Application of Machine Learning Method in Validation of Screening Cognitive Test for Parkinsonisms

Study Overview
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Brief Summary
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Based on a prospectively collected data analysis, a new tool, namely CoMDA (Cognition in Movement Disorders Assessment) is developed by merging each item of Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA) and Frontal Assessment Battery (FAB). A machine learning, able to classify the cognitive profile and predict patients' at risk of dementia, is created.

Detailed Description
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A prospectively data-base was setting up, collecting CoMDA and in-depht-neuropsychologocal-battery scores, obtained from the evaluation of 500 patients with parkinsonisms. Data were analyzed to compare the classification of patient cognition profile, obtained with CoMDA, MMSE, MoC and FAB, with that obtained from in-depth neuropsychological evaluation. A very high percentage of false negative emerged, for MMSE, MoCA and FAB. Conversely, the CoMDA score significantly reduces the rate of false negative. This new tool, namely CoMDA (Cognition in Movement Disorders Assessment), was composed, by merging each item of Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA) and Frontal Assessment Battery (FAB). Moreover, we created a machine learning, namely Neural Net 91classification able to classify the cognitive profile and predict patients' at risk of dementia, providing a prediction of the findings resulting from a in-depht neuropsychological evaluation. CoMDA and the related Neural Net 91classification represent a reliable, time-sparing screening instrument, which is much more powerful of other common, widely-adopted tools.

Official Title
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Cognitive Screening in Patients With Parkinsonism: Proposal for a New, Machine Learning Based Diagnostic Tool

Conditions
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Primary Parkinsonism, Secondary Vascular Parkinson Disease, Multiple System Atrophy, Supranuclear Palsy, Progressive

Intervention / Treatment
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* Diagnostic Test: CoMDA associated with Neural Net 91 classificator


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: diagnosis of idiopathic PD according to the MDS clinical diagnostic criteria (Postuma et al. 2015); b) diagnosis of PSP according to the MDS clinical diagnostic criteria (Höglinger et al. 2017); c) diagnosis of MSA according to the second diagnostic consensus statement (Gilman et al. 2008); d) diagnosis of VP according to Zijlmans et al (Zijlmans et al. 2004). Exclusion Criteria: a) any focal brain lesion detected with brain imaging studies (CT or MRI); b) diagnosis of clinically relevant psychiatric disorders, psychosis (evaluated with Neuropsychiatric Inventory) and/or delirium; c) diagnosis of dementia or MCI; d) diagnosis of neurological diseases other than PD or atypical parkinsonian syndromes; e) other medical conditions negatively affecting the cognitive status; f) disturbing resting and/or action tremor, corresponding to scores 2-4 in the specific items of MDS Unified Parkinson's Disease Rating Scale (MDS-UPDRS) III, such as to affect the psychometric evaluation; g) disturbing dyskinesia, corresponding to scores 2-4 in the specific items of MDS-UPDRS III, such as to affect the psychometric evaluation; h) auditory and/or visual dysfunctions impairing the patient´s ability to perform cognitive tests.
Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Subjects affected from Parkinsonims<br>Scores of MMSE, FAB MoCA were summarized to calculate the CoMDA scores, than they were used to develop the Neural Net 91 classificator | Diagnostic Test: CoMDA associated with Neural Net 91 classificator<br> <br> |
| Health Controls<br>CoMDA was administered and total score was calculate to develop the Neural Net 91 classificator | Diagnostic Test: CoMDA associated with Neural Net 91 classificator<br> <br> |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Neural Net 91 classificator from CoMDA score | prediction of cognitive level obtained from the application of Neural Net 91 classificator at CoMDA score | 30 minuts |