CoMDA-ML-P: Application of Machine Learning Method in Validation of Screening Cognitive Test for Parkinsonisms

Sponsor

Ospedale Generale Di Zona Moriggia-Pelascini (Other)

Overall Status

Completed

CT.gov ID

NCT04858893

Collaborator

Ospedale di Vipiteno-Sterzing (SABES-ASDAA) (Other)

562

Enrollment

Location

Actual Duration (Months)

12.8

Patients Per Site Per Month

Study Details

Study Description

Brief Summary

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.

Condition or Disease	Intervention/Treatment	Phase
Primary Parkinsonism Secondary Vascular Parkinson Disease Multiple System Atrophy Supranuclear Palsy, Progressive	Diagnostic Test: CoMDA associated with Neural Net 91 classificator

Detailed Description

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.

Study Design

Study Type:

Observational

Actual Enrollment :

562 participants

Observational Model:

Cohort

Time Perspective:

Prospective

Official Title:

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

Actual Study Start Date :

Jan 1, 2017

Actual Primary Completion Date :

Feb 1, 2020

Actual Study Completion Date :

Aug 31, 2020

Arms and Interventions

Arm	Intervention/Treatment
Subjects affected from Parkinsonims 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
Health Controls CoMDA was administered and total score was calculate to develop the Neural Net 91 classificator	Diagnostic Test: CoMDA associated with Neural Net 91 classificator

Arm

Intervention/Treatment

Subjects affected from Parkinsonims

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

Health Controls

CoMDA was administered and total score was calculate to develop the Neural Net 91 classificator

Diagnostic Test: CoMDA associated with Neural Net 91 classificator

Outcome Measures

Primary Outcome Measures

Neural Net 91 classificator from CoMDA score [30 minuts]
prediction of cognitive level obtained from the application of Neural Net 91 classificator at CoMDA score

Eligibility Criteria

Criteria

Ages Eligible for Study:

N/A and Older

Sexes Eligible for Study:

All

Accepts Healthy Volunteers:

Yes

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:

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.