Screening performances of an 8-item UPSIT Italian version in the diagnosis of Parkinson's disease.
Machine learning
Parkinson’s disease
Smell impairment
University of Pennsylvania Smell Identification Test
Journal
Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
ISSN: 1590-3478
Titre abrégé: Neurol Sci
Pays: Italy
ID NLM: 100959175
Informations de publication
Date de publication:
Mar 2023
Mar 2023
Historique:
received:
18
03
2022
accepted:
12
10
2022
pubmed:
20
11
2022
medline:
16
2
2023
entrez:
19
11
2022
Statut:
ppublish
Résumé
Hyposmia is a common finding in Parkinson's disease (PD) and is usually tested through the University of Pennsylvania Smell Identification Test (UPSIT). The aim of our study is to provide a briefer version of the Italian-adapted UPSIT test, able to discriminate between PD patients and healthy subjects (HS). By means of several univariate and multivariate (machine-learning-based) statistical approaches, we selected 8 items by which we trained a partial-least-square discriminant analysis (PLS-DA) and a decision tree (DT) model: class predictions of both models performed better with the 8-item version when compared to the 40-item version. An area under the receiver operating characteristic (AUC-ROC) curve built with the selected 8 odors showed the best performance (sensitivity 86.8%, specificity 82%) in predicting the PD condition at a cut-off point of ≤ 6. These performances were higher than those previously calculated for the 40-item UPSIT test (sensitivity 82% and specificity 88.2 % with a cut-off point of ≤ 21). Qualitatively, our selection contains one odor (i.e., apple) which is Italian-specific, supporting the need for cultural adaptation of smell testing; on the other hand, some of the selected best discriminating odors are in common with existing brief smell test versions validated on PD patients of other cultures, supporting the view that disease-specific odor patterns may exist and deserve a further evaluation.
Identifiants
pubmed: 36401656
doi: 10.1007/s10072-022-06457-2
pii: 10.1007/s10072-022-06457-2
pmc: PMC9676802
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
889-895Informations de copyright
© 2022. The Author(s).
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