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Optimising classification of Parkinson's disease based on motor, olfactory, neuropsychiatric and sleep features.

ABSTRACT: Olfactory loss, motor impairment, anxiety/depression, and REM-sleep behaviour disorder (RBD) are prodromal Parkinson's disease (PD) features. PD risk prediction models typically dichotomize test results and apply likelihood ratios (LRs) to scores above and below cut-offs. We investigate whether LRs for specific test values could enhance classification between PD and controls. PD patient data on smell (UPSIT), possible RBD (RBD Screening Questionnaire), and anxiety/depression (LADS) were taken from the Tracking Parkinson's study (n = 1046). For motor impairment (BRAIN test) in PD cases, published data were supplemented (n = 87). Control data (HADS for anxiety/depression) were taken from the PREDICT-PD pilot study (n = 1314). UPSIT, RBDSQ, and anxiety/depression data were analysed using logistic regression to determine which items were associated with PD. Gaussian distributions were fitted to BRAIN test scores. LRs were calculated from logistic regression models or score distributions. False-positive rates (FPRs) for specified detection rates (DRs) were calculated. Sixteen odours were associated with PD; LRs for this set ranged from 0.005 to 5511. Six RBDSQ and seven anxiety/depression questions were associated with PD; LRs ranged from 0.35 to 69 and from 0.002 to 402, respectively. BRAIN test LRs ranged from 0.16 to 1311. For a 70% DR, the FPR was 2.4% for the 16 odours, 4.6% for anxiety/depression, 16.0% for the BRAIN test, and 20.0% for the RBDSQ. Specific selections of (prodromal) PD marker features rather than dichotomized marker test results optimize PD classification. Such optimized classification models could improve the ability of algorithms to detect prodromal PD; however, prospective studies are needed to investigate their value for PD-prediction models.

SUBMITTER: Bestwick JP

PROVIDER: S-EPMC8463675 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Objective: To conduct an investigation into the reliability of assessing the olfactory function of patients with Parkinson's disease (PD) in a clinical setting of crowding patients in populated countries, such as China, by the hyposmia rating scale (HRS) and compare other non-motor features between patients with PD with olfactory dysfunction (PD-OD) and patients with PD without olfactory dysfunction (PD-NOD), according to the result of olfactory function assessed by the Sniffin' Sticks test. Methods: A total of 320 patients with clinically confirmed or clinically possible PD were recruited. Olfactory function of all participants was assessed with the HRS and the Sniffin' Sticks test. Demographic data and clinical information were collected, and patients were evaluated using standardized assessment protocols. With reference to the Sniffin' Sticks test, the specificity, sensitivity, coincidence rate, and kappa value of the HRS was computed, and then its reliability was evaluated. We divided patients into PD-OD and PD-NOD groups based on the results of olfactory function assessed by the Sniffin' Sticks test. Clinical manifestations were compared between PD-OD and PD-NOD. Results: The percentage of patients with OD determined by the Sniffin' Sticks test was 65.6%, and the percentage of those with OD was 55.6% when using the HRS measured olfactory function. With reference to the Sniffin' Sticks test, the specificity, sensitivity, coincidence rate, and kappa value of the HRS were 82.73, 75.71, 78.13%, and 0.55, respectively. The area under the receiver operating characteristic curve for the HRS was 0.793. There were no differences in demographic characteristics between the PD-OD and PD-NOD groups. The patients with hyposmia had more severe non-motor symptoms. Conclusion: The HRS is of great value as a self-assessment scale for evaluating olfactory function, especially in PD patients over 55 years old. Moreover, PD patients with hyposmia have more severe non-motor features than PD patients without hyposmia, mainly in terms of mood and constipation.

Project description:BackgroundFreezing of gait (FOG) is a sensitive problem, which is caused by motor control deficits and requires greater attention during postural transitions such as turning in people with Parkinson's disease (PD). However, the turning characteristics have not yet been extensively investigated to distinguish between people with PD with and without FOG (freezers and non-freezers) based on full-body kinematic analysis during the turning task. The objectives of this study were to identify the machine learning model that best classifies people with PD and freezers and reveal the associations between clinical characteristics and turning features based on feature selection through stepwise regression.MethodsThe study recruited 77 people with PD (31 freezers and 46 non-freezers) and 34 age-matched older adults. The 360° turning task was performed at the preferred speed for the inner step of the more affected limb. All experiments on the people with PD were performed in the "Off" state of medication. The full-body kinematic features during the turning task were extracted using the three-dimensional motion capture system. These features were selected via stepwise regression.ResultsIn feature selection through stepwise regression, five and six features were identified to distinguish between people with PD and controls and between freezers and non-freezers (PD and FOG classification problem), respectively. The machine learning model accuracies revealed that the random forest (RF) model had 98.1% accuracy when using all turning features and 98.0% accuracy when using the five features selected for PD classification. In addition, RF and logistic regression showed accuracies of 79.4% when using all turning features and 72.9% when using the six selected features for FOG classification.ConclusionWe suggest that our study leads to understanding of the turning characteristics of people with PD and freezers during the 360° turning task for the inner step of the more affected limb and may help improve the objective classification and clinical assessment by disease progression using turning features.

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Optimising classification of Parkinson's disease based on motor, olfactory, neuropsychiatric and sleep features.

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