Transcript 20090424_diagnosis

2nd Annual Resident
Evidence-Based Medicine Workshop
Diagnosis
Jeffrey P Schaefer MSc MD FRCPC
April 24, 2009
Objectives
• Diagnostics
– General Issues
– Gold Standard
– Test Characteristics
– Critical Appraisal
Medical Diagnostics
history
physical
examination
diagnostic
tests
Medical Diagnostics
general process
history
physical
examination
diagnostic
tests
Medical Diagnostics
general process
history
physical
examination
diagnostic
tests
Diagnostic Testing
• Advantages
– can assess parameters beyond the 5 senses
– can be more ‘objective’ than clinical data
• Disadvantages
– test results can be incorrect
– test results may lead you in the wrong direction
– tests cost money
– tests may confer risk
– some diseases have no diagnostic test
Issues in Diagnostic Testing
• Risk
– urine sample versus brain biopsy versus autopsy
• Cost
– glucoscan strip ~ $1.00 versus MRI $1,000.00
• Availability
– hemogram versus Positive Emission Tomogram
• Patient Acceptability
– urine sample versus 3 day fecal fat collection
Clinical Scenario
• A 70 year old man presents to the ED
complaining chest pain and shortness of
breath for one hour.
• He has prostate cancer and just completed
a car trip from Vancouver.
• He’s on no medications nor has allergies.
• He is distressed. Pulse is 130 / min,
respiratory rate is 32 / min. There are no
other physical findings.
What’s the clinical question?
What’s the
DIAGNOSIS?
What are some causes for this
man’s presentation?
• We need an approach...
Sidebar: Approach to Diagnosis
• JP Schaefer’s classification scheme….
– 7 approaches
1. Epidemiological Approach
• What’s common in this clinical setting?
– Right Lower Quadrant Pain: 18 year old male
appendicitis
– Right Lower Quadrant Pain: 80 year old
cecal carcinoma
2. Physiological Approach
• What pathophysiology is causing the
condition?
• Hypoxemia
– shunt
– v/q mismatch
– alveolar hypoventilation
– decreased pAO2
– increased diffusion gradient
– (low mixed venous oxygen pressure)
3. Anatomical Approach
• Where is the problem?
• Chest Pain
– skin: shingles
– muscle: strain / injury / nail gun
– rib / spine: fracture / tumor
– lung / pleura: pneumonia, embolism
– heart / pericardium: angina / pericarditis
– esophagus: spasm / tumor
4. Pathological Approach
• What pathology is involved?
• Left sided weakness
– brain tumor (slow)
– stroke (fast)
– multiple sclerosis (recurrent)
5. Pattern Recognition
• What’s this?
– have you seen this before? (Herpes simplex
labialis secondary to HSV Type 1 in an
immunocompromised patient)
6. Interventional Efficacy
• Headache
– muscle tension
– migraine
– meningioma
– warning bleed of SAH
7. No approach
• Common problem
– used to be my method!
Back to the case...
• DDx chest pain and shortness of breath…
– epidemiology
–anatomy
– physiology
– pathology
– pattern recognition
– interventional efficacy
Test for Pulmonary Embolism
• Gold Standard: pulmonary angiogram
– invasive
– costly
– not readily available
– risky
• Other tests:
– D-dimer, V/Q scans, Spiral CT scan
– ? may be helpful in right setting with right results
- complex
PE - diagnosis
Pulmonary angiogram
- gold standard
PE - diagnosis (spiral CT scan)
PE - diagnosis (V/Q scan)
• high probability V/Q scan (2 defects)
Pulmonary Thromboembolism
How well does the test perform?
• Welcome to the world of
TEST CHARACTERISTICS
Take a deep breath...
Test Characteristics
• Sensitivity
• Specificity
• Positive predictive value
• Negative predictive value
• Accuracy
• Positive Likelihood ratio
• Negative Likelihood ratio
DISEASE
Present
Absent
TRUE
FALSE
Positive
TEST
POSITIVE POSITIVE
FALSE
TRUE
Negative
NEGATIVE NEGATIVE
Hypothetical Test Results
DISEASE (PE)
Present
Absent
Positive
TRUE
POSITIVE
a = 80
FALSE
POSITIVE
b = 20
a + b = 100
Negative
FALSE
NEGATIVE
c = 10
TRUE
NEGATIVE
d = 90
c + d = 100
a + c = 90
b + d = 110
a+b+c+d = 200
TEST
(V/Q
scan)
Sensitivity
• Probability that test is positive given that
disease is present.
