Pediatric Informatics: Computer Applications in Child Health (Health ...

162 M.J. Feldman
in patients without the disease. In a test of low specificity, many patients without
the disease will test positive.
The prevalence of a disease is the frequency with which it is found in a population.
Symptoms, signs and tests of high sensitivity are useful for ruling out a diagnosis
because a negative result suggests exclusion (low false negative rate). Those with
high specificity are useful for confirming a diagnosis because a positive result suggests
inclusion (low false positive rate). Clinicians initially use highly sensitive
symptoms, signs, and tests to screen for disease. Following screening, clinicians
will then use specific symptoms, signs, and tests to confirm the final diagnosis.
For diagnoses of high impact (morbidity, mortality, or significance), symptoms, signs,
and tests of both high sensitivity and specificity are needed. For diagnoses of high
impact but of low prevalence, high sensitivity is more important than specificity.
Newborn metabolic screening tests for congenital hypothyroidism are highly sensitive,
resulting in few missed (false negative) cases, with a lower specificity that results in a small
number of erroneously identified healthy (false positive) cases. High sensitivity is crucial
because early treatment is essential to prevent impairment, whereas misidentification of a
healthy newborn is corrected by confirmatory testing (of higher specificity). “Gold standard”
tests (highest positive predictive value (See next section) ) are used to confirm and
prevent harm from unnecessary interventions.
12.3 Diagnosis: Prevalence and Positive Predictive Value
How do clinicians diagnose? Several methods are likely incorporated, depending
on the complexity of the case. Pattern recognition is helpful in simple cases.
Deduction is useful in complex cases where a set of individual hypotheses about
a diagnosis is investigated, and the set is revised based on new information. 5
Knowledge of the prevalence of clinical findings and likely diseases both in the
general population and in the patient’s demographic is helpful in arriving at a correct
diagnosis, especially with common conditions. Knowledge of the probability
of a particular disease occurring in a patient with a given finding (positive predictive
value, or PPV) is of great importance. A clinician tacitly combining these
processes is applying Bayes’ Law. 6
In a medical context, Bayes’ Law asks:
Given the prevalence of a disease and the probabilities of a particular finding
(or test result) in patients with and without that disease, what is the probability that
given this finding in a patient, that the patient has the disease (What is the positive
predictive value (PPV) of the finding or positive test result)?
In mathematical terms:
The positive predictive value (PPV) of a test or finding with respect to a disease
(the probability that a patient with a positive test or finding has the disease (P(D|F) ):
P( F | D) × P( D)
PPV = P( D| F)
=
P( F | D) × P( D) + P( F | − D) × P( −D)