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Presentation Transcript

Questions

- A patient presents to us with a chief complaint
- Why do we order tests?
- What tests to order? Based on what?
- What do we hope to achieve as we get the result of the test?
- What if there are multiple tests that are related to this complaint?
- What if we are considering 6 or 7 possible diagnoses that might explain this chief complaint?

Test-Treatment Threshold

Post-test probability

- Assessing the validity and reliability of
diagnostic tests?

- Choose diagnostic tests wisely
- Interpret the result of diagnostic tests

1. Diagnostic Index

Subjective Index

headache, dizzy, disgusting ...

Semi-subjective (or semi-objective)

hardness of liver, rale in lung...

Objective index

blood pressure value, blood sugar value, blood cells

counting...

the most accurate and reliable diagnostic method(s).

chest X-ray and sputum smear

--- pneumonia electrocardiogram (ECG) and serum enzyme

---acute myocardial infarction

tissue biopsy --- cancer

True

Positive(a)

＋

patient

False

Negative(c)

Clients

－

Diagnostic

test

False

Positive(b)

＋

Non-patient

－

True

Negative(d)

Assessing the Validity of Diagnostic Tests

The 2x2 Table describes test outcomes:

Disease

present

Disease

absent

Group (a)

True Positive

Group (b)

False Positive

Positive

result

Group (c)

False Negative

Group (d)

True Negative

Negative

result

- Sensitivity: proportion of those with disease
- who test positive
- (a)
- (a) + (c)

Disease

present

Disease

absent

Group (a)

True Positive

Group (b)

False Positive

Positive

result

Group (c)

False Negative

Group (d)

True Negative

Negative

result

2) Specificity: proportion of those without disease who test negative

(d)

(b) + (d)

Disease

absent

Disease

present

Group (a)

True Positive

Group (b)

False Positive

Positive

result

Group (c)

False Negative

Group (d)

True Negative

Negative

result

The Ideal Situation--100% Agreement

Disease

present

Disease

absent

n = 200

n = 800

200

True positive

0

False positive

Positive

result

0

False negative

800

True negative

Negative

result

A More Likely Outcome

Disease

present

Disease

absent

n = 200

n = 800

170

True Positive

30

False Positive

Positive

result

30

False Negative

770

True Negative

Negative

result

- Consequences of a False Positive
- Even 3-5% will be large on a population level
- Follow-up tests, cost, potential harm, anxiety

- Consequences of a False Negative
- Even one person can have tragic implications
- At best, a false sense of security
- Might neglect future tests

when there is an important penalty for missing a disease ;

when a great many possibilities are being considered, in order to reduce the number of possibilities;

when the probability of disease is relatively low and the purpose of the test is to discover disease.

Uses of specific tests

When to confirm a diagnosis that has been suggested by other tests.

When false positive results bring severe harm to the client physically, emotionally, or financially.

If a test result is positive, how likely is it that this individual has the disease?

3. Predictive value individual has the disease?

- Definition:
The probability of disease, given the results of a test.

Characteristics of Screening Tests

Positive Predictive Value (PPV): individual has the disease?

The likelihood that a positive test result indicates the existence of the disease

(a)

(a) + (b)

Disease

present

Disease

absent

Group (a)

True Positive

Group (b)

False Positive

Positive result

Group (c)

False Negative

Group (d)

True Negative

Negative result

Negative Predictive Value (NPV): individual has the disease?

The likelihood that a negative test result indicates the absence of the disease

(d)

(c) + (d)

Disease

present

Disease

absent

Group (a)

True Positive

Group (b)

False Positive

Positive result

Group (c)

False Negative

Group (d)

True Negative

Negative result

- The relationship between predictive value and Se, Sp, P (prevalence)
Se × P

+PV=

(Se × P)+(1-Sp) × (1-P)

(1-P) × Sp

- PV=

(1-P) × Sp + P × (1-Se)

Bayes’ theorem (prevalence):

As the prevalence of a disease increases, the positive predictive value of the test increases (PPV) and its negative predictive value (NPV) decreases.

Predictive Values and Prevalence (prevalence)

Sensitivity = 99%; Specificity = 95%

4. Multiple Test (prevalence)

- Parallel testing (prevalence)
Test A or test B or test C is positive

Test A and test B and test C are negative

A +

_ Sensitivity

B +

_ Specificity

C +

_

Test A and test B and test C is positive (prevalence)

Test A or test B or test C are negative

- Serial testing
A + B + C +

- - -

Sensitivity Specificity

Effect of parallel and serial testing on sensitivity, (prevalence)

specificity,and predictive value of test combinations

test Se (%) Sp (%) PPV(%) NPV(%)

A 80 60 33 92

B 90 90 69 97

A or B (parallel) 98 54 35 99

A and B (serial) 72 96 82 93

for 20%prevalence

Test A Test B (prevalence)

Patient + + 70

+ - 15

- + 10

- - 5

Non-patient + + 10

+ - 15

- + 20

- - 55

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