A Bayesian adaptive algorithm (QUEST) to estimate olfactory threshold in hyposmic patients

Abstract

AbstractObjectivesThe sense of smell is important as a warning system, in social communication and in guiding food intake. Impairment is common, and cases are increasing following COVID‐19. Olfactory dysfunction may lead to decreased quality of life. There are several established ways to assess olfaction including the “Sniffin’ Sticks” which are a validated test for healthy and diseased populations.MethodsThe odor threshold is traditionally determined using a single staircase procedure, with narrow or wide step. We investigated a Bayesian adaptive algorithm (QUEST) to estimate olfactory threshold in a hyposmic population compared with a healthy control group. Thresholds were measured using the three procedures in two sessions (Test and Retest).ResultsAll the tested methods showed considerable overlap in both groups: there was a positive correlation between the QUEST procedure and classic staircase method (r = 0.88), and high test–retest reliability for all three methods used (Sniffin’ Sticks narrow: r = 0.81; Sniffin’ Sticks wide: r = 0.95; QUEST: r = 0.80).ConclusionsResults from these approaches exhibit considerable overlap with all of them being suitable for clinical use. An advantage of the QUEST method can be the defined number of trials needed to determine an odor threshold.