Diagnosis: EBM Approach Michael Brown MD Grand Rapids MERC/ Michigan State University.

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Diagnosis: EBM Approach Michael Brown MD Grand Rapids MERC/ Michigan State University

Transcript of Diagnosis: EBM Approach Michael Brown MD Grand Rapids MERC/ Michigan State University.

Page 1: Diagnosis: EBM Approach Michael Brown MD Grand Rapids MERC/ Michigan State University.

Diagnosis: EBM Approach

Michael Brown MD

Grand Rapids MERC/ Michigan State

University

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Scenario

1 day colicky pain with nausea diffuse to RLQ mild tenderness, T 37

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Step 1: Clinical Question

In the patient presenting to the ED with suspected appendicitis, what is the accuracy of helical CT ?

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Step 2: Search

MeSH Browser– appendicitis AND– computerized tomography AND– sensitivity and specificity

Clinical Query– diagnosis

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Step 3: Critical Appraisal

Internal Validity Results (focus today)

– even if critical appraisal not your bag

External Validity

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Evidenced-based Medicine

stresses methodology de-emphasizes statistics simplify: NNT, LR

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Likelihood Ratio: How to use

How to calculate

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Examples

Appendicitis Pulmonary embolism

– JAMA series– current slant

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Threshold Approach to Clinical Decision Making

Treatment threshold– if above, start therapy

Test threshold– if below, no further testing

Pauker NEJM 1980

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Diagnostic Testing

Treatment threshold for PE?– If above: heparin

Test threshold for PE?– If below: discharge home

If between?– Further testing

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Test/Treatment Threshold

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prior probability

post-test probLR

(prevalence) (predictive value)

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Test/Treatment Threshold

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Pretest Probability

experience in your setting– patient population

prevalence of condition in literature– Oxford web site

scoring systems

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Estimate Pretest Probability for PE

history risk factors physical exam initial screening tests

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Estimate Pretest Probability for PE

Not exact science– usually a range 40-60%– low, intermediate, high

done daily in clinical practice clinical prediction rules

– physicians estimate very close

Wicki 2001

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Bayesian Analysis?

Thomas Bayes 1702-1761 English clergyman Doctrine of Chances

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Bayesian Analysis

pretest probability– prevalence

LR for diagnostic test result post-test probability

– predictive value

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Interpretation

convert pretest prob to odds odds x LR = post-test odds convert odds back to prob

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Interpretation

convert pretest prob to odds odds x LR = post-test odds convert odds back to prob

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Fagan Nomogram

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Effect on pretest probability:

>10 or <0.1 large changes 5-10 and 0.1-0.2 moderate approach 1 no effect

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Advantages of LR:

combines sensitivity and specificity interpret test result on individual patient multiple cut-offs sequential testing

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Shortcut: LR for + test =

sensitivity

1 - specificity

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Calculate: LR=

prob (test result) with disease

prob (test result) without disease

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2 x 2 Table

Target Disorder Totals

Present Absent

DiagnosticTest Result

Positive a b a+b

Negative c d c+d

Totals a+c b+d a+b+c+d

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CT and Appendicitis

Target Disorder Totals

Present Absent

DiagnosticTest Result

Positive 29 4 33

Negative 1 66 67

Totals 30 70 100

Funaki et al

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CT and Appendicitis

probability of + CT with appendicitis

29/30 = .97

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CT and Appendicitis

Target Disorder Totals

Present Absent

DiagnosticTest Result

Positive 29 4 33

Negative 1 66 67

Totals 30 70 100

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CT and Appendicitis

Likelihood of + CT with appendicitis

29/30 = .97 Likelihood of +CT without appendicitis

4/70 = .057 LR for + CT = 17

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Effect on pretest probability:

>10 or <0.1 large changes 5-10 and 0.1-0.2 moderate approach 1 no effect

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Scenario

1 day colicky pain with nausea diffuse to RLQ mild tenderness, T 37

Pretest probability 30% – range 20 - 40%

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Helical CT

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Helical CT

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Helical CT

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Effect on pretest probability:

>10 or <0.1 large changes 5-10 and 0.1-0.2 moderate approach 1 no effect

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Advantages of LR:

combines sensitivity and specificity interpret test result on individual patient multiple cut-offs

– don’t have to lump!

sequential testing

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Multiple cut-offs

appendicitis No disease Total

CT positive

CT equivocal a b

CT negative

Total x y

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Calculate: LR=

prob equivocal CT with disease

prob equivocal CT without disease

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Multiple cut-offs

appendicitis No disease Total

CT positive

CT equivocal a b

CT negative

Total x y

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Multiple cut-offs: V/Q scan

normal low prob intermediate prob high prob

LR 0.1

LR 0.4

LR 1

LR 18

JAMA series

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Sequential Testing

post-test probability 1st test new pretest probability for 2nd test assume independence

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Helical CT : Diagnosis of PE

CT +

CT -

LR 8

LR .2

Rathbun, 2000

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ELISA D-dimer: Diagnosis of PE

> 500

<500

LR 2

LR .1

Brown, Bermingham 2001

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Helical CT

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D-dimer

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Test/Treatment Threshold

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Questions?

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Scenario

1 day colicky pain with nausea diffuse to RLQ mild tenderness, T 37

Pretest probability 30%

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Discussion: CT and appy

Internal Validity Results External Validity

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CT and Appendicitis

Flaws?– Minor– Major– Fatal

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CT and Appendicitis (Rao)

Target Disorder

Totals

Present Absent

Diagnostic Test Result

Positive

52 1 53

Negative

1 46 47

Totals 53 47 100

Rao et al

+LR = .98/.02 = 46-LR = .019/.98= .02

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CT and Appendicitis (Funaki)

Target Disorder Totals

Present Absent

DiagnosticTest Result

Positive 29 4 33

Negative 1 66 67

Totals 30 70 100

Funaki et al

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CT and Appendicitis

Likelihood of + CT with appendicitis

29/30 = .97 Likelihood of +CT without appendicitis

4/70 = .057 LR for + CT = 17

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Funaki: CT and Appendicitis

LR for a positive CT 17 LR for a negative CT 0.03

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CT and Appendicitis

Likelihood of + CT with appendicitis

29/30 = .97 Likelihood of +CT without appendicitis

4/70 = .057 LR for + CT = 17

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Helical CT

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Sources of LR:

PE: + CT LR = 8 - CT LR = .2

– meta-analysis: Rathbun et al Pharyngitis: neg rapid strep

- LR = 0.2– hosptial data

Alcoholism: CAGE >3 LR = 250

– web

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Combinations (LRxLR)

D-dimer <500, CT - LR .02