Interpreting positive and negative predictive values of diagnostic tests is crucial for clinical decision-making as they quantify the clinician’s confidence in an individual’s disease status after testing. For a given diagnostic test, these values depend on the pretest probability (ie, the probability that an individual has the disease before testing), which differs across individuals. Therefore, they should not be presented as a single pair for clinical use. To account for this individual variability in pretest probability and the minimum confidence level required to conclude on an individual’s disease status, we propose the use of the minimum and maximum pretest probabilities (“PTP+_conf” and “PTP-_conf”). These thresholds depend on the test’s sensitivity and specificity, as well as the clinician’s predefined confidence level. They represent the pretest probability above (or below) which a positive (or negative) test result allows the clinician to reach that minimum confidence level (“conf”) regarding the presence or absence of disease. These “PTP+_conf” and “PTP-_conf” values can be considered as intrinsic characteristics of a diagnostic test for a given confidence threshold. Clinicians then only need to compare their bedside estimate of the individual’s pretest probability with “PTP+_conf” (if positive result) or “PTP-_conf” (if negative result) to determine whether they can conclude with sufficient confidence after obtaining the test result.