Clinical trial NCT04858893: Application of Machine Learning Method in Validation of Screening Cognitive Test for Parkinsonisms
Official title: Cognitive Screening in Patients With Parkinsonism: Proposal for a New, Machine Learning Based Diagnostic Tool
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Primary Parkinsonism
Secondary Vascular Parkinson Disease
Multiple System Atrophy
Supranuclear Palsy, Progressive
Lead sponsor: Ospedale Generale Di Zona Moriggia-Pelascini
Sponsor class: OTHER
Collaborators: name: Ospedale di Vipiteno-Sterzing (SABES-ASDAA)
First posted: 2021-04-26
Enrollment: 562
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: 500 subjects suffering from different forms of Parkinson Disease or Atypical Parkinsonims Syndorme and 61 Helathy Controls

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.

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.

Eligibility criteria: 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.

Primary outcomes:
measure: Neural Net 91 classificator from CoMDA score
timeFrame: 30 minuts
Secondary outcomes:
Other outcomes:
Locations:
city: Gravedona E Uniti
country: Italy
facility: "Moriggia Pelascini" Hospital
state: Como
Study officials:
References:
citation: Ortelli P, Ferrazzoli D, Versace V, Cian V, Zarucchi M, Gusmeroli A, Canesi M, Frazzitta G, Volpe D, Ricciardi L, Nardone R, Ruffini I, Saltuari L, Sebastianelli L, Baranzini D, Maestri R. Optimization of cognitive assessment in Parkinsonisms by applying artificial intelligence to a comprehensive screening test. NPJ Parkinsons Dis. 2022 Apr 11;8(1):42. doi: 10.1038/s41531-022-00304-z.