Transcript Diagnostic Cases - Cedar Rapids Medical Education Foundation
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 composition age 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
Out of total= 7 standards recommended Year of article publication From Bandiolier http://www.jr2.ox.ac.uk/ban dolier/band26/b26-2.html
Case #1
Strep Throat
The cases: Estimate the pretest probability of strep throat (using the Palm tool), in the space below:
A 9 year old boy with fever 103F, whitish exudate on tonsillar pillars, tender anterior neck nodes, and a classic scarletinaform rash all over his body. He has no cough.
51%
What is the posttest probability if you have a POSTIVE strep antigen test? A NEGATIVE strep antigen test?
Indicate in the space below: would you Test, Treat w/o testing, or Wait (no test, no treat)?
Pos=93% Neg=14% Consider treating without testing, as you pretest probability is so high, and he has other findings that are classic.
A 16 year old girl with temp of 99F, hx of 1 day of pain on swallowing and some cough; exam shows only mildly red post. pharynx 1% A classmate of the 9 year old patient who has no complaint but Mom is concerned because he “slept over” with him last weekend… 5 to 15% strep presence due to "carrier" A 50 year old teacher, with a temperature of 101F and no cough. Her exam shows swollen lymph nodes.
10% Pos=11% Neg=0% Pos=Don't do the test Neg=Don't do the test Pos=57% Neg=2% Treat as a viral illness.
Consider Test only if parent/patient are "streptophobic".
Instruct mother to watch and wait for symptoms.
Test. Here the test may make a big difference.
Bayesian Graph: Post-test probability as function of test result and pre-test probability
1.000
0.900
0.800
0.700
0.600
0.500
0.400
0.300
0.200
0.100
0.000
0 0.1
0.2
0.3
0.4
0.5
0.6
0.7
Pre-test Probability
0.8
0.9
1 1.1
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
Test positive Disease a 12 No disease b 4 a+b 16 Test negative c 3 a+c 15 d 381 c+d 384 b+da+b+c+d 385 400
Test positive Disease a 12 No disease b 4 a+b 16 Test negative c 3 a+c 15 d 381 c+d 384 b+da+b+c+d 385 400 Sensitivity Specificity a/(a+c) d/(b+d) Positive Pred Value a/(a+b) Negative Pred Value d/(c+d) 0.8000
0.9896
0.7500
0.9922
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 Case
Chief 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 positive
Zung screening test?
2)
What is the
negative predictive value
of this Zung screening test?
3) For every 1000 patients who are screened, how many truly depressed patients will be found?
4) In that same 1000 patients, how many will be determined to be "false positives" after psychiatric interview?
5) Finally, how many of depressed patients out of 1000 will be missed with the Zung?
21.4% 98.7% 61
(for NNS of 16)
223 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?"
Mammography & CAD 1) What is the gold standard we
should
use to determine the effectiveness of plain mammography and CAD as a screening tool? How would you design that study?
2) Why is looking at biopsy outcomes insufficient to really evaluate this tool as a cancer screen.?
3) Since biopsy outcomes are all we have, look at the 2x2 tables we can construct from this data. What is the chance that a woman recommended to have a biopsy will have cancer?
Ideally, death from Breast cancer.
Secondarily, path dx of breast cancer in cohort followed over many years.
Prospective DBRCT of CAD vs plain mammography, over 5 years.
Patients whose lesions are missed by mammography are not referred to biopsy. This makes Specificity seem higher than it really is. (Spec->100% when none missed).
Radiologist Alone
33%
CAD Alone
6.4%
Combined R+CAD
39.5%
Mammography & CAD 4) How many additional cancers will be found by adding CAD per 1000 women screened?
0.6220,
or
1 per 1607 women
or
Sensitivity increases from 85% to 100%?? (does it?)
or
a 19% increase in # cancers found 5) What is the total cost to find that additional Cancer? (watch out= trick question!) 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% to 7.7% = 154 MORE patients called back.
So additional costs of extra films, lost time, 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.