Chapter 1-5: The Threshold Model: Conceptualizing Probabilities

The ends of the bar in the threshold model represent 0% and 100% pretest probability. The treatment threshold is the probability above which the diagnosis is so likely you would treat the patient without further testing. The test threshold is the probability below which the diagnosis is so unlikely it is excluded without further testing (Figure 1-2).

For example, consider Ms. A, a 19-year-old woman, who complains of 30 seconds of sharp right-sided chest pain after lifting a heavy box. The pretest probability of cardiac ischemia is so low that no further testing is necessary (Figure 1-3).

Now consider Mr. B, a 60-year-old man, who smokes and has diabetes, hypertension, and 15 minutes of crushing substernal chest pain accompanied by nausea and diaphoresis, with an ECG showing ST-segment elevations in the anterior leads. The pretest probability of an acute MI is so high you would treat without further testing, such as measuring cardiac enzymes (Figure 1-4).

Diagnostic tests are necessary when the pretest probability of disease is in the middle, above the test threshold and below the treatment threshold. A really useful test shifts the probability of disease so much that the posttest probability (the probability of disease after the test is done) crosses one of the thresholds (Figure 1-5). Test and treatment thresholds vary depending on the seriousness of the disease, the toxicity of the treatment, and the invasiveness of the test. For example, the treatment threshold for bacterial meningitis is low: it is a potentially fatal disease and antibiotics are a relatively nontoxic treatment. Treatments for lung cancer (such as chemotherapy or radiation therapy) have considerable toxicity, making the treatment threshold for lung cancer 100%; treatment is never given without a positive biopsy.