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Clinical Decision Making Strategies
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 near-certain diagnosis (98% probability). When diagnosis of a disease is certain, the decision to treat is a straightforward determination of whether there is a benefit of treatment (compared with no treatment, and taking into account the potential 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 financial, social, 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 treatment threshold. When there is some uncertainty about the diagnosis, the decision to treat 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. 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 life-threatening infection), clinicians tend to accept high diagnostic uncertainty and might initiate treatment even if probability of infection is fairly low (eg, 30%—see figure 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 Horizontal lines represent post-test probability. Quantitatively, the treatment threshold 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 treatment threshold 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 treatment threshold 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 myocardial infarction (MI) be before thrombolytic therapy should be given, assuming the only risk considered is short-term 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, treatment threshold is 1/(3+1), or 25%; thus, treatment should be given if the probability of acute MI is>25%. p) equal the risk:benefit ratio (R/B). The same numerical result is obtained as in the previously described example, with the treatment threshold occurring at the odds of the risk:benefit ratio (1/3); 1/3 odds corresponds to the previously obtained probability of 25% (see Probability and odds ). Quantitative clinical decision making seems precise, but because many elements in the calculations (eg, pre-test probability) are often imprecisely known (if they are known at all), this methodology is difficult to use in all but the most well-defined and studied clinical situations. In addition, the patient's philosophy regarding medical care (ie, tolerance of risk and uncertainty) also needs to be taken into account in a shared decision-making process. For instance, although clinical guidelines do not recommend starting a lifelong course of urate-lowering drugs after a first attack of gout, some patients prefer to begin such treatment immediately because they strongly want to avoid a second attack. 