Better Diagnosis Series: Differential Diagnosis
Good Morning!
This week I have put together a series of different articles based on
helping all of us to know and understand the components in a
diagnosis and tools that may help in determining a proper and
affective way in determining the facts.
Better Diagnosis Series: Differential Diagnosis
I would like to thank the Centre for Health Evidence for this great
research.
Differential Diagnosis
Sick persons seldom present with the diagnosis already made; instead,
they present with one or more symptoms. These symptoms prompt the
clinician to gather information through history and physical
examination, identifying clinical findings that suggest explanations
for the symptom(s). For example, in an older woman presenting with
generalized pruritis, the clinician could identify recent anorexia
and weight loss, along with jaundice and the absence of a rash. For
most symptoms, the clinician must consider multiple causes for the
patient's findings.
Differential diagnosis is the method by which the clinician considers
the possible causes of a patient's clinical findings before making a
final diagnosis. [2] [3] Experienced clinicians often group the
findings into meaningful clusters, summarized in brief phrases about
the symptom, body location or organ system involved, such
as "generalized pruritis", "painless jaundice" and "constitutional
symptoms" for the older woman mentioned earlier.
We call these clusters `clinical problems', [3] [4] and include
problems of biologic, psychologic or sociologic origin. [5] It is for
these clinical problems, rather than for the final diagnosis, that
the clinician selects a patient's differential diagnosis.
When considering a patient's differential diagnosis, how is the
clinician to decide which disorders to pursue? If the clinician were
to consider all known causes equally likely and test for them all
simultaneously (the `possibilistic' approach), then the patient would
undergo unnecessary testing. Instead, the experienced clinician is
selective, considering first those disorders that are more likely
(a `probabilistic' approach), more serious if left undiagnosed and
untreated (a `prognostic' approach) or more responsive to treatment
if offered (a `pragmatic' approach).
Wisely selecting a patient's differential diagnosis involves all three
considerations (probabilistic, prognostic and pragmatic). The
clinician's single best explanation for the patient's clinical problem
(s) can be termed the `leading hypothesis' or `working diagnosis'. A
few (usually 1 to 5) other diagnoses, termed `active alternatives' ,
may be worth considering further at the time of initial work-up,
because of their likelihood, seriousness if undiagnosed and
untreated, or responsiveness to treatment. Additional causes of
the clinical problem(s), termed `other hypotheses' , may be too
unlikely to consider at the time of initial diagnostic work-up, but
remain possible and could be considered further if the working
diagnosis and active alternatives are later disproved. Using this
framework for the patient with palpitations in the scenario, you are
considering anxiety as the working diagnosis, and you are wondering
whether cardiac arrhythmias, hyperthyroidism or pheochromocytoma
belong in the active alternatives or the other hypotheses.
Selecting a patient-specific differential diagnosis has implications
for both diagnostic testing and initial therapy. For the leading
hypothesis, the clinician may choose to confirm the diagnosis, using
a highly specific test with a high likelihood ratio for a positive
result. [10] [11] For the active alternatives, the clinician would
choose to exclude these diagnoses, using highly sensitive tests with
low likelihood ratios for negative results. Usually, the clinician
would not order tests initially for the other hypotheses. The
clinician may start initial therapy for both the working diagnosis
and for one or more of the active alternatives, depending on
circumstances.
How can information about disease probability help clinicians select
patients' differential diagnoses? We'll illustrate with some brief
cases. First, consider a patient who presents with a painful eruption
of grouped vesicles in the distribution of a single dermatome. In an
instant, an experienced clinician would make a diagnosis of herpes
zoster and turn to thinking about whether to offer the patient
therapy. The working diagnosis is zoster and there are no active
alternatives. In other words, the probability of zoster is so high
(near 1.0 or 100%) that it is above a threshold where no further
testing is required.
Next, consider a previously healthy athlete who presents with lateral
rib cage pain after being accidentally struck by an errant baseball
pitch. Again, the experienced clinician might rapidly recognize the
clinical problem (post-traumatic lateral chest pain), and quickly
list a leading hypothesis (rib contusion) and an active alternative
(rib fracture), and plan a test (radiograph) to exclude the latter.
If asked, the clinician could also list disorders that are too
unlikely to consider further (such as myocardial infarction). In
other words, while not as likely as rib contusion, the probability of
a rib fracture is above a threshold for testing, while the
probability of myocardial infarction is below the threshold for
testing.
These cases illustrate how clinicians can estimate the probability of
disease from the patient's clinical findings, risk factors,
exposures, etc., and then compare disease probabilities to two
thresholds. The probability above which the diagnosis is sufficiently
likely to warrant therapy defines the upper threshold. This threshold
is termed the `test-treatment' or simply the `treatment' threshold.
[12] In the case of shingles above, the clinician judged the
diagnosis of zoster to be above this treatment threshold of
probability. The probability below which the clinician a diagnosis
warrants no further consideration defines the llower threshold. This
treatment is termed the `no test-test' or simply the `test'
threshold. In the case of post-traumatic torso pain above, the
diagnosis of rib fracture fell above, and the diagnosis of
myocardial infarction below, the test threshold.
Clinicians begin with pre-test estimates of disease probability, and
then adjust the probability as new diagnostic information arrives.
Test results are useful when they move our pre-test probabilities
across one of these two thresholds. For a disorder with a pre-test
probability above the treatment threshold, a confirmatory test that
raises the probability further would not aid diagnostically. On the
other end of the scale, for a disorder with a pre-test probability
below the test threshold, an exclusionary test that lowers the
probability further would not aid diagnostic
