Visualizing the value of diagnostic tests and prediction models, part I: introduction and expected gain in utility as...

In this first of a 3-part series, we review expected gain in utility (EGU) calculations and graphs; in later parts, we contrast them with net benefit calculations and graphs. Our example is plasma D-dimer as a test for pulmonary embolism.

We approach EGU calculations from the perspective of a clinician evaluating a patient. The clinician is considering 1) not testing and not treating, 2) testing and treating according to the test result; or 3) treating without testing. We use simple algebra and graphs to show how EGU depends on pretest probability and the benefit of treating someone with disease (B) relative to the harms of treating someone without the disease (C) and the harm of the testing procedure itself (T).

The treatment threshold probability, i.e., the probability of disease at which the expected benefit of treating those with disease is balanced by the harm of treating those without disease (EGU = 0) is C/(C + B). When a diagnostic test is available, the course of action with the highest EGU depends on C, B, T, the pretest probability of disease, and the test result. For a given C, B, and T, the lower the pretest probability, the more abnormal the test result must be to justify treatment.

EGU calculations and graphs allow visualization of how the value of testing can be calculated from the prior probability of the disease, the benefit of treating those with disease, the harm of treating those without disease, and the harm of testing itself.