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’s disease (PD). Methods 890 PD and 313 control participants in the Discovery cohort study underwent Sniffin’ stick odour 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 two 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 3 stick model determined poor smell and an RBD screening questionnaire score of ≥5, separated iRBD from controls with a sensitivity, specificity, PPV and NPV 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