
One of the most commonly used strategies for medical decision making mirrors the scientific method of hypothesis generation followed by hypothesis testing. Diagnostic hypotheses are accepted or rejected based on testing.
Hypothesis generation:
Hypothesis generation involves the identification of the main diagnostic possibilities (differential diagnosis) that might account for the patient's clinical problem. The patient's chief complaint (eg, chest pain) and basic demographic data (age, sex, race) are the starting points for the differential diagnosis, which is usually generated by pattern recognition. Each element on the list of possibilities is ideally assigned an estimated probability (see Sidebar 1: Probability and Odds), or likelihood, of its being the correct diagnosis (pretest probability—see Table 1: Hypothetical Differential Diagnosis and PreTest and PostTest Probabilities for a 50YrOld Hypertensive, Diabetic Cigarette Smoker With Chest Pain).
Clinicians often use vague terms such as “highly likely,” “improbable,” and “cannot rule out” to describe the likelihood of disease. Both clinicians and patients often misinterpret such semiquantitative terms; explicit statistical terminology should be used instead when available. Mathematical computations assist clinical decision making and, even when exact numbers are unavailable, can better define clinical probabilities and narrow the list of hypothetical diseases further.
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Sidebar 1

 





Hypothesis testing:
The initial differential diagnosis based on chief complaint and demographics is usually very large, so the clinician first tests the hypothetical possibilities during the history and physical examination, asking questions or doing specific examinations that support or refute a suspected diagnosis. For instance, in a patient with chest pain, a history of leg pain and a swollen, tender leg detected during examination increases the probability of pulmonary embolism.
When the history and physical examination form a clearcut pattern, a presumptive diagnosis is made. Diagnostic testing is used when uncertainties persist after the history and physical examination, particularly when the diseases remaining under consideration are serious or have dangerous or costly treatment. Test results further modify the probabilities of different diagnoses (posttest probability). For example, Table 1: Hypothetical Differential Diagnosis and PreTest and PostTest Probabilities for a 50YrOld Hypertensive, Diabetic Cigarette Smoker With Chest Pain shows how the additional findings that the hypothetical patient had leg pain and swelling and a normal ECG and chest xray modify diagnostic probabilities—the probability of acute coronary syndrome, dissecting aneurysm, and pneumothorax decreases, and the probability of pulmonary embolism increases. These changes in probability may lead to additional testing (in this example, probably chest CT angiography) that further modifies posttest probability (see Table 1: Hypothetical Differential Diagnosis and PreTest and PostTest Probabilities for a 50YrOld Hypertensive, Diabetic Cigarette Smoker With Chest Pain) and, in some cases, confirms or refutes a diagnosis.
Table 1

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Hypothetical Differential Diagnosis and PreTest and PostTest Probabilities for a 50YrOld Hypertensive, Diabetic Cigarette Smoker With Chest Pain 
Diagnosis

PreTest Probability

PostTest Probability I (Additional Findings of Leg Pain, Swelling, and Normal ECG and Chest XRay)

PostTest Probability II (Additional Findings of Segmental Defect on Chest CT Angiography and Normal Serum Troponin I Level)

Acute coronary syndrome

40%

28%

1%

STsegment elevation MI

20%

< 1%

< 1%

Chest wall pain

30%

20%

< 1%

Pulmonary embolism

5%

50%

98%

Dissecting thoracic aortic aneurysm

< 3%

< 1%

< 1%

Spontaneous pneumothorax

< 2%

< 1%

< 1%


Probability Estimations and the Treatment Threshold
The disease probability at and above which treatment is given and no further testing is warranted is termed the treatment threshold (TT).
The above hypothetical example of a patient with chest pain converged on a nearcertain diagnosis (98% probability). When diagnosis of a disease is certain, the decision to treat is a straightforward determination that there is a benefit of treatment (compared with no treatment, and taking into account adverse effects of treatment). When the diagnosis has some degree of uncertainty, as is almost always the case, the decision to treat also must balance the benefit of treating a sick person against the risk of erroneously treating a well person or a person with a different disorder; benefit and risk encompass both financial and medical consequences. This balance must take into account both the likelihood of disease and the magnitude of the benefit and risk. This balance determines where the clinician sets the TT.
Conceptually, if the benefit of treatment is very high and the risk is very low (as when giving a safe antibiotic to a patient with diabetes who possibly has a lifethreatening infection), clinicians tend to accept high diagnostic uncertainty and might initiate treatment even if probability of infection is fairly low (eg, 30%—see Fig. 1: Variation of treatment threshold (TT) with risk of treatment.). However, when the risk of treatment is very high (as when doing a pneumonectomy for possible lung cancer), clinicians want to be extremely sure of the diagnosis and might recommend treatment only when the probability of cancer is very high, perhaps > 95% (see Fig. 1: Variation of treatment threshold (TT) with risk of treatment.). Note that the TT does not necessarily correspond to the probability at which a disease might be considered confirmed or ruled in. It is simply the point at which the risk of not treating is greater than the risk of treating.
Quantitatively, the TT can be described as the point at which probability of disease (p) times benefit of treating a person with disease (B) equals probability of no disease (1 − p) times risk of treating a person without disease (R). Thus, at the TT
p × B = (1 − p) × R
Solving for p, this equation reduces to
p = R/(B + R)
From this equation, it is apparent that if B (benefit) and R (risk) are the same, the TT becomes 1/(1 + 1) = 0.5, which means that when the probability of disease is > 50%, clinicians would treat, and when probability is < 50%, clinicians would not treat.
For a clinical example, a patient with chest pain can be considered. How high should the clinical likelihood of acute MI be before thrombolytic therapy should be given, assuming the only risk considered is shortterm mortality? If it is postulated (for illustration) that mortality due to intracranial hemorrhage with thrombolytic therapy is 1%, then 1% is R, the fatality rate of mistakenly treating a patient who does not have an MI. If net mortality in patients with MI is decreased by 3% with thrombolytic therapy, then 3% is B. Then, TT is 1/(3 + 1), or 25%; thus, treatment should be given if the probability of acute MI is > 25%.
Alternatively, the TT equation can be rearranged to show that the TT is the point at which the odds of disease p/(1 − p) equal the risk:benefit ratio (R/B). The same numerical result is obtained as in the previously described example, with the TT occurring at the odds of the risk:benefit ratio (1/3); 1/3 odds corresponds to the previously obtained probability of 25% (see Sidebar 1: Probability and Odds).
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Limitations:
Quantitative clinical decision making seems precise, but because many elements in the calculations are often imprecisely known (if they are known at all), this methodology is difficult to use in all but the most welldefined and studied clinical situations.
Last full review/revision March 2010 by Douglas L. McGee, DO
Content last modified August 2013
 
