Development of a simplified smell test to identify Parkinson’s disease using multiple cohorts, machine learning and item response theory

Abstract

To develop a simplified smell test for identifying patients with Parkinson’s disease (PD), we reevaluated the Sniffin’-Sticks-Identification-Test (SST-ID) and University-of-Pennsylvania-Smell-Identification-Test (UPSIT), using three case-control studies. These included 301 patients with PD or dementia with Lewy bodies (DLB), 68 subjects with multiple-system atrophy (MSA) or progressive supranuclear palsy (PSP), and 281 healthy controls (HC). Scents were ranked by area-under-the-curve values for group classification and results leveraged by 8 published studies with 5853 individuals. PD/DLB patients showed markedly worse olfaction than controls, whereas scores for MSA/PSP subjects were intermediate. We identified and validated a subset of 7 shared odorants that performed similarly to the traditional 16-scent SST-ID and 40-scent UPSIT tests in distinguishing PD/DLB from HC. There, the identification of 4 or fewer scents out of 7 served as an effective cut-off between the two groups. We also identified a critical role for distractors (from correct answers) and age on olfaction performance.