Incorporating Uncertainty Into Medical Decision Making: An Approach to Unexpected Test Results

Abstract

The utility of diagnostic tests derives from the ability to translate the population concepts of sensitivity and specificity into information that will be useful for the individual patient: the predictive value of the result. As the array of available diagnostic testing broadens, there is a temptation to de-emphasize history and physical findings and defer to the objective rigor of technology. However, diagnostic test interpretation is not always straightforward. One significant barrier to routine use of probability-based test interpretation is the uncertainty inherent in pretest probability estimation, the critical first step of Bayesian reasoning. The context in which this uncertainty presents the greatest challenge is when test results oppose clinical judgment. It is this situation when decision support would be most helpful. The authors propose a simple graphical approach that incorporates uncertainty in pretest probability and has specific application to the interpretation of unexpected results. This method quantitatively demonstrates how uncertainty in disease probability may be amplified when test results are unexpected (opposing clinical judgment), even for tests with high sensitivity and specificity. The authors provide a simple nomogram for determining whether an unexpected test result suggests that one should "switch diagnostic sides.'' This graphical framework overcomes the limitation of pretest probability uncertainty in Bayesian analysis and guides decision making when it is most challenging: interpretation of unexpected test results.

Clinical judgment to estimate pretest probability in the diagnosis of Cushing's syndrome under a Bayesian perspective

“…In addition, the rates of false-negative and falsepositive test results depend not only on sensitivity and specificity of the test, but also on the prior probability that the subject has CS. Without any additional information, the pretest probability is equal to the prevalence of the disease (14,15), and this may be true for a disease with high prevalence. However, it is not applicable in the case of rare endogenous CS, which besides the overlapping of some clinical findings with more prevalent disorders, has little accurate epidemiological information.…”

“…Indeed, the choice of pretest probabilities in clinical settings may be inaccurate and vary widely (18)(19)(20). However, this uncertainty in pretest probability represents no obstacle for clinicians, once they use their intuition based on their experience, as they are natural Bayesians (6,14).…”

Uncertainty in Clinical Prediction Rules: The Value of Credible Intervals

“…1,4 The resultant uncertainty interval within a Bayesian framework is known as a credible interval (CrI) and gives the range in which the "true" likelihood of a given diagnosis or outcome lies for a specified probability level. 26 Conceptually, it is the probability of a probability.…”

“…Conversely, where the slope is relatively flat, consideration of uncertainty of the pretest probability will only have a relatively small impact on the posterior probability uncertainty interval. 4 The degree to which uncertainty in the likelihood ratio impacts the posterior probability is dependent on the magnitude of the pretest probability. Therefore, incorporation of uncertainty of both the pretest probability and the likelihood ratio is required to calculate the degree of uncertainty of the posterior probability.…”

Screening for Obstructive Sleep Apnea: Bayes Weighs in

“…The complication rates were similar at the three levels of OSA severity, suggesting that the sensitivity and specificity for patients with AHI >5 is a clinically relevant cutoff. Unexpected results, such as a negative screen in a high risk patient, represent a challenge to diagnostic interpretation, especially when the pre-test probability (pre-TP) is uncertain [14]. OSA screening faces the potentially false reassurance of a negative screen: although the negative result lowers the probability of OSA, the remaining risk may be unacceptably high.…”