Diagnostic by alicejenny

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									Diagnostic test


    Wenjie Yang

   ywjie@zzu.edu.cn

       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?
  – 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...
“ 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
Clients                                     -
                               Diagnostic       Negative(c)
                                  test
                                            +    False
                      Non-                       Positive(b)
                     patient
                                            -   True
                                                Negative(d)




   Assessing the Validity of Diagnostic Tests
The 2x2 Table describes test outcomes:

                    Disease        Disease
                    present        absent

      Positive     Group (a)        Group (b)
      result     True Positive    False Positive


      Negative     Group (c)        Group (d)
      result     False Negative   True Negative
1) Sensitivity: proportion of those with disease
who test positive
                           (a)
                        (a) + (c)
             Disease            Disease
             present            absent


 Positive    Group (a)         Group (b)
            True Positive    False Positive
 result

 Negative     Group (c)        Group (d)
 result     False Negative   True Negative
2) Specificity: proportion of those without disease
who test negative
                         (d)
                      (b) + (d)
            Disease            Disease
            present            absent

 Positive      Group (a)      Group (b)
             True Positive   False Positive
 result

 Negative     Group (c)        Group (d)
 result     False Negative   True Negative
The Ideal Situation--100% Agreement
               Disease          Disease
               present          absent
               n = 200          n = 800


   Positive       200                0
   result     True positive    False positive



   Negative         0              800
   result     False negative   True negative
A More Likely Outcome
              Disease         Disease
              present         absent
              n = 200         n = 800


Positive        170              30
result      True Positive   False Positive


Negative         30              770
result     False Negative   True Negative
• 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
Changing a Diagnostic Cut Point
           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
• 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
 Definition:

   The probability of disease, given the results of a test.
      Characteristics of Screening Tests
Positive Predictive Value (PPV):
The likelihood that a positive test result indicates the
 existence of the disease
        (a)
      (a) + (b)       Disease        Disease
                      present           absent

Positive result       Group (a)        Group (b)
                     True Positive    False Positive

                       Group (c)        Group (d)
   Negative result   False Negative   True Negative
Negative Predictive Value (NPV):
The likelihood that a negative test result indicates
 the absence of the disease
        (d)
                    Disease       Disease
     (c) + (d)      present       absent

    Positive result      Group (a)       Group (b)
                       True Positive   False Positive

Negative result         Group (c)        Group (d)
                      False Negative   True Negative
     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:
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
Sensitivity = 99%; Specificity = 95%

Prevalence = 1%    Disease Yes   Disease No   PPV
 Positive result   99            495
 Negative result   1             9405
Total              100           9900         17%
Prevalence = 5%
 Positive result   495           475
 Negative result   5             9025
Total              500           9500         51%
4. Multiple Test
 Parallel testing
   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          +
           _
 Serial testing

 Test A and test B and test C is positive
 Test A or test B or test C are negative

 A         +       B       +     C           +
       -               -                 -

     Sensitivity           Specificity
  Effect of parallel and serial testing on sensitivity,
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
Patient         +        +      70
                +         -     15
                -        +      10
                -        -       5
Non-patient    +         +      10
                +         -     15
                -         +      20
                -         -      55

								
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