Better Diagnosis Series: Differential Diagnosis

[Guest guest]

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 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.

 

 

 

References:

 

1. Weber BE, Kapoor WN. Evaluation and outcomes of patients with palpitations.

Am J Med 1996; 100: 138-148.

 

2. Sox HC, Blatt MA, Higgins MC, Marton KI. Medical Decision Making Butterworth,

Boston, 1988.

 

3. Barondess JA, Carpenter CCJ, Eds. Differential Diagnosis, Philadelphia, Lea &

Febiger, 1994.

 

4. Glass RD. Diagnosis: A Brief Introduction, Melbourne, Oxford University

Press, 1996.

 

5. Engel GL. The need for a new medical model: a challenge for biomedicine.

Science 1977; 196: 129-136.

 

6. Laupacis A, Wells G, Richardson WS, Tugwell P, for the Evidence-Based

Medicine Working Group. Users’ guides to the medical literature: V. How to use

an article about prognosis. JAMA 1994; 272: 234 - 237.

 

7. Guyatt GH, Sackett DL, Cook DJ, for the Evidence-Based Medicine Working

Group. Users’ guides to the medical literature: II. How to use an article about

therapy or prevention: A. Are the results of the study valid? JAMA 1993; 270:

2598-2601.

 

8. Guyatt GH, Sackett DL, Cook DJ, for the Evidence-Based Medicine Working

Group. Users’ guides to the medical literature: II. How to use an article about

therapy or prevention: B. What are the results and will they help me in caring

for my patients? JAMA 1994; 271: 59-63.

 

9. Dans AL, Dans LF, Guyatt GH, Richardson S, for the Evidence-Based Medicine

Working Group. Users’ guides to the medical literature: XIV. How to decide on

the applicability of clinical trial results to your patient. JAMA 1998; 279: 545

- 549.

 

10. Jaeschke R, Guyatt GH, Sackett DL, for the Evidence-Based Medicine Working

Group. Users’ guides to the medical literature: III. How to use an article about

a diagnostic test. A. Are the results of the study valid? JAMA 1994; 271: 389 -

311.

 

11. Jaeschke R, Guyatt GH, Sackett DL, for the Evidence-Based Medicine Working

Group. Users’ guides to the medical literature: III. How to use an article about

a diagnostic test. B. What are the results and will they help me in caring for

my patients? JAMA 1994; 271: 703 -707.

 

12. Pauker SG, Kassirer JP. The threshold approach to clinical decision making.

N Engl J Med 1980; 302: 1109 - 1117.

 

13. Tversky A, Kahneman D. Judgment under uncertainty: heuristics and biases.

Science 1974; 185: 1124-1131.

 

14. Dawson NV, Arkes HR. Systematic errors in medical decision making: judgment

limitations. J Gen Intern Med 1987; 2: 183-187.

 

15. Kassirer JP, Kopelman RI. Cognitive errors in diagnosis: instantiation,

classification, and consequences. Am J Med 1989; 86: 433 - 441.

 

16. Richardson WS. “Where do pre-test probabilities come from?” [EBM Note]

Evidence Based Medicine (in press).

 

17. Guyatt GH, Patterson C, Ali M, Singer J, Levine M, Turpie I, Meyer R.

Diagnosis of iron deficiency anemia in the elderly Am J Med 1990; 88: 205 - 209.