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MRCPsych Part B > Evaluating Diagnosis > Flashcards

Evaluating Diagnosis Flashcards

1
Q

What do studies of a diagnostic test usually evaluate the relationship between?

A

Being positive for standard diagnostic test vs being positive for evaluated screening test

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2
Q

What are the 4 groups in diagnostic test studies

A

True positive = A
False positive = B
False negative = C
True negative = D

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3
Q

First column for table for diagnostic test studies

A

Test positive
Test negative
Total

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4
Q

Second column

A

Disease (gold standard positive)
A
C
A+C

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5
Q

3rd column

A

No disease (gold standard -ve)
B
D
B+D

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6
Q

4th column

A

A+B
C+D
A+B+C+D

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7
Q

First row

A

Diseased, No disease

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8
Q

2nd row

A

Test positive, A, B, A+B

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9
Q

3rd row

A

Test negative, C, D, C+D

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10
Q

4th row

A

Total, A+C, B+D, A+B+C+D

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11
Q

Define sensitivity

A

Usefulness of a test for truly diseased population

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12
Q

Define specificity

A

Usefulness of a test of non-diseased population

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13
Q

Calculation for sensitivity

A

A/A+C

true positive/total diseased

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14
Q

Calculation for specificity

A

D/B+D

true negative/total non-diseased

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15
Q

Calculation for accuracy

A

A+D/A+B+C+D

true positive + true negative/total population

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16
Q

Characteristics of a highly sensitive test

A

Will pick up cases that show even slightest of evidence

Makes more false positives

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17
Q

When is a highly sensitive test preferred?

A

If diagnosis should not be missed but overdiagnosis is not harmful

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18
Q

Characteristics of highly specific test

A

Will pick up cases only if definitive evidence noted

Makes more false negatives

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19
Q

When is a highly specific test preferred?

A

If missing some cases is not bad but wrong labelling can be costly/harmful

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20
Q

Impact of higher cut off on specificity and sensitivity

A

Low sensitivity

High specificity

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21
Q

When is a receiver operator curve (ROC) useful?

A

Choosing between two diagnostic tests of different sensitivity and specificity
Choosing a cut off point for making a diagnosis

22
Q

X and Y axis of ROC curve

A

X: (1-specificity)
Y: sensitivity

23
Q

How do interpret a ROC curve

A

Elbow/knee of the curve is cut-off point with balance between sensitivity and specificity.
Among 2 curves, the one closer to the left upper corner is better screening test

24
Q

What is ROC analysis used for?

A

To select optimal cut off point

To dichotomize a continuous scale

25
Q

What does accuracy of ROC analysis depend on?

A

Quality of gold standard test

26
Q

Impact of increase in sensitivity on specificity?

A

Decrease in specificity

27
Q

How do we know if a test is not accurate in a ROC curve?

A

The closer the curve comes to the chance line

28
Q

What is the area under the ROC curve?

A

Accuracy

29
Q

What is Likelihood ratios for a positive test (LR+)?

A

Likelihood for testing positive rightly/wrong

30
Q

What is Likelihood ratio for a negative test (LR-)?

A

Likelihood of testing negatively rightly/wrongly

31
Q

Calculation for LR+

A

(A/A+C)/(B/B+D)

32
Q

Calculation for LR-

A

(C/A+C)/(D/B+D)

33
Q

Name another calculation for LR+

A

Sens/1-spec

34
Q

Name another calculation for LR-

A

1-sens/spec

35
Q

What is LR+ for most clinically available tests?

A

> 1

36
Q

What value of LR is used to use a diagnostic tool

A

> 10

37
Q

What LR value is used to avoid a diagnostic tool

A

<0.1

38
Q

What does likelihood of a diagnosis depend on?

A

Prevalence or prior probability of disease before applying the test

39
Q

What is the pretest probability

A

Prevalence in studied population

40
Q

Calculation for pretest probability

A

(A+C)/(A+B+C+D)

41
Q

How to calculate post-test probability

A

Convert pretest probability to prest odds

Convert pretest odds to post test odds

Convert posttest odds to post-test probability

42
Q

Calculation to convert pretest probability to pretest odds

A

Prob/1-prob

(A+C)/(B+D)

i.e. diseased/not diseased

43
Q

Calculation to convert pretest odds to post-test odds

A

LR x Pretest odds

44
Q

Calculation to convert post-test odds to post-test probability

A

Post-test odds / 1+ posttest odds

45
Q

What is the Bayesian nomogram to calculate post-test probability

A

Draw a straight line across pretest probability and likelihood ratio

Post-test probability s the value obtained on the other side of the nomogram

46
Q

Who created the Bayesian Nomogram

A

Fagan

47
Q

What does positive predictive value (PPV) tell us?

A

The chance of having the disease if test +ve

48
Q

Calculation for PPV

A

A/A+B

49
Q

What does PPV increase with

A

Prevalence

50
Q

What does the negative predictive value (NPV) tell us?

A

Chance of not having the disease if test -ve

51
Q

Calculation for NPV

A

D/C+D

52
Q

What happens to NPV as prevalence increases?

A

NPV decreases

MRCPsych Part B (64 decks)

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