Olfactory testing in Parkinson disease and REM behavior disorder: A machine learning approach

Objective We sought to identify an abbreviated test of impaired olfaction amenable for use in busy clinical environments in prodromal (isolated REM sleep behavior disorder [iRBD]) and manifest Parkinson disease (PD). Methods Eight hundred ninety individuals with PD and 313 controls in the Discovery cohort study underwent Sniffin’ Stick odor identification assessment. Random forests were initially trained to distinguish individuals with poor (functional anosmia/hyposmia) and good (normosmia/super-smeller) smell ability using all 16 Sniffin’ Sticks. Models were retrained using the top 3 sticks ranked by order of predictor importance. One randomly selected 3-stick model was tested in a second independent PD dataset (n = 452) and in 2 iRBD datasets (Discovery n = 241, Marburg n = 37) before being compared to previously described abbreviated Sniffin’ Stick combinations. Results In differentiating poor from good smell ability, the overall area under the curve (AUC) value associated with the top 3 sticks (anise/licorice/banana) was 0.95 in the Development dataset (sensitivity 90%, specificity 92%, positive predictive value 92%, negative predictive value 90%). Internal and external validation confirmed AUCs ≥0.90. The combination of the 3-stick model determined poor smell, and an RBD screening questionnaire score of ≥5 separated those with iRBD from controls with a sensitivity, specificity, positive predictive value, and negative predictive value of 65%, 100%, 100%, and 30%. Conclusions Our 3-Sniffin’-Stick model holds potential utility as a brief screening test in the stratification of individuals with PD and iRBD according to olfactory dysfunction. Classification of Evidence This study provides Class III evidence that a 3-Sniffin’-Stick model distinguishes individuals with poor and good smell ability and can be used to screen for individuals with iRBD