Abstract
OBJECTIVE: To assess the predictive potential of the complete response pattern from the University of Pennsylvania Smell Identification Test for the diagnosis of Parkinson's disease. METHODS: We analyzed a large dataset from the Arizona Study of Aging and Neurodegenerative Disorders, a longitudinal clinicopathological study of health and disease in elderly volunteers. Using the complete pattern of responses to all 40 items in each subject's test, we built predictive models of neurodegenerative disease, and we validated these models out of sample by comparing model predictions against postmortem pathological diagnosis. RESULTS: Consistent with anatomical considerations, we found that the specific test response pattern had additional predictive power compared with a conventional measure - total test score - in Parkinson's disease, but not Alzheimer's disease. We also identified specific test questions that carry the greatest predictive power for disease diagnosis. INTERPRETATION: Olfactory ability has typically been assessed with either self-report or total score on a multiple choice test. We showed that a more accurate clinical diagnosis can be made using the pattern of responses to all the test questions, and validated this against the "gold standard" of pathological diagnosis. Information in the response pattern also suggests specific modifications to the standard test that may optimize predictive power under the typical clinical constraint of limited time. We recommend that future studies retain the individual item responses for each subject, and not just the total score, both to enable more accurate diagnosis and to enable additional future insights.