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Pocket Guide to Diagnostic Tests-0838581358.pdf

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16 <strong>Pocket</strong> <strong>Guide</strong> <strong>to</strong> <strong>Diagnostic</strong> <strong>Tests</strong><br />

3<br />

2<br />

If the CKMB test is negative (LR − = 0.05), then the posttest odds of<br />

having a myocardial infarction are<br />

3<br />

2<br />

× 32 =<br />

96<br />

2<br />

015 . ⎛ 0152 . 015<br />

× 005=<br />

7<br />

2 0152+ 1<br />

= .<br />

⎜<br />

⎝<br />

015+ 2<br />

=<br />

⎞<br />

. odds % probability⎟<br />

. .<br />

⎠<br />

Sequential Testing<br />

⎛ 48 1 48<br />

48 1 ⎜<br />

96<br />

⎝ 48 1 + 1<br />

= 48 + 1<br />

=<br />

⎞<br />

or odds % probability⎟<br />

⎠<br />

To this point, the impact of only one test on the probability of disease<br />

has been discussed, whereas during most diagnostic workups, clinicians<br />

obtain clinical information in a sequential fashion. To calculate the<br />

posttest odds after three tests, for example, the clinician might estimate<br />

the pretest odds and use the appropriate likelihood ratio for each test:<br />

Pretest odds × LR1 × LR2 × LR3<br />

= Posttest odds<br />

When using this approach, however, the clinician should be aware of a<br />

major assumption: the chosen tests or findings must be conditionally<br />

independent. For instance, with liver cell damage, the aspartate aminotransferase<br />

(AST) and alanine aminotransferase (ALT) enzymes may<br />

be released by the same process and are thus not conditionally independent.<br />

If conditionally dependent tests are used in this sequential<br />

approach, an overestimation of posttest probability will result.<br />

Threshold Approach <strong>to</strong> Decision Making<br />

A key aspect of medical decision making is the selection of a treatment<br />

threshold, ie, the probability of disease at which treatment is indicated.<br />

Figure 1–9 shows a possible way of identifying a treatment threshold by<br />

considering the value (utility) of the four possible outcomes of the<br />

treat/don’t treat decision.<br />

A diagnostic test is useful only if it shifts the disease probability<br />

across the treatment threshold. For example, a clinician might decide<br />

<strong>to</strong> treat with antibiotics if the probability of strep<strong>to</strong>coccal pharyngitis<br />

in a patient with a sore throat is greater than 25% (Figure 1–10A). If,<br />

after reviewing evidence from the his<strong>to</strong>ry and physical examination,<br />

the clinician estimates the pretest probability of strep throat <strong>to</strong> be 15%,<br />

then a diagnostic test such as throat culture (LR + = 7) would be useful<br />

only if a positive test would shift the posttest probability above 25%.<br />

Use of the nomogram shown in Figure 1–7 indicates that the posttest

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