P (T+ | D+)
Sensitivity
DISEASE (PE)
Present
Absent
Positive
TRUE
POSITIVE
a = 80
FALSE
POSITIVE
b = 20
a + b = 100
Negative
FALSE
NEGATIVE
c = 10
TRUE
NEGATIVE
d = 90
c + d = 100
a + c = 90
b + d = 110
a+b+c+d = 200
TEST
(V/Q
scan)
80 / (80 + 10) = 88.9%
Specificity
• Probability that test is negative given that
disease is absent.
P (T- | D-)
Specificity
DISEASE (PE)
Present
Absent
Positive
TRUE
POSITIVE
a = 80
FALSE
POSITIVE
b = 20
a + b = 100
Negative
FALSE
NEGATIVE
c = 10
TRUE
NEGATIVE
d = 90
c + d = 100
a + c = 90
b + d = 110
a+b+c+d = 200
TEST
(V/Q
scan)
90 / (90 + 20) = 81.8%
Sensitivity - Specificity Trade-Off
• Most test results are not positive or negative.
• There is often a selected value
– over which a test is said to be positive
– under which a test is said to be negative.
• As a result….
– increasing sensitivity results in loss of specificity
– increasing specificity results in loss of sensitivity
Sensitivity / Specificity Trade-off
Sensitivity Decreases
Specificity Increases
Sensitivity / Specificity Trade-off
• Receiver Operating Characteristic (ROC) curve
Test Characteristic Issues
• Highly Sensitive Tests:
– tend to be less invasive, less risky, less costly
– best for screening programs
– best for ruling out disease: “SNOUT”
Test Characteristic Issues
• Highly Specific Tests:
– tend to be more invasive, more risky, more costly
– best for confirming (ruling in) disease: “SPIN”
Positive Predictive Value
• Probability that disease is present given
that the test was positive.
P (D+ | T+)
Positive Predictive Value
DISEASE (PE)
Present
Absent
Positive
TRUE
POSITIVE
a = 80
FALSE
POSITIVE
b = 20
a + b = 100
Negative
FALSE
NEGATIVE
c = 10
TRUE
NEGATIVE
d = 90
c + d = 100
TEST
(V/Q
scan)
80a /+ c(80
80.0%
= 90 + 20)
b + d=
= 110
a+b+c+d = 200
Negative Predictive Value
• Probability that disease is absent given that
the test was negative.
P (D- | T-)
Negative Predictive Value
DISEASE (PE)
Present
Absent
Positive
TRUE
POSITIVE
a = 80
FALSE
POSITIVE
b = 20
a + b = 100
Negative
FALSE
NEGATIVE
c = 10
TRUE
NEGATIVE
d = 90
c + d = 100
a + c = 90
b + d = 110
a+b+c+d = 200
TEST
(V/Q
scan)
90 / (90 + 10) = 90.0%
Test Characteristic Issues
• Positive and Negative Predictive Values
suffer from depending on disease
prevalence
• This is a major drawback.*
(* excellent exam question)
Change Disease Prevalence from 90 to 110 per 200
DISEASE (PE)
Present
Absent
prevalence = 110 / 200
= 0.55 = FALSE
55% (was 45%)
TRUE
POSITIVE POSITIVE a + b =
Positive
sensitivity = 97.7 / 110
88.8% (unchanged)
a ==80
b = 20
114.1
TEST
16.4
specificity
= 73.6 / 9097.7
= 81.7% (unchanged)
(V/Q
FALSE
TRUE
scan)
NEGATIVE c + d =
positiveNegative
predictiveNEGATIVE
value = 86.5%
(was 80%)
c = 10
d = 90
85.8
negative predictive value
(was 90%)
12.2 = 85.8%73.6
a + c = 90
b+d=
a+b+c+d
110
110 90
= 200
Accuracy
• Probability that the test is true.
• (not a useful concept as you’ll see later)
Accuracy
DISEASE (PE)
Present
Absent
Positive
TRUE
POSITIVE
a = 80
FALSE
POSITIVE
b = 20
a + b = 100
Negative
FALSE
NEGATIVE
c = 10
TRUE
NEGATIVE
d = 90
c + d = 100
a + c = 90
b + d = 110
a+b+c+d = 200
TEST
(V/Q
scan)
(80+90) / (80+ 20 + 10 + 90) = 85.0%
Test Characteristic Issues
• Accuracy:
– not useful characteristic
– high sensitivity / low specificity test may have
same accuracy as low sensitivity / high specifity
test
(positive) Likelihood Ratio
• Ratio of:
probability of positive test when disease is present
-------------------------------------------------------------------probability of positive test when disease is absent
Positive Likelihood Ratio
DISEASE (PE)
Present
Absent
Positive
TRUE
POSITIVE
a = 80
FALSE
POSITIVE
b = 20
a + b = 100
Negative
FALSE
NEGATIVE
c = 10
TRUE
NEGATIVE
d = 90
c + d = 100
a + c = 90
b + d = 110
a+b+c+d = 200
TEST
(V/Q
scan)
(80 / 90) / (20 / 110) =
4.89
Utility of (Positive) Likelihood Ratios
• expresses how many times more likely a
test result is to be found in diseased,
compared to nondiseased, people.
• can estimate the post-test probability of
disease if prevalence is known.
Pre-test Probability of Disease
• Consider: a female presents for a screening
breast mammogram for breast cancer.
• What’s her pre-test probability of disease?
Prevalence of Disease
Positive Test Result
• Say that her mammogram show her to have
a 1 cm spiculated calcification
• Say that this finding is associated with a
likelihood ratio of 20 (a very suspicious
lesion).
Highly suspicious lesion
What is the post-test probability of
disease?
Answer:
Pretest odds x Likelihood Ratio = Posttest odds
(the use of odds ratios makes the math convoluted)
What is the post-test probability of disease?
Pretest odds x Likelihood Ratio = Posttest odds
Assume: prevalence = 10 / 1000 = 1% = P(0.01)
Odds = probability of event / (1 - probability of event)
Pre-test Odds = (10/1000) / (1 - (10/1000)) = 0.0101
What is the post-test probability of
disease?
Pretest odds x Likelihood Ratio = Posttest odds
0.0101 x 20 = 0.2020
Probability = Odds / (1 + Odds)
Posttest Probability = 0.2020 / (1 + 0.2020)
Posttest Probability = 0.167 = 16.7%
Utility of (Positive) Likelihood Ratio
Pre-test Probability = 1%
Post-test Probability = 16.7%
Prudent Course: move from screening test
to confirmatory test!
Critical Appraisal
Articles about Diagnosis
• Are the results in the study valid?
• What are the results?
• Will the results help care for my patients?
Validity
1. Was there an independent, blind comparison
with a reference standard?
2. Did the patient sample include an appropriate
spectrum of patients to whom the diagnostic test
will be applied in clinical practice?
3. Did the results of the test being evaluated
influence the decision to perform the reference
standard?
4. Were the methods for performing the test
described in sufficient detail to permit
replication?
Results
• Are likelihood ratios for the test results
presented or data necessary for their
calculation provided?
How much do Likelihood Ratios (LRs)
change disease likelihood?
LRs >10 or <0.1 cause large changes in likelihood.
LRs 5-10 or 0.1-0.2 cause moderate changes.
LRs 2-5 or 0.2-0.5 cause small changes.
LRs between <2 and 0.5 cause little or no change.
Applicability
• Will the reproducibility of the test result and
its interpretation be satisfactory in my
setting?
• Are the results applicable to my patient?
• Will the results change my management?
• Will patients be better off as a result of the
test?
Volume 292 January 2, 1975 Number 1
•
Immunoblastic lymphadenopathy. A hyperimmune entity resembling
Hodgkin's disease
•
Immunoblastic lymphadenopathy with mixed cryoglobulinemia. A
detailed case study
•
Vinyl-chloride-induced liver disease. From idiopathic portal
hypertension (Banti's syndrome) to Angiosarcomas
•
Hodgkin's Disease, tonsillectomy and family size
•
Reduction of ischemic injury by nitroglycerin during acute myocardial
infarction (no abstract available)
•
Frederick Stohlman, Jr., M.D
TREATMENT EFFECT