Better Diagnosis Series: Differential Diagnosis

[Guest guest]

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 diagnostically. When the clinician

believes the pre-test probability is high enough to test for and not

high enough to begin treatment (i.e. between the two thresholds), a

test could be diagnostically useful if it moves the probability

across either threshold.

How do clinicians arrive at pre-test estimates of disease

probability? They remember prior cases with the same clinical problem

(s), so that disorders diagnosed frequently have higher probability

than diagnoses made less frequently. Remembered cases are easily and

quickly available, and are calibrated to our local practices. Yet our

memories are imperfect, and the probabilities that result are subject

to biases and errors. [13] [14] [15]

Knowing memory is prone to bias, clinicians can consult other sources

including population prevalence statistics and original research.

Inclusion of the entire population, rather than persons with a given

clinical problem, limit the usefulness of population surveys. [16]

Inconsistency of how diagnoses are made and recorded may further

limit survey usefulness.

Original research constitutes another source of information about

disease probability. For example, in a study of diagnostic tests for

anemia in the aged, investigators compared blood tests with bone

marrow results in 259 elderly persons, finding iron deficiency in 94

(36%) patients. [17] Thus, while this study focused on evaluating

tests for iron deficiency, it also provides information about disease

frequency.

Keep in mind that selecting a patient's differential diagnosis wisely

includes not only considering how likely various disorders are, but

also considering how serious are the various diseases if left

undiagnosed and untreated, and how much other clinical actions, like

treatment or public health measures to reduce disease spread, could

help the patient or the community.