diagnostic cases. goals & objectives highlight bayesian and boolean processes used in classic...
TRANSCRIPT
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
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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.