonsillarpillars, tender anterior necknodes, and a classicscarletinaform rash all over hisbody. He has no cough.

51%

Neg=14%

Consider treatingwithout testing, as youpretest probability is sohigh, and he has otherfindings that are classic.

Pos=11%A 16 year old girl with temp of99F, hx of 1 day of pain onswallowing and some cough;exam shows only mildly redpost. pharynx

1%

Neg=0%

Treat as a viral illness.Consider Test only ifparent/patient are"streptophobic".

Pos=Don't dothe test

A classmate of the 9 year oldpatient who has no complaint butMom is concerned because he“slept over” with him lastweekend…

5 to 15%streppresencedue to"carrier"

Neg=Don't dothe test

Instruct mother towatch and wait forsymptoms.

Pos=57%A 50 year old teacher, with atemperature of 101F and nocough. Her exam shows swollenlymph nodes.

10%

Neg=2%

Test. Here the test maymake a big difference.

Bayesian Graph: Post-test probability as function of test result and pre-test probability

0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.800

0.900

1.000

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1

Pre-test Probability

Post

-test

Pro

bability

Probability given Positive Test Probability given Negative Test If no test

The 9 year old, if he had NO rash, would get most benefit from testing.

The teacher is benefited mostly by a positive test.

How do you tell a “carrier” state from a disease causing strep?

A Bayes Rule of Thumb: Tests work best when Pretest Probability is 50:50

15/400 individuals= 3.75% disease prevalence

Disease No disease

a b a+bTest positive 12 4 16

c d c+dTest negative 3 381 384

a+c b+da+b+c+d15 385 400

Sensitivity a/(a+c) 0.8000Specificity d/(b+d) 0.9896Positive Pred Value a/(a+b) 0.7500Negative Pred Value d/(c+d) 0.9922

Disease No disease

a b a+bTest positive 12 4 16

c d c+dTest negative 3 381 384

a+c b+da+b+c+d15 385 400

Screening Principles

• Is the problem serious, and do patients care about it?• Is the screening test accurate?

• Is the “gold standard” comparison reliable?• Is the positive predictive value acceptable?• Does early detection of the disease improve outcomes?• Is screening or treatment benign (i.e. not harmful)?• Does screening do more good than harm?• In a world of limited resources, is screening cost effective?

• Absolutely effective compared to natural hx of disease?• Relatively effective compared to using resources to find/treat other problems?

Depression CaseChief Complaint

Sadie Blue is a 22 year old female. Her chief complaint is “no energy".

History of Present Illness

She reported: enjoys interaction with opposite sex none of the time | depressed most of the time | feel best in morning some of the time | normal thinking none of the time | full life some of the time | irritable most of the time | decisive none of the time | restless a good part of the time | hopeful none of the time | useful none of the time | crying spells a good part of the time | enjoying activities none of the time.

She denied: suicidal ideation some of the time.

Past, Family, and Social History

Social History

Activities for Daily Living

History of: normal activities none of the time.

Review of Systems

Constitutional

She reported: eating as much as before some of the time | weight loss a good part of the time | fatigue most of the time.

Cardiovascular

She denied: palpitations some of the time.

Gastrointestinal

She reported: constipation a good part of the time.

Neurological

She reported: dyssomnia most of the time.

Self-assessment Scales

Title: Zung Depression Scale

Description: This 14-item scale for depression is a classic in self-rating scales. William Zung at Duke University published this early scale for patient use in 1965. Valued for its brevity, it remains a useful screening tool for depression.

Patient Score: 65 - Moderate to Marked

Scoring Key and Interpretation:

0 - 50 : Normal

51 - 60 : Minimal to Mild

61 - 69 : Moderate to Marked

70 - 999 : Severe to Extreme

Reference: Zung, W.W.K.: A self-rating depression scale. Archives of General Psychiatry, 1965; 12:63-70.

What does this mean?

Title: Zung Depression Scale

Description: This 14-item scale for depression is a classic in self-rating scales. William Zung at Duke University published this early scale for patient use in 1965. Valued for its brevity, it remains a useful screening tool for depression.

Patient Score: 65 - Moderate to Marked

Scoring Key and Interpretation:

0 - 50 : Normal

51 - 60 : Minimal to Mild

61 - 69 : Moderate to Marked

70 - 999 : Severe to Extreme

Reference: Zung, W.W.K.: A self-rating depression scale. Archives of General Psychiatry, 1965; 12:63-70.

Depression Screening

1) What is the predictive value of this positiveZung screening test?

21.4%

2) What is the negative predictive value of thisZung screening test?

98.7%

3) For every 1000 patients who are screened,how many truly depressed patients will be found?

61(for NNS of 16)

4) In that same 1000 patients, how many will bedetermined to be "false positives" after psychiatricinterview?

223

5) Finally, how many of depressed patients out of1000 will be missed with the Zung?

9

Mammography & CAD

My wife recently had a mammogram. She came home and said, "They asked me if I wanted to pay $25 more to have a computer help read my mammogram. I told them 'No, that's the doctors job!'. Was that the right thing to do?"

1) What is the gold standard weshould use to determine theeffectiveness of plain mammographyand CAD as a screening tool? Howwould you design that study?

Ideally, death from Breast cancer.Secondarily, path dx of breast cancerin cohort followed over many years.Prospective DBRCT of CAD vs plainmammography, over 5 years.

2) Why is looking at biopsy outcomesinsufficient to really evaluate this toolas a cancer screen.?

Patients whose lesions are missed bymammography are not referred tobiopsy. This makes Specificity seemhigher than it really is. (Spec->100%when none missed).

33%

6.4%

3) Since biopsy outcomes are all wehave, look at the 2x2 tables we canconstruct from this data. What is thechance that a woman recommendedto have a biopsy will have cancer? 39.5%

Mammography & CAD

Radiologist Alone

CAD Alone

Combined R+CAD

Mammography & CAD

4) How many additional cancers willbe found by adding CAD per 1000women screened?

0.6220,or 1 per 1607 women

or Sensitivity increases from 85% to100%?? (does it?)

or a 19% increase in # cancers found

5) What is the total cost to find thatadditional Cancer? (watch out= trickquestion!)

CAD cost alone is $25 x 1607= $40, 187.5+ 21 more biopsies x $500=$10,500

Total= $56,687.50

Increase in "callback rate" from 6.5% to7.7% = 154 MORE patients called back.

So additional costs of extra films, losttime, pain and anxiety, etc.

Figure 1. ROC curves and sensitivity and specificity data obtained from the interpretation of 104 mammograms by 10 radiologists. A cluster of microcalcifications was present in all cases; 46 cancers and 58 benign lesions were confirmed at biopsy. The effect of a computer aid was tested; it provided an estimate of the likelihood that microcalcifications were due to a malignancy. Sensitivity and specificity results were based on the radiologists’ recommendations for biopsy or follow-up. The ROC curves were based on the radiologists’ diagnostic confidence.

Summary• For uncommon illnesses (screening, like breast cancer) there

will be lots of false positives.• Apply the test correctly, to the correct population• “Clinical judgment” means you figure out which population the

patient belongs to, before applying the test (i.e. good pretest probability)

• Good tools for pretest probability are hard to find: use the ones we have well!

• Watch out for back end costs- complications and death from testing, anaphylaxis from antibiotics, social stigma from psych diagnoses, etc.

Reference

• How to Read a Paper: Papers that Report Diagnostic or Screening Tests. BMJ 1997: 315: 540-543 (August 30).

• Available on Internet, full text.