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Diagnostic test. Wenjie Yang [email protected] 2009.12. 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?

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Diagnostic test
Diagnostic test

Wenjie Yang

[email protected]

2009.12


Questions
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
Test-Treatment Threshold

Post-test probability



1 diagnostic index
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...


“ Gold Standard”

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


Diabetes

OGTT (oral glucose tolerance test)

blood sugar test

urine sugar test


Gold standard

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
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
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



Uses of sensitive 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
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.



3 predictive value
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



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
Predictive Values and Prevalence (prevalence)

Sensitivity = 99%; Specificity = 95%


4 multiple test
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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