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

NCT04858893 — Completed, 562 participants, Ospedale Generale Di Zona Moriggia-Pelascini

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

RegistryNCT04858893, acronym CoMDA-ML-P
StatusCompleted
Study typeObservational
Enrollment562 (Actual)
SponsorOspedale Generale Di Zona Moriggia-Pelascini (Other); collaborators: Ospedale di Vipiteno-Sterzing (SABES-ASDAA)
ConditionsPrimary Parkinsonism, Secondary Vascular Parkinson Disease, Multiple System Atrophy, Supranuclear Palsy, Progressive
InterventionsCoMDA associated with Neural Net 91 classificator (Diagnostic test)
Datesstart 2017-01-01, primary completion 2020-02-01, completion 2020-08-31, first posted 2021-04-26, last updated 2021-05-12
Locations1 site in Italy

Related Papers Related Patents Related Grants Related Experts
All trials for Primary Parkinsonism All trials for Secondary Vascular Parkinson Disease All trials of CoMDA associated with Neural Net 91 classificator

-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.

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+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.

+Arms and interventions

ArmTypeInterventionsDescription
Subjects affected from ParkinsonimsDiagnostic Test: CoMDA associated with Neural Net 91 classificatorScores of MMSE, FAB MoCA were summarized to calculate the CoMDA scores, than they were used to develop the Neural Net 91 classificator
Health ControlsDiagnostic Test: CoMDA associated with Neural Net 91 classificatorCoMDA was administered and total score was calculate to develop the Neural Net 91 classificator
  • CoMDA associated with Neural Net 91 classificator (Diagnostic test)

+Outcome measures

Primary outcomes

  • 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

Sex: All, accepts healthy volunteers

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.

+Locations (1)

FacilityCityCountryStatus
"Moriggia Pelascini" HospitalGravedona E Uniti, ComoItaly

+Publications

  • Results publication: 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. PMID 35410449