The therapeutic threshold in clinical decision-making for TB

Abstract

Because TB control is still hampered by the limitations of diagnostic tools, diagnostic uncertainty is common. The decision to offer treatment is based on clinical decision-making. The therapeutic threshold, test threshold and test-treatment threshold can guide in making these decisions. This review summarizes the literature on methods to estimate the therapeutic threshold that have been applied for TB. Only five studies estimated the threshold for the diagnosis of TB. The therapeutic threshold can be estimated by prescriptive methods, based on calculations, and by descriptive methods, deriving the threshold from observing clinical practice. Test and test-treatment thresholds can be calculated using the therapeutic threshold and the characteristics of an available diagnostic test. Estimates of the therapeutic threshold for pulmonary TB from intuitive descriptive approaches (20%-50%) are higher than theoretical prescriptive calculations (2%-3%). In conclusion, estimates of the therapeutic threshold for pulmonary TB depend on the method used. Other methods exist within the field of decision-making that have yet to be implemented or adapted as tools to estimate the TB therapeutic threshold. Because clinical decision-making is a core element of TB management, it is necessary to find a new, clinician-friendly way to unbiasedly estimate context-specific, agreed upon therapeutic thresholds.

Keywords: TB; clinical decision-making; diagnosis; threshold.

Figure 1.

Utility function of potential treatment. The expected utility of treat and no treatment options are shown as a function of the probability of disease, inspired by Sox et al. A patient either has or does not have the disease, and is treated or not treated. The four combinations of true status and decision taken are shown as dots in the graph. When the true disease status is not known, it is possible to estimate the probability that a patient has the disease and estimate the expected utility of, respectively, treating (blue line) and not treating (red line). The therapeutic threshold is the probability of disease at which the expected utilities are equal. The expected utility theory (EUT), aimed at choosing the option with the highest expected utility, is calculated as the sum of utilities of all possible outcomes of an action weighted by their corresponding probabilities. EUT takes into account the net utility (or ‘value’) of treating when the disease is present (U_(T|D)) or absent (U_{(T|no D)}), and not treating when the disease is present (U_{(no T|D)}) or absent (U_{(no T|no D)}). The therapeutic threshold is the probability of disease at which the expected utility of treating and not treating is the same.

Figure 2.

Test threshold and test-treatment threshold. The test and test-treatment thresholds and the need for a diagnostic test is shown, as inspired by Decroo et al. A probability of disease equal to the test threshold before testing will result in a probability equal to the therapeutic threshold if the test is positive (arrow A). Similarly, a probability of disease equal to the test-treatment threshold before testing will result in a probability equal to the therapeutic threshold if the test is negative (arrow B). Consequently, when the probability of disease is below the test threshold or above the test-treatment threshold, the test result does not bring sufficient evidence to impact the treatment decision (dashed arrows). Only when the probability of disease lies between the two thresholds should the test be performed and its result followed (dotted arrows).

Figure 3.

Flowchart showing articles retrieved at different steps of the literature review. n, number